Boole Microcap Fund

March 13, 2022

This week’s blog post coversThe Essays of Warren Buffett: Lessons for Corporate America (4th edition, 2015), selected and arranged byLawrence A. Cunningham. The book is based on 50 years of Buffett’s letters to shareholders, organized according to topic.

Not only is Warren Buffett arguably the greatest investor of all time; butBuffett wants to be remembered as a “Teacher.” Buffett and Munger have been outstanding “professors” for decades now, carrying on the value investing community’s tradition of generosity. Munger:

The best thing a human being can do is to help another human being know more.

Every section (but taxation) from The Essays of Warren Buffett is included here:

• • Prologue: Owner-Related Business Principles
  • Corporate Governance
  • Finance and Investing
  • Investment Alternatives
  • Common Stock
  • Mergers and Acquisitions
  • Valuation and Accounting
  • Accounting Shenanigans
  • Berkshire at Fifty and Beyond

 

PROLOGUE: OWNER-RELATED BUSINESS PRINCIPLES

Buffett writes that Berkshire Hathaway shareholders are unusual because nearly all of them focus on long-term compounding of business value. At the end of a typical year, 98% of those who own shares in Berkshire owned the shares at the beginning of the year.

Buffett remarks that, to a large extent, companies end up with the shareholders they seek and deserve. Buffett sets forth Berkshire’s fifteen owner-related business principles:

1. 1. Although our form is corporate, our attitude is partnership. Charlie Munger and I think of our shareholders as owner-partners, and of ourselves as managing partners… We do not view the company itself as the ultimate owner of our business assets but instead view the company as a conduit through which our shareholders own the assets.
   2. In line with Berkshire’s owner-orientation, most of our directors have a major portion of their net worth invested in the company. We eat our own cooking.
   3. Our long-term economic goal (subject to some qualifications mentioned later) is to maximize Berkshire’s average annual rate of gain in intrinsic business value on a per-share basis. We do not measure the economic significance or performance of Berkshire by its size; we measure by per-share progress…
   4. Our preference would be to reach our goal by directly owning a diversified group of businesses that generate cash and consistently earn above-average returns on capital. Our second choice is to own parts of similar businesses, attained primarily through purchases of marketable common stocks by our insurance subsidiaries…
   5. Because of our two-pronged approach to business ownership and because of the limitations of conventional accounting, consolidated reported earnings may reveal relatively little about our true economic performance. Charlie and I, both as owners and managers, virtually ignore such consolidated numbers. However, we will also report to you the earnings of each major business we control, numbers we consider of great importance. These figures, along with other information we will supply about the individual businesses, should generally aid you in making judgments about them.
   6. Accounting consequences do not influence our operating or capital-allocation decisions. When acquisition costs are similar, we much prefer to purchase $2 of earnings that is not reportable by us under standard accounting principles than to purchase $1 of earnings that is reportable. This is precisely the choice that often faces us since entire businesses (whose earnings will be fully reportable) frequently sell for double the pro-rate price of small portions (whose earnings will be largely unreportable). In aggregate and over time, we expect the unreported earnings to be fully reflected in our intrinsic business value through capital gains.
   7. We use debt sparingly and, when we do borrow, we attempt to structure our loans on a long-term fixed-rate basis. We will reject interesting opportunities rather than over-leverage our balance sheet. This conservatism has penalized our results but it is the only behavior that leaves us comfortable, considering our fiduciary obligations to policyholders, lenders and the many equity holders who have committed unusually large portions of their net worth to our care. (As one of the Indianapolis ‘500’ winners said: ‘To finish first, you must first finish.’)
   8. A managerial ‘wish list’ will not be filled at shareholder expense. We will not diversify by purchasing entire businesses at control prices that ignore long-term economic consequences to our shareholders. We will only do with your money what we would do with our own, weighing fully the values you can obtain by diversifying your own portfolios through direct purchases in the stock market.
   9. We feel noble intentions should be checked periodically against results. We test the wisdom of retained earnings by assessing whether retention, over time, delivers shareholders at least $1 of market value for each $1 retained. To date, this test has been met. We will continue to apply it on a five-year rolling basis…
   10. We will issue common stock only when we receive as much in business value as we give…
   11. You should be fully aware of one attitude Charlie and I share that hurts our financial performance: Regardless of price, we have no interest at all in selling any good businesses that Berkshire owns. We are also very reluctant to sell sub-par businesses as long as we expect them to generate at least some cash and as long as we feel good about their managers and labor relations…
   12. We will be candid in our reporting to you, emphasizing the pluses and minuses important in appraising business value. Our guideline is to tell you the business facts that we would want to know if our positions were reversed. We owe you no less… We also believe candor benefits us as managers: The CEO who misleads others in public may eventually mislead himself in private.
   13. Despite our policy of candor we will discuss our activities in marketable securities only to the extent legally required. Good investment ideas are rare, valuable and subject to competitive appropriation…
   14. To the extent possible, we would like each Berkshire shareholder to record a gain or loss in market value during his period of ownership that is proportional to the gain or loss in per-share intrinsic value recorded by the company during that holding period…
   15. We regularly compare the gain in Berkshire’s per-share book value to the performance of the S&P 500…

 

CORPORATE GOVERNANCE

Buffett explains:

At Berkshire, full reporting means giving you the information that we would wish you to give to us if our positions were reversed. What Charlie and I would want under the circumstance would be all the important facts about current operations as well as the CEO’s frank view of the long-term economic characteristics of the business. We would expect both a lot of financial details and a discussion of any significant data we would need to interpret what was presented.

Buffett comments that it is deceptive and dangerous – as he and Charlie see it – for CEOs to predict publicly growth rates for their companies. Though they are pushed to do so by analysts and their own investor relations departments, such predictions too often lead to trouble. Having internal targets is fine, of course. Buffett:

The problem arising from lofty predictions is not just that they spread unwarranted optimism. Even more troublesome is the fact that they corrode CEO behavior. Over the years, Charlie and I have observed many instances in which CEOs engaged in uneconomic operating maneuvers so that they could meet earnings targets they had announced. Worse still, after exhausting all that operating acrobatics would do, they sometimes played a wide variety of accounting games to ‘make the numbers.’

Buffett offers three suggestions for investors. He says:

• First, beware of companies displaying weak accounting… When managements take the low road in aspects that are visible, it is likely they are following a similar path behind the scenes.
• Second, unintelligible footnotes usually indicate untrustworthy management. If you can’t understand a footnote or other managerial explanation, it’s usually because the CEO doesn’t want you to…
• Finally, be suspicious of companies that trumpet earnings projections and growth expectations. Businesses seldom operate in a tranquil, no-surprise environment, and earnings simply don’t advance smoothly (except, of course, in the offering books of investment bankers).

Buffett writes that when CEOs fall short, it’s quite difficult to remove them. Part of the problem is that there are no objective standards.

At too many companies, the boss shoots the arrow of managerial performance and then hastily paints the bullseye around the spot where it lands.

A further problem is that the CEO has no immediate superior whose performance is itself being measured. Buffett describes this and related issues:

But the CEO’s boss is a board of directors that seldom measures itself and is infrequently held to account for substandard corporate performance. If the Board makes a mistake in hiring, and perpetuates that mistake, so what? Even if the company is taken over because of the mistake, the deal will probably bestow substantial benefits on the outgoing board members…

Finally, relations between the Board and the CEO are expected to be congenial. At board meetings, criticisms of the CEO’s performance is often viewed as the social equivalent of belching…

These points should not be interpreted as a blanket condemnation of CEOs or Boards of Directors: Most are able and hardworking, and a number are truly outstanding. But the management failings Charlie and I have seen make us thankful that we are linked with the managers of our permanent holdings. They love their businesses, they think like owners, and they exude integrity and ability.

Buffett wrote more about corporate governance on a different occasion. He points out that there are three basic manager/owner situations.

The first situation – by far the most common – is that there is no controlling shareholder. Buffett argues that directors in this case should act as if there is a single absentee owner, whose long-term interest they should try to further. If a board member sees management going wrong, he should try to convince other board members. Failing that, he should make his views known to absentee owners, says Buffett. Also, the board should set standards for CEO performance and regularly meet – without the CEO present – to measure that performance. Finally, board members should be chosen based on business savvy, interest in the job, and owner-orientation, holds Buffett.

The second situation is that the controlling owner is also the manager. In this case, if the owner/manager is failing, it’s difficult for board members to improve things. If the board members agree, they could as a unit convey their concerns. But this probably won’t achieve much. On an individual level, a board member who has serious concerns could resign.

The third governance situation is when there is a controlling owner who is not involved in management. In this case, unhappy directors can go directly to the owner, observes Buffett.

Buffett then remarks:

Logically, the third case should be the most effective in insuring first-class management. In the second case the owner is not going to fire himself, and in the first case, directors often find it very difficult to deal with mediocrity or mild over-reaching. Unless the unhappy directors can win over a majority of the board – an awkward social and logistical task, particularly if management’s behavior is merely odious, not egregious – their hands are effectively tied…

Buffett also writes that most directors are decent folks who do a first-class job. But, nonetheless, being human, some directors will fail to be objective if their director fees are a large part of their annual income.

Buffett says that Berkshire’s policy is only to work with people they like and admire. Berkshire generally only buys a business when they like and admire the manager and when that manager is willing to stay in place.

…Berkshire’s ownership may make even the best of managers more effective. First, we eliminate all of the ritualistic and nonproductive activities that normally go with the job of CEO. Our managers are totally in charge of their personal schedules. Second, we give each a simple mission: Just run your business as if:

• • you own 100% of it;
  • it is the only asset in the world that you and your family have or will ever have; and
  • you can’t sell or merge it for at least a century.

As a corollary, we tell them they should not let any of their decisions be affected even slightly by accounting considerations. We want our managers to think about what counts, not how it will be counted.

Buffett comments that very few CEOs of public companies can follow such mandates, chiefly because they have owners (shareholders) who focus on short-term prospects and reported earnings. It’s not that Berkshire ignores current results, says Buffett, but that they should never be achieved at the expense of building ever-greater long-term competitive strengths.

I believe the GEICO story demonstrates the benefits of Berkshire’s approach. Charlie and I haven’t taught Tony a thing – and never will – but we have created an environment that allows him to apply all of his talents to what’s important. He does not have to devote his time or energy to board meetings, press interviews, presentations by investment bankers or talks with financial analysts. Furthermore, he need never spend a minute thinking about financing, credit ratings or ‘Street’ expectations for earnings per share. Because of our ownership structure, he also knows that this operational framework will endure for decades to come. In this environment of freedom, both Tony and his company can convert their almost limitless potential into matching achievements.

Buffett discusses the importance of building long-term competitive strengths:

Every day, in countless ways, the competitive position of each of our businesses grows either weaker or stronger. If we are delighting customers, eliminating unnecessary costs and improving our products and services, we gain strength. But if we treat customers with indifference or tolerate bloat, our businesses will wither. On a daily basis, the effects of our actions are imperceptible; cumulatively, though, their consequences are enormous.

When our long-term competitive position improves as a result of these almost unnoticeable actions, we describe the phenomenon as ‘widening the moat.’ And doing that is essential if we are to have the kind of business we want a decade or two from now. We always, of course, hope to earn more money in the short-term. But when short-term and long-term conflict, widening the moat must take precedence.

It’s interesting that Berkshire Hathaway itself, a textile operation, is one of Buffett’s biggest investment mistakes. Furthermore, Buffett owned the textile business from 1965 to 1985, despite generally bad results. Buffett explains that he held on to this business because management was straightforward and energetic, labor was cooperative and understanding, the company was a large employer, and the business was still earning modest cash returns.

Buffett was able to build today’s Berkshire Hathaway, one of the largest and most successful companies in the world, because he took cash out of the textile operation and reinvested in a series of highly successful businesses. Buffett did have to close the textile business in 1985 – twenty years after acquiring it – because, by then, the company was losing money each year, with no prospect for improvement.

Buffett tells the story of Burlington, the largest U.S. textile enterprise. From 1964 to 1985, Burlington spent about $3 billion on improvement and expansion. This amounted to more than $200-a-share on a $60 stock. However, after 20 years, the stock had gone nowhere, while the CPI had more than tripled. Buffett:

This devastating outcome for the shareholders demonstrates what can happen when much brain power and energy are applied to a faulty premise…

My conclusion from my own experiences and from much observation of other businesses is that a good managerial record (measured by economic returns) is far more a function of what business boat you get into than it is of how effectively you row (though intelligence and effort help considerably, of course, in any business, good or bad). Should you find yourself in a chronically-leaking boat, energy devoted to changing vessels is likely to be more productive than energy devoted to patching leaks.

Buffett also covers the topic of executive pay:

When returns on capital are ordinary, an earn-more-by-putting-up-more record is no great managerial achievement. You can get the same result personally by operating from your rocking chair. Just quadruple the capital you commit to a savings account and you will quadruple your earnings. You would hardly expect hosannas for that particular accomplishment. Yet, retirement announcements regularly sing the praises of CEOs who have, say, quadrupled earnings of their widget company during their reign – with no one examining whether this gain was attributable simply to many years of retained earnings and the workings of compound interest.

If the widget company consistently earned a superior return on capital throughout the period, or if capital employed only doubled during the CEO’s reign, the praise for him may be well deserved. But if return on capital was lackluster and capital employed increased in pace with earnings, applause should be withheld. A savings account in which interest was reinvested would achieve the same year-by-year increase in earnings – and, at only 8% interest, would quadruple its annual earnings in 18 years.

The power of this simple math is often ignored by companies to the detriment of their shareholders. Many corporate compensation plans reward managers handsomely for earnings increases produced solely, or in large part, by retained earnings – i.e., earnings withheld from owners…

Buffett points out that ten-year, fixed-price options ignore the fact that earnings automatically build value, and that carrying capital has a cost. Managers in this situation profit just as they would if they had an option on the savings account that automatically was building value.

Buffett repeatedly emphasizes that excellent management performance should be rewarded. Indeed, says Buffett, exceptional managers nearly always get less than they should. But that means you have to measure return on capital versus cost of capital. Buffett does admit, however, that some managers he admires enormously disagree with him regarding fixed-price options.

Buffett designs Berkshire’s employment contracts with managers based on returns on capital employed versus the cost of that capital. If the return on capital is high, the manager is rewarded. If return on capital is sub-standard, then the manager is penalized. Fixed-price options, by contrast, besides not usually being adjustable for the cost of capital, also fall short in that they reward managers on the upside without penalizing them on the downside. (Buffett does adjust manager contracts based on the economic characteristics of the business, however. A regulated business will have lower but still acceptable returns, for instance.)

Regarding reputation, Buffett has written for over 30 years:

We can’t be perfect but we can try to be…

We can afford to lose money – even a lot of money. But we can’t afford to lose reputation – even a shred of reputation.

Most auditors, observes Buffett, see that the CEO and CFO pay their fees. So the auditors are more worried about offending the CEO than they are about accurate reporting. Buffett suggests that audit committees ask the following four questions of auditors:

1. If the auditor were solely responsible for the preparation of the company’s financial statements, would they in any way have been prepared differently from the manner selected by management? This question should cover both material and nonmaterial differences. If the auditor would have done something differently, both management’s argument and the auditor’s response should be disclosed. The audit committee should then evaluate the facts.
2. If the auditor were an investor, would he have received – in plain English – the information essential to his understanding the company’s financial performance during the reporting period?
3. Is the company following the same internal audit procedure that would be followed if the auditor himself were CEO? If not, what are the differences and why?
4. Is the auditor aware of any actions – either accounting or operational – that have had the purpose and effect of moving revenues or expenses from one reporting period to another?

Buffett remarks that this procedure would save time and expense, in addition to focusing auditors on their duty.

 

FINANCE AND INVESTING

Buffett discusses his purchase of a farm in Nebraska in 1986, a few years after a bubble in Midwest farm prices had popped. First, he learned from his son how many bushels of corn and of soybeans would be produced, and what the operating expenses would be. Buffett determined that the normalized return from the farm would be 10%, and that productivity and prices were both likely to increase over time. Three decades later, the farm had tripled its earnings and Buffett’s investment had grown five times in value.

Buffett also mentions buying some real estate next to NYU shortly after a bubble in commercial real estate had popped. The unlevered current yield was 10%. Earnings subsequently tripled and annual distributions soon exceeded 35% of the original equity investment.

Buffett says these two investments illustrate certain fundamentals of investing, which he spells out as follows:

• You don’t need to be an expert in order to achieve satisfactory investment returns. But if you aren’t, you must recognize your limitations and follow a course certain to work reasonably well. Keep things simple and don’t swing for the fences. When promised quick profits, respond with a quick ‘no.’
• Focus on the future productivity of the asset you are considering. If you don’t feel comfortable making a rough estimate of the asset’s future earnings, just forget it and move on. No one has the ability to evaluate every investment possibility. But omniscience isn’t necessary; you only need to understand the actions you undertake.
• If you instead focus on the prospective price change of a contemplated purchase, you are speculating. There is nothing improper about that. I know, however, that I am unable to speculate successfully, and I am skeptical of those who claim sustained success at doing so… And the fact that a given asset has appreciated in the recent past is never a reason to buy it.
• With my two small investments, I thought only of what the property would produce and cared not at all about their daily valuations. Games are won by players who focus on the playing field – not by those whose eyes are glued to the scoreboard. If you can enjoy Saturdays and Sundays without looking at stock prices, give it a try on weekdays.
• Forming macro opinions or listening to the macro or market opinions of others is a waste of time. Indeed, it is dangerous because it may blur your vision of the facts that are truly important…
• My two purchases were made in 1986 and 1993. What the economy, interest rates, or the stock market might do in the years immediately following – 1987 and 1994 – was of no importance to me in making those investments. I can’t remember what the headlines or pundits were saying at the time. Whatever the chatter, corn would keep growing in Nebraska and students would flock to NYU.

Many long-term investors make the mistake of feeling good when stock prices rise. Buffett says that if you’re going to be a long-term investor and regularly add to your investments, you should prefer stock prices to fall rather than rise. Eventually, stock prices follow business results. And it’s safe to assume the U.S. economy will continue to grow over the long term. But between now and then, if you’re a net buyer of stocks, you’re better off if stock prices fall before they rise. Buffett:

So smile when you read a headline that says ‘Investors lose as market falls.’ Edit it in your mind to ‘Disinvestors lose as market falls – but investors gain.’

Buffett advises most investors to invest in index funds: https://boolefund.com/warren-buffett-jack-bogle/

But for a handful of investors who can understand some businesses, it’s better to patiently wait for the fattest pitches. Buffett gives an analogy:

If my universe of business opportunities was limited, say, to private companies in Omaha, I would, first, try to assess the long-term economic characteristics of each business; second, assess the quality of the people in charge of running it; and, third, try to buy into a few of the best operations at a sensible price. I certainly would not wish to own an equal part of every business in town. Why, then, should Berkshire take a different tack when dealing with the larger universe of public companies? And since finding great businesses and outstanding managers is so difficult, why should we discard proven products?

Buffett then quotes the economist and investor John Maynard Keynes:

‘As time goes on, I get more and more convinced that the right method in investment is to put fairly large sums into enterprises which one thinks one knows something about and in the management of which one thoroughly believes. It is a mistake to think that one limits one’s risk by spreading too much between enterprises about which one knows little and has no reason for special confidence. One’s knowledge and experience are definitely limited and there are seldom more than two or three enterprises at any given time in which I personally feel myself entitled to put full confidence.’ – J. M. Keynes

Here are details on Keynes as an investor: https://boolefund.com/greatest-economist-defied-convention-got-rich/

Buffett explains Berkshire’s equity investment strategy by quoting its 1977 annual report:

We select our marketable equity securities in much the way we would evaluate a business for acquisition in its entirety. We want the business to be one (a) that we can understand; (b) with favorable long-term prospects; (c) operated by honest and competent people; and (d) available at a very attractive price.

Buffett then notes that, due to Berkshire’s much larger size as well as market conditions, they would now substitute ‘an attractive price’ for ‘a very attractive price.’ How do you decide what’s ‘attractive’? Buffett quotesThe Theory of Investment Value, by John Burr Williams:

‘The value of any stock, bond or business today is determined by the cash inflows and outflows – discounted at an appropriate interest rate – that can be expected to occur during the remaining life of the asset.’

Buffett comments:

The investment shown by the discounted-flows-of-cash calculation to be the cheapest is the one that the investor should purchase – irrespective of whether the business grows or doesn’t, displays volatility or smoothness in its earnings, or carries a high price or low in relation to its current earnings and book value…

Leaving the question of price aside, the best business to own is one that over an extended period can employ large amounts of incremental capital at very high rates of return. The worst business to own is one that must, or will, do the opposite – that is, consistently employ ever-greater amounts of capital at very low rates of return. Unfortunately, the first type of business is very hard to find…

Though the mathematical calculations required to evaluate equities are not difficult, an analyst – even one who is experienced and intelligent – can easily go wrong in estimating future ‘coupons.’ At Berkshire, we attempt to deal with this problem in two ways. First, we try to stick to businesses we believe we understand. That means they must be relatively simple and stable in character. If a business is complex or subject to constant change, we’re not smart enough to predict future cash flows. Incidentally, that shortcoming doesn’t bother us. What counts for most people in investing is not how much they know, but rather how realistically they define what they don’t know. An investor needs to do very few things right as long as he or she avoids big mistakes.

Second, and equally important, we insist on a margin of safety in our purchase price. If we calculate the value of a common stock to be only slightly higher than its price, we’re not interested in buying. We believe this margin-of-safety principle, so strongly emphasized by Ben Graham, to be the cornerstone of investment success.

At another point, Buffett explains concentrated, buy-and-hold investing:

Inactivity strikes us as intelligent behavior. Neither we nor most business managers would dream of feverishly trading highly profitable subsidiaries because a small move in the Federal Reserve’s discount rate was predicted or because some Wall Street pundit had reversed his views on the market. Why, then, should we behave differently with our minority positions in wonderful businesses? The art of investing in public companies successfully is little different from the art ofsuccessfully acquiring subsidiaries. In each case you simply want to acquire, at a sensible price, a business with excellent economics and able, honest management. Thereafter, you need only monitor whether these qualities are being preserved.

When carried out capably, an investment strategy of that type will often result in its practitioner owning a few securities that will come to represent a very large portion of his portfolio. This investor would get a similar result if he followed a policy of purchasing an interest in, say, 20% of the future earnings of a number of outstanding college basketball stars. A handful of these would go on to achieve NBA stardom, and the investor’s take from them would soon dominate his royalty stream. To suggest that this investor should sell off portions of his most successful investments simply because they have come to dominate this portfolio is akin to suggesting that the Bulls trade Michael Jordan because he has become so important to the team.

Buffett reiterates that he and Charlie, when buying subsidiaries or common stocks, focus on businesses and industries unlikely to change much over time:

…The reason for that is simple: Making either type of purchase, we are searching for operations that we believe are virtually certain to possess enormous competitive strength ten or twenty years from now. A fast-changing industry environment may offer the chance for huge wins, but it precludes the certainty we seek.

I should emphasize that, as citizens, Charlie and I welcome change: Fresh ideas, new products, innovative processes and the like cause our country’s standard of living to rise, and that’s clearly good. As investors, however, our reaction to a fermenting industry is much like our attitude toward space exploration: We applaud the endeavor but prefer to skip the ride.

Obviously all businesses change to some extent. Today, See’s is different in many ways from what it was in 1972 when we bought it: It offers a different assortment of candy, employs different machinery and sells through different distribution channels. But the reasons why people today buy boxed chocolates, and why they buy them from us rather than from someone else, are virtually unchanged from what they were in the 1920s when the See family was building the business. Moreover, these motivations are not likely to change over the next 20 years, or even 50.

Buffett goes on to discuss Coca-Cola and Gillette, labeling companies like Coca-Cola ‘The Inevitables.’ Buffett points out that he’s not downplaying the important work these companies must continue to do in order to maximize their results over time. He’s merely saying that all sensible observers agree that Coke will dominate worldwide over an investment lifetime. This degree of brand strength – reflected in sustainably high returns on capital – is very rare. Buffett:

Obviously many companies in high-tech businesses or embryonic industries will grow much faster in percentage terms than will The Inevitables. But I would rather be certain of a good result than hopeful of a great one.

The main danger for a great company is getting sidetracked from its wonderful core business while acquiring other businesses that are mediocre or worse.

Unfortunately, that is exactly what transpired years ago at Coke. (Would you believe that a few decades back they were growing shrimp at Coke?) Loss of focus is what most worries Charlie and me when we contemplate investing in businesses that in general look outstanding. All too often, we’ve seen value stagnate in the presence of hubris or of boredom that caused the attention of managers to wander.

***

Buffett (again) recommends index funds for most investors:

Most investors, both individual and institutional, will find that the best way to own common stocks is through an index fund that charges minimal fees. Those following this path are sure to beat the net results (after fees and expenses) delivered by the great majority of investment professionals.

For those investors seeking to pick individual stocks, the notion ofcircle of competence is crucial. Buffett and Munger are well aware of which companies they can evaluate and which they can’t. Buffett:

If we have a strength, it is in recognizing when we are operating well within our circle of competence and when we are approaching the perimeter. Predicting the long-term economics of companies that operate in fast-changing industries is simply far beyond our perimeter. If others claim predictive skill in those industries – and seem to have their claims validated by the behavior of the stock market – we neither envy nor emulate them. Instead, we just stick with what we understand.

Mistakes of the First 25 Years

Buffett first notes that the lessons of experience are not always helpful. But it’s still good to review past mistakes ‘before committing new ones.’ To that end, Buffett lists mistakes of the twenty-five years up until 1989:

** My first mistake, of course, was in buying control of Berkshire. Though I knew its business – textile manufacturing – to be unpromising, I was enticed to buy because the price looked cheap. Stock purchases of that kind had proved reasonably rewarding in my early years, though by the time Berkshire came along in 1965 I was becoming aware that the strategy was not ideal.

If you buy a stock at a sufficiently low price, there will usually be some hiccup in the fortunes of the business that gives you a chance to unload at a decent profit, even though the long-term performance of the business may be terrible. I call this the ‘cigar butt’ approach to investing. A cigar butt found on the street that has only one puff left in it may not offer much of a smoke, but the ‘bargain purchase’ will make that puff all profit.

Unless you are a liquidator, that kind of approach to buying businesses is foolish. First, the original ‘bargain’ price probably will not turn out to be such a steal after all. In a difficult business, no sooner is one problem solved than another surfaces – never is there just one cockroach in the kitchen. Second, any initial advantage you secure will be quickly eroded by the low return that the business earns…

** That leads right into a related lesson: Good jockeys will do well on good horses, but not on broken-down nags…

I’ve said many times that when a management with a reputation for brilliance tackles a business with a reputation for bad economics, it is the reputation of the business that remains intact…

**A further related lesson: Easy does it. After 25 years of buying and supervising a great variety of businesses, Charlie and I have not learned how to solve difficult business problems. What we have learned is to avoid them. To the extent we have been successful, it is because we concentrated on identifying one-foot hurdles that we could step over rather than because we acquired any ability to clear seven-footers.

The finding may seem unfair, but in both business and investments it is usually far more profitable to simply stick with the easy and obvious than it is to resolve the difficult. On occasion, tough problemsmust be tackled. In other instances, a great investment opportunity occurs when a marvelous business encounters a one-time huge, but solvable, problem as was the case many years back at both American Express and GEICO…

**My most surprising discovery: the overwhelming importance in business of an unseen force that we might call ‘the institutional imperative.’ In business school, I was given no hint of the imperative’s existence and I did not intuitively understand it when I entered the business world. I thought then that decent, intelligent, and experienced managers would automatically make rational business decisions. But I learned over time that isn’t so. Instead, rationality frequently wilts when the institutional imperative comes into play.

For example: (1) As if governed by Newton’s First Law of Motion, an institution will resist any change in its current direction; (2) Just as work expands to fill available time, corporate projects or acquisitions will materialize to soak up available funds; (3) Any business craving of the leader, however foolish, will be quickly supported by detailed rate-of-return and strategic studies prepared by his troops; and (4) The behavior of peer companies, whether they are expanding, acquiring, setting executive compensation or whatever, will be mindlessly imitated.

** After some mistakes, I learned to go into business only with people I like, trust, and admire… We’ve never succeeded in making a good deal with a bad person.

**Some of my worst mistakes were not publicly visible. These were stock and business purchases whose virtues I understood and yet didn’t make… For Berkshire’s shareholders, myself included, the cost of this thumb-sucking has been huge.

**Our consistently-conservative financial policies may appear to have been a mistake, but in my view were not. In retrospect, it is clear that significantly higher, though still conventional, leverage ratios at Berkshire would have produced considerably better returns on equity than the 23.8% we have actually averaged. Even in 1965, perhaps we could have judged there to be a 99% probability that higher leverage would lead to nothing but good. Correspondingly, we might have seen only a 1% chance that some shock factor, external or internal, would cause a conventional debt ratio to produce a result falling somewhere between temporary anguish and default.

We wouldn’t have liked those 99:1 odds – and never will. A small chance of distress or disgrace cannot, in our view, be offset by a large chance of extra returns. If your actions are sensible, you are certain to get good results; in most such cases, leverage just moves things along faster. Charlie and I have never been in a big hurry: We enjoy the process far more than the proceeds – though we have learned to live with those also.

 

INVESTMENT ALTERNATIVES

Buffett in 2011:

Investment possibilities are both many and varied. There are three major categories, however, and it’s important to understand the characteristics of each. So let’s survey the field.

Investments that are denominated in a given currency include money-market funds, bonds, mortgages, bank deposits, and other instruments. Most of these currency-based investments are thought of as ‘safe.’ In truth they are among the most dangerous of assets. Their beta may be zero but their risk is huge.

Over the past century these instruments have destroyed the purchasing power of investors in many countries, even as the holders continued to receive timely payments of interest and principal. This ugly result, moreover, will forever recur. Governments determine the ultimate value of money, and systemic forces will sometimes cause them to gravitate to policies that produce inflation. From time to time such policies spin out of control.

Even in the U.S., where the wish for a stable currency is strong, the dollar has fallen a staggering 86% in value since 1965, when I took over management of Berkshire. It takes no less than $7 today to buy what $1 did at that time. Consequently, a tax-free institution would have needed 4.3% interest annually from bond investments over that period to simply maintain its purchasing power. Its managers would have been kidding themselves if they thought of any portion of that interest as income.

Buffett then notes that it’s even worse for tax-paying investors, who would have needed 5.7% annually to hold their ground. In other words, an invisible ‘inflation tax’ has consumed 4.3% per year. Given that interest rates today (mid-2017) are very low, currency-based investments are not attractive for the long term (decades).

The second major category of investments involves assets that will never produce anything, but that are purchased in the hope that someone else – who also knows that the assets will be forever unproductive – will pay more for them in the future. Tulips, of all things, briefly became a favorite of such buyers in the 17th century.

This type of investment requires an expanding pool of buyers, who, in turn, are enticed because they believe the buying pool will expand still further. Owners are not inspired by what the asset itself can produce – it will remain lifeless forever – but rather by the belief that others will desire it even more avidly in the future.

The major asset in this category is gold, currently a huge favorite of investors who fear almost all other assets, especially paper money (of whose value, as noted, they are right to be fearful). Gold, however, has two significant shortcomings, being neither of much use nor procreative. True, gold has some industrial and decorative utility, but the demand for these purposes is both limited and incapable of soaking up new production. Meanwhile, if you own one ounce of gold for an eternity, you will still own one ounce of gold at its end.

…

Today, the world’s gold stock is about 170,000 metric tons. If all of this gold were melded together, it would form a cube of about 68 feet per side. (Picture it sitting comfortably within a baseball infield.) At $1,750 per ounce – gold’s price as I write this – its value would be $9.6 trillion. Call this cube pile A.

Let’s now create a pile B costing an equal amount. For that, we could buy all U.S. cropland (400 million acres with output of about $200 billion annually), plus sixteen Exxon Mobiles (the world’s most profitable company, one earning more than $40 billion annually). After these purchases, we would have about $1 trillion left over for walking-around money (no sense feeling strapped after this buying binge). Can you imagine an investor with $9.6 trillion selecting pile A over pile B?

…

A century from now the 400 million acres of farmland will have produced staggering amounts of corn, wheat, cotton, and other crops – and will continue to produce that valuable bounty, whatever the currency may be. Exxon Mobile will probably have delivered trillions of dollars in dividends to its owners and will also hold assets worth many more trillions (and, remember, you get 16 Exxons). The 170,000 tons of gold will be unchanged in size and still incapable of producing anything. You can fondle the cube, but it will not respond.

Admittedly, when people a century from now are fearful, it’s likely many will still rush to gold. I’m confident, however, that the $9.6 trillion current valuation of pile A will compound over the century at a rate far inferior to that achieved by pile B.

Our first two categories enjoy maximum popularity at peaks of fear: Terror over economic collapse drives individuals to currency-based assets, most particularly U.S. obligations, and fear of currency collapse drives investors to sterile assets such as gold. We heard ‘cash is king’ in late 2008, just when cash should have been deployed rather than held…

My own preference – and you knew this was coming – is our third category: investment in productive assets, whether businesses, farms, or real estate. Ideally, these assets should have the ability in inflationary times to deliver output that will retain its purchasing-power value while requiring a minimum of new capital investment. Farms, real estate, and many businesses such as Coca-Cola, IBM, and our own See’s Candy meet that double-barreled test. Certain other companies – think of our regulated utilities for example – fail it because inflation places heavy capital requirements on them. To earn more, their owners must invest more. Even so, these investments will remain superior to nonproductive or currency-based assets.

Whether the currency a century from now is based on gold, seashells, shark teeth, or a piece of paper (as today), people will be willing to exchange a couple of minutes of their daily labor for a Coca-Cola or some See’s peanut brittle. In the future the U.S. population will move more goods, consume more food, and require more living space than it does now. People will forever exchange what they produce for what others produce.

Our country’s businesses will continue to efficiently deliver goods and services wanted by our citizens… I believe that over any extended period of time this category of investing will prove to be the runaway winner among the three we’ve examined. More important, it will be by far the safest.

***

Pessimism creates low prices. But you cannot be a contrarian blindly:

The most common cause of low prices is pessimism – sometimes pervasive, sometimes specific to a company or industry. We want to do business in such an environment, not because we like pessimism but because we like the prices it produces. It’s optimism that is the enemy of the rational buyer.

None of this means, however, that a business or stock is an intelligent purchase simply because it is unpopular; a contrarian approach is just as foolish as a follow-the-crowd strategy. What’s required is thinking rather than polling. Unfortunately, Bertrand Russell’s observation about life in general applies with unusual force in the financial world: ‘Most men would rather die than think. Many do.’

 

COMMON STOCK

Transaction costs eat up an astonishing degree of corporate earnings every year. Buffett writes at length – in the 2005 letter – about how this works:

The explanation of how this is happening begins with a fundamental truth: …the most that owners in aggregate can earn between now and Judgment Day is what their businesses in aggregate earn. True, by buying and selling that is clever or lucky, investor A may take more than his share of the pie at the expense of investor B. And, yes, all investors feel richer when stocks soar. But an owner can exit only by having someone take his place. If one investor sells high, another must buy high. For owners as a whole, there is simply no magic – no shower of money from outer space – that will enable them to extract wealth from their companies beyond that created by the companies themselves.

Indeed, owners must earn less than their businesses earn because of ‘frictional’ costs. And that’s my point: These costs are now being incurred in amounts that will cause shareholders to earn far less than they historically have.

To understand how this toll has ballooned, imagine for a moment that all American corporations are, and always will be, owned by a single family. We’ll call them the Gotrocks. After paying taxes on dividends, this family – generation after generation – becomes richer by the aggregate amount earned by its companies. Today that amount is about $700 billion annually. Naturally, the family spends some of these dollars. But the portion it saves steadily compounds for its benefit. In the Gotrocks household everyone grows wealthier at the same pace, and all is harmonious.

But let’s now assume that a few fast-talking Helpers approach the family and persuade each of its members to try to outsmart his relatives by buying certain of their holdings and selling them certain others. The Helpers – for a fee, of course – obligingly agree to handle these transactions. The Gotrocks still own all of corporate America; the trades just rearrange who owns what. So the family’s annual gain in wealth diminishes, equaling the earnings of American business minus commissions paid. The more that family members trade, the smaller their share of the pie and the larger the slice received by the Helpers. This fact is not lost upon these broker-Helpers: Activity is their friend and, in a wide variety of ways, they urge it on.

After a while, most of the family members realize that they are not doing so well at this new ‘beat-my-brother’ game. Enter another set of Helpers. These newcomers explain to each member of the Gotrocks clan that by himself he’ll never outsmart the rest of the family. The suggested cure: ‘Hire a manager – yes, us – and get the job done professionally.’ These manager-Helpers continue to use the broker-Helpers to execute trades; the managers may even increase their activity so as to permit the brokers to prosper still more. Overall, a bigger slice of the pie now goes to the two classes of Helpers.

The family’s disappointment grows. Each of its members is now employing professionals. Yet overall, the group’s finances have taken a turn for the worse. The solution? More help, of course.

It arrives in the form of financial planners and institutional consultants, who weigh in to advise the Gotrocks on selecting manager-Helpers. The befuddled family welcomes this assistance. By now its members know they can pick neither the right stocks nor the right stock-pickers. Why, one might ask, should they expect success in picking the right consultant? But this question does not occur to the Gotrocks, and the consultant-Helpers certainly don’t suggest it to them.

The Gotrocks, now supporting three classes of expensive Helpers, find that their results get worse, and they sink into despair. But just as hope seems lost, a fourth group – we’ll call them the hyper-Helpers – appears. These friendly folk explain to the Gotrocks that their unsatisfactory results are occurring because the existing Helpers – brokers, managers, consultants – are not sufficiently motivated and are simply going through the motions…

The new arrivals offer a breathtakingly simple solution: Pay more money. Brimming with self-confidence, the hyper-Helpers assert that huge contingent payments – in addition to stiff fixed fees – are what each family member must fork over in order to really outmaneuver his relatives.

The more observant members of the family see that some of the hyper-Helpers are really just manager-Helpers wearing new uniforms, bearing sewn-on sexy names like HEDGE FUND or PRIVATE EQUITY. The new Helpers, however, assure the Gotrocks that this change of clothing is all-important… Calmed by this explanation, the family decides to pay up.

And that’s where we are today: A record portion of the earnings that would go in their entirety to owners – if they all just stayed in their rocking chairs – is now going to a swelling army of Helpers. Particularly expensive is the recent pandemic of profit arrangements under which Helpers receive large portions of the winnings when they are smart or lucky, and leave family members with all of the losses – and large fixed fees to boot – when the Helpers are dumb or unlucky (or occasionally crooked).

A sufficient number of the arrangements like this – heads, the Helper takes much of the winnings; tails, the Gotrocks lose and pay dearly for the privilege of doing so – may make it more accurate to call the family the Hadrocks. Today, in fact, the family’s frictional costs of all sorts may well amount to 20% of the earnings of American business. In other words, the burden of paying Helpers may cause American equity investors, overall, to earn only 80% or so of what they would earn if they just sat still and listened to no one.

Long ago, Sir Isaac Newton gave us three laws of motion, which were the work of genius. But Sir Isaac’s talents didn’t extend to investing: He lost a bundle in the South Sea Bubble explaining later, ‘I can calculate the movement of the stars, but not the madness of men.’ If he had not been traumatized by this loss, Sir Isaac might well have gone on to discover the Fourth Law of Motion: For investors as a whole, returns decrease as motion increases.

For more details, see: http://berkshirehathaway.com/letters/2005ltr.pdf

 

MERGERS AND ACQUISITIONS

The Oracle of Omaha says:

Of all our activities at Berkshire, the most exhilarating for Charlie and me is the acquisition of a business with excellent economic characteristics and a management that we like, trust, and admire. Such acquisitions are not easy to make, but we look for them constantly…

In the past, I’ve observed that many acquisition-hungry managers were apparently mesmerized by their childhood reading of the story about the frog-kissing princess. Remembering her success, they pay dearly for the right to kiss corporate toads, expecting wondrous transfigurations. Initially, disappointing results only deepen their desire to round up new toads… Ultimately, even the most optimistic manager must face reality. Standing knee-deep in unresponsive toads, he then announces an enormous ‘restructuring’ charge. In this corporate equivalent of a Head Start program, the CEO receives the education but the stockholders pay the tuition.

Not only do most acquisitions fail to create value for the acquirer; many actually destroy value. However, a few do create value. Buffett writes:

…many managerial princesses remain serenely confident about the future potency of their kisses – even after their corporate backyards are knee-deep in unresponsive toads. In fairness, we should acknowledge that some acquisition records have been dazzling. Two major categories stand out.

The first involves companies that, through design or accident, have purchased only businesses that are particularly well adapted to an inflationary environment. Such favored business must have two characteristics: (1) an ability to increase prices rather easily (even when product demand is flat and capacity is not fully utilized) without fear of significant loss of either market share or unit volume, and (2) an ability to accommodate large dollar volume increases in business (often produced more by inflation than by real growth) with only minor additional investment of capital. Managers of ordinary ability, focusing only on acquisition possibilities meeting these tests, have achieved excellent results in recent decades. However, very few enterprises possess both characteristics, and competition for those that do has now become fierce to the point of being self-defeating.

The second category involves the managerial superstars – who can recognize the rare prince who is disguised as a toad, and who have managerial abilities that enable them to peel away the disguise.

Capital allocation decisions, including value-destroying acquisitions, add up over the long term. Buffett:

Over time, the skill with which a company’s managers allocate capital has an enormous impact on the enterprise’s value. Almost by definition, a really good business generates far more money (at least after its early years) than it can use internally. The company could, of course, distribute the money to shareholders by way of dividends or share repurchases. But often the CEO asks a strategic planning staff, consultants or investment bankers whether an acquisition or two might make sense. That’s like asking your interior decorator whether you need a $50,000 rug.

The acquisition problem is often compounded by a biological bias: Many CEOs obtain their positions in part because they possess an abundance of animal spirits and ego. If an executive is heavily endowed with these qualities – which, it should be acknowledged, sometimes have their advantages – they won’t disappear when he reaches the top…

…

At Berkshire, our managers will continue to earn extraordinary returns from what appear to be ordinary businesses. As a first step, these managers will look for ways to deploy their earnings advantageously in their businesses. What’s left, they will send to Charlie and me. We then will try to use those funds in ways that build per-share intrinsic value. Our goal will be to acquire either part or all of businesses that we believe we understand, that have good, sustainable underlying economics, and that are run by managers whom we like, admire and trust.

Over the years, Berkshire Hathaway has become the buyer of choice for many private business owners. Buffett remarks:

Our long-avowed goal is to be the ‘buyer of choice’ for businesses – particularly those built and owned by families. The way to achieve this goal is to deserve it. That means we must keep our promises; avoid leveraging up acquired businesses; grant unusual autonomy to our managers; and hold the purchased companies through thick and thin (though we prefer thick and thicker).

Our record matches our rhetoric. Most buyers competing against us, however, follow a different path. For them, acquisitions are ‘merchandise.’ Before the ink dries on their purchase contracts, these operators are contemplating ‘exit strategies.’ We have a decided advantage, therefore, when we encounter sellers who truly care about the future of their businesses.

 

VALUATION AND ACCOUNTING

Buffett writes about Aesop and the Inefficient Bush Theory:

The formula for valuing all assets that are purchased for financial gain has been unchanged since it was first laid out by a very smart man in about 600 B.C. (though he wasn’t smart enough to know it was 600 B.C.).

The oracle was Aesop, and his enduring, though somewhat incomplete, investment insight was ‘a bird in the hand is worth two in the bush.’ To flesh out this principle, you must answer only three questions. How certain are you that there are indeed birds in the bush? When will they emerge and how many will there be? What is the risk-free interest rate (which we consider to be the yield on long-term U.S. bonds)? If you can answer these three questions, you will know the maximum value of the bush – and the maximum number of the birds you now possess that should be offered for it. And, of course, don’t literally think birds. Think dollars.

Aesop’s investment axiom, thus expanded and converted into dollars, is immutable. It applies to outlays for farms, oil royalties, bonds, stocks, lottery tickets, and manufacturing plants. And neither the advent of the steam engine, the harnessing of electricity nor the creation of the automobile changed the formula one iota – nor will the Internet. Just insert the correct numbers, and you can rank the attractiveness of all possible uses of capital throughout the universe.

Common yardsticks such as dividend yield, the ratio of price to earnings or to book value, and even growth rates have nothing to do with valuation except to the extent they provide clues to the amount and timing of cash flows into and from the business. Indeed, growth can destroy value if it requires cash inputs in the early years of a project or enterprise that exceed the discounted value of the cash that those assets will generate in later years…

Alas, though Aesop’s proposition and the third variable – that is, interest rates – are simple, plugging in numbers for the other two variables is a difficult task. Using precise numbers is, in fact, foolish; working with a range of possibilities is the better approach.

Usually, the range must be so wide that no useful conclusion can be reached. Occasionally, though, even very conservative estimates about the future emergence of birds reveal that the price quoted is startingly low in relation to value. (Let’s call this phenomenon the IBT – Inefficient Bush Theory.) To be sure, an investor needs some general understanding of business economics as well as the ability to think independently to reach a well-founded positive conclusion. But the investor does not need brilliance nor blinding insights.

At the other extreme, there are many times when the most brilliant of investors can’t muster a conviction about the birds to emerge, not even when a very broad range of estimates is employed. This kind of uncertainty frequently occurs when new businesses and rapidly changing industries are under examination. In cases of this sort, any capital commitment must be labeled speculative.

…

The line separating investment and speculation, which is never bright and clear, becomes blurred still further when most market participants have recently enjoyed triumphs. Nothing sedates rationality like large doses of effortless money.

Here Buffett is talking about the bubble in internet stocks in 1999. He acknowledges that, overall, much value had been created and there was much more to come. However, many individual internet companies destroyed value rather than creating it.

As noted earlier, Buffett and Munger love technological progress. But they generally don’t invest in tech companies because it doesn’t fit their buy-and-hold approach. It’s just not their game. Some venture capitalists have excelled at it, but it usually takes a statistical investment approach whereby a few big winners eventually outweigh a large number of losses.

• For example, the invention of the automobile transformed the U.S. economy, but the huge majority of auto companies – most of which were launched in the early part of last century – failed:https://en.wikipedia.org/wiki/List_of_defunct_automobile_manufacturers_of_the_United_States

Buffett again:

At Berkshire, we make no attempt to pick the few winners that will emerge from an ocean of unproven enterprises. We’re not smart enough to do that, and we know it. Instead, we try to apply Aesop’s 2600-year-old equation to opportunities in which we have reasonable confidence as to how many birds are in the bush and when they will emerge (a formulation that my grandsons would probably update to ‘A girl in the convertible is worth five in the phone book.’) Obviously, we can never precisely predict the timing of cash flows in and out of a business or their exact amount. We try, therefore, to keep our estimates conservative and to focus on industries where business surprises are unlikely to wreak havoc on owners. Even so, we make many mistakes: I’m the fellow, remember, who thought he understood the future economics of trading stamps, textiles, shoes and second-tier department stores.

Buffett writes about how to evaluate management:

The primary test of managerial economic performance is the achievement of a high earnings rate on equity capital employed (without undue leverage, accounting gimmickry, etc.) and not the achievement of consistent gains in earnings per share. In our view, many businesses would be better understood by their shareholder owners, as well as the general public, if managements and financial analysts modified the primary emphasis they place upon earnings per share, and upon yearly changes in that figure.

This leads to a discussion of economic Goodwill:

…businesses logically are worth far more than net tangible assets when they can be expected to produce earnings on such assets considerably in excess of market rates of return. The capitalized value of this excess return is economic Goodwill.

In 1972 (and now) relatively few businesses could be expected to consistently earn the 25% after tax on net tangible assets that was earned by See’s – doing it, furthermore, with conservative accounting and no financial leverage. It was not the fair market value of inventories, receivables or fixed assets that produced the premium rates of return. Rather it was a combination of intangible assets, particularly a pervasive favorable reputation with consumers based upon countless pleasant experiences they have had with both product and personnel.

Such a reputation creates a consumer franchise that allows the value of the product to the purchaser, rather than its production cost, to be the major determinant of selling price. Consumer franchises are a prime source of economic Goodwill. Other sources include governmental franchises not subject to profit regulation… and an enduring position as the low cost producer in an industry.

Buffett compares economic Goodwill with accounting Goodwill. As mentioned, economic Goodwill is when the net tangible assets produce earnings in excess of market rates of return. By contrast, accounting Goodwill is when company A buys company B, and the price paid is above the fair market value of net tangible assets. The difference between price paid and net tangible asset value is accounting Goodwill.

In the past, companies would amortize accounting Goodwill, typically over a 40-year period. But the current rule is that companies periodically test the value of the assets acquired. If it is determined that the acquired assets have less value than when acquired, then the accounting Goodwill is written down based on an impairment charge. This new way of measuring accounting Goodwill is what Buffett and Munger suggested.

Earlier we saw that the net present value of any business is the discounted value of its future cash flows. However, when we estimate future cash flows, it’s important to distinguish between earnings and free cash flow. Buffett uses the the termowner earnings instead offree cash flow. Buffett onowner earnings:

…These represent (a) reported earnings plus (b) depreciation, depletion, amortization, and certain other non-cash charges… less (c) the average annual amount of capitalized expenditures for plant and equipment, etc. that the business requires to fully maintain its long-term competitive position and its unit volume.

Buffett then observes that item (c), capital expenditures, usually requires a guess. Soowner earnings, or free cash flow, must also be an estimate. Nonetheless, free cash flow is what matters when estimating the intrinsic value of a business.

If a business requires heavy capital expenditures to maintain its competitive position, that’s worth less to an owner. By the same logic, if a business requires very little capital investment to maintain its competitive position, that’s clearly worth much more. The capital-light business will generally earn much higher returns on capital.

So, generally speaking, as Buffett points out, when capital expenditure requirements exceed depreciation, GAAP earnings overstate owner earnings. When capital expenditure requirements are less than depreciation, GAAP earnings understate owner earnings.

Moreover, Buffett offers a warning. Often marketers of businesses and securities present ‘cash flow’ as simply (a) plus (b), without subtracting (c). However, looking at cash flows without subtracting capital expenditures can give you a very misleading notion of what the business is worth. Every business must make some capital expenditures over time to maintain its competitive position.

Buffett sums up the discussion of owner earnings – or free cash flow – with a note on accounting:

Accounting numbers of course, are the language of business and as such are of enormous help to anyone evaluating the worth of a business and tracking its progress. Charlie and I would be lost without these numbers: they invariably are the starting point for us in evaluating our own businesses and those of others. Managers and owners need to remember, however, that accounting is but an aid to business thinking, never a substitute for it.

 

ACCOUNTING SHENANIGANS

Buffett observes that managers should try to report the essential information that investors need:

What needs to be reported is data – whether GAAP, non-GAAP, or extra-GAAP – that helps financially literate readers answer three key questions: (1) Approximately how much is this company worth? (2) What is the likelihood that it can meet its future obligations? and (3) How good a job are its managers doing, given the hand they have been dealt?

In 1998, Buffett observed that it had become common to manipulate accounting statements:

In recent years, probity has eroded. Many major corporations still play things straight, but a significant and growing number of otherwise high-grade managers – CEOs you would be happy to have as spouses for your children or as trustees under your will – have come to the view that it’s OK to manipulate earnings to satisfy what they believe are Wall Street’s desires. Indeed, many CEOs think this kind of manipulation is not only okay, but actually their duty.

These managers start with the assumption, all too common, that their job at all times is to encourage the highest stock price possible (a premise with which we adamantly disagree). To pump the price, they strive, admirably, for operational excellence. But when operations don’t produce the result hoped for, these CEOs result to unadmirable accounting strategems. These either manufacture the desired ‘earnings’ or set the stage for them in the future.

Rationalizing this behavior, these managers often say that their shareholders will be hurt if their currency for doing deals – that is, their stock – is not fully-priced, and they also argue that in using accounting shenanigans to get the figures they want, they are only doing what everybody else does. Once such an everybody’s-doing-it attitude takes hold, ethical misgivings vanish. Call this behavior Son of Gresham: Bad accounting drives out good.

The distortion du jour is the ‘restructuring charge,’ an accounting entry that can, of course, be legitimate but that too often is a device for manipulating earnings. In this bit of legerdemain, a large chunk of costs that should properly be attributed to a number of years is dumped into a single quarter, typically one already fated to disappoint investors. In some cases, the purpose of the charge is to clean up earnings misrepresentations of the past, and in others it is to prepare the ground for future misrepresentations. In either case, the size and timing of these charges is dictated by the cynical proposition that Wall Street will not mind if earnings fall short by $5 per share in a given quarter, just as long as this deficiency ensures that quarterly earnings in the future will consistently exceed expectations by five cents per share.

This dump-everything-into-one-quarter behavior suggests a corresponding ‘bold, imaginative’ approach to – golf scores. In his first round of the season, a golfer should ignore his actual performance and simply fill his card with atrocious numbers – double, triple, quadruple bogeys – and then turn in a score of, say, 140. Having established this ‘reserve,’ he should go to the golf shop and tell his pro that he wishes to ‘restructure’ his imperfect swing. Next, as he takes his new swing onto the course, he should count his good holes, but not his bad ones. These remnants from his old swing should be charged instead to the reserve established earlier. At the end of five rounds, then, his record will be 140, 80, 80, 80, 80 rather than 91, 94, 89, 94, 92. On Wall Street, they will ignore the 140 – which, after all, came from a ‘discontinued’ swing – and will classify our hero as an 80 shooter (and one who never disappoints).

For those who prefer to cheat up front, there would be a variant of this strategy. The golfer, playing alone with a cooperative caddy-auditor, should defer the recording of bad holes, take four 80s, accept the plaudits he gets for such athleticism and consistency, and then turn in a fifth card carrying a 140 score. After rectifying his earlier scorekeeping sins with this ‘big bath,’ he may mumble a few apologies but will refrain from returning the sums he has previously collected from comparing scorecards in the clubhouse. (The caddy, need we add, will have acquired a loyal patron.)

Unfortunately, CEOs who use variations of these scoring schemes in real life tend to become addicted to the games they’re playing – after all, it’s easier to fiddle with the scorecard than to spend hours on the practice tee – and never muster the will to give them up.

***

In discussing pension estimates, Buffett explains why index fund investors will do better – net of all costs – than active investors:

Naturally, everyone expects to be above average. And those helpers – bless their hearts – will certainly encourage their clients in this belief. But, as a class, the helper-aided group must be below average. The reason is simple: (1) Investors, overall, will necessarily earn an average return, minus costs they incur; (2) Passive and index investors, through their very inactivity, will earn that average minus costs that are very low; (3) With that group earning average returns, so must the remaining group – the active investors. But this group will incur high transaction, management, and advisory costs. Therefore, the active investors will have their returns diminished by a far greater percentage than will their inactive brethren. That means that the passive group – the ‘know-nothings’ – must win.

 

BERKSHIRE AT FIFTY AND BEYOND

Remarks by Buffett (in early 2015) on Berkshire’s fiftieth anniversary:

At Berkshire, we can – without incurring taxes or much in the way of other costs – move huge sums from businesses that have limited opportunities for incremental investment to other sectors with greater promise. Moreover, we are free of historical biases created by lifelong association with a given industry and are not subject to pressures from colleagues having a vested interest in maintaining the status quo. That’s important: If horses had controlled investment decisions, there would have been no auto industry.

Another major advantage we possess is an ability to buy pieces of wonderful business – a.k.a. common stocks. That’s not a course of action open to most managements. Over our history, this strategic alternative has proved to be very helpful; a broad range of options sharpens decision-making. The businesses we are offered by the stock market every day – in small pieces, to be sure – are often far more attractive than the businesses we are concurrently being offered in their entirety. Additionally, the gains we’ve realized from marketable securities have helped us make certain large acquisitions that would otherwise have been beyond our financial capabilities.

In effect, the world is Berkshire’s oyster – a world offering us a range of opportunities far beyond those realistically open to most companies. We are limited, of course, to businesses whose economic prospects we can evaluate. And that’s a serious limitation: Charlie and I have no idea what a great many companies will look like ten years from now. But that limitation is much smaller than that borne by an executive whose experience has been confined to a single industry. On top of that, we can profitably scale to a far larger size than many businesses that are constrained by the limited potential of the single industry in which they operate.

Berkshire has one further advantage that has become increasingly important over the years: We are now the home of choice for the owners and managers of many outstanding businesses. Families that own successful businesses have multiple options when they contemplate sale. Frequently, the best decision is to do nothing. There are worse things in life than having a prosperous business that one understands well. But sitting tight is seldom recommended by Wall Street. (Don’t ask the barber whether you need a haircut.)

When one part of a family wishes to sell while others wish to continue, a public offering often makes sense. But, when owners wish to cash out entirely, they usually consider one of two paths. The first is sale to a competitor who is salivating at the possibility of wringing ‘synergies’ from the combining of the two companies. The buyer invariably contemplates getting rid of large numbers of the seller’s associates, the very people who have helped the owner build his business. A caring owner, however – and there are plenty of them – usually does not want to leave his long-time associates sadly singing the old country song: ‘She got the goldmine, I got the shaft.’

The second choice for sellers is the Wall Street buyer. For some years, these purchasers accurately called themselves ‘leveraged buyout firms.’ When that term got a bad name in the early 1990s – remember RJR and Barbarians at the Gate? – these buyers hastily relabeled themselves ‘private-equity.’ The name may have changed but that was all: Equity is dramatically reduced and debt is piled on in virtually all private-equity purchases. Indeed, the amount that a private-equity purchaser offers to the seller is in part determined by the buyer assessing the maximum amount of debt that can be placed on the acquired company.

Later, if things go well and equity begins to build, leveraged buy-out shops will often seek to re-leverage with new borrowings. They then typically use part of the proceeds to pay a huge dividend that drives equity sharply downward, sometimes even to a negative figure. In truth, ‘equity’ is a dirty word for many private-equity buyers; what they love is debt. And, these buyers can frequently pay top dollar. Later the business will be resold, often to another leveraged buyer. In effect, the business becomes a piece of merchandise.

Berkshire offers a third choice to the business owner who wishes to sell: a permanent home, in which the company’s people and culture will be retained (though, occasionally, management changes will be needed). Beyond that, any business we acquire dramatically increases its financial strength and ability to grow. Its days of dealing with banks and Wall Street analysts are also forever ended. Some sellers don’t care about these matters. But, when sellers do, Berkshire does not have a lot of competition.

Buffett also observes that companies are worth more as a part of Berkshire than they would be separately. Berkshire can move funds between businesses or to new ventures instantly and without tax. Also, some costs would be duplicated if the businesses were independent entities. This includes regulatory and administrative expenses. Moreover, there are tax efficiencies, says Buffett: Certain tax credits available to Berkshire’s utilities are realizable because Berkshire generates large taxable income in other operations.

Buffett sums it up:

Today Berkshire possesses (1) an unmatched collection of businesses, most of them now enjoying favorable economic prospects; (2) a cadre of outstanding managers who, with few exceptions, are unusually devoted to both the subsidiary they operate and to Berkshire; (3) an extraordinary diversity of earnings, premier financial strength and oceans of liquidity that we will maintain under all circumstances; (4) a first-choice ranking among many owners and managers who are contemplating sale of their businesses; and (5) in a point related to the preceding item, a culture, distinctive in many ways from that of most large companies, that we have worked 50 years to develop and that is now rock-solid. These strengths provide us a wonderful foundation on which to build.

For the rest of Buffett’s comments, as well as observations by Charles T. Munger on the history and evolution of Berkshire Hathaway, see pages 34-43 of the 2014 letter:http://berkshirehathaway.com/letters/2014ltr.pdf

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here:https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

March 6, 2022

Lifelong learning–especially if pursued in a multidisciplinary fashion–can continuously improve your productivity and ability to think. Lifelong learning boosts your capacity to serve others.

Robert Hagstrom’s wonderful book,Investing: The Last Liberal Art(Columbia University Press, 2013), is based on the notion of lifelong, multidisciplinary learning.

Ben Franklin was a strong advocate for this broad-based approach to education. Charlie Munger–Warren Buffett’s business partner–wholeheartedly agrees with Franklin. Hagstrom quotes Munger:

Worldly wisdom is mostly very, very simple. There are a relatively small number of disciplines and a relatively small number of truly big ideas. And it’s a lot of fun to figure out. Even better, the fun never stops…

What I am urging on you is not that hard to do. And the rewards are awesome… It’ll help you in business. It’ll help you in law. It’ll help you in life. And it’ll help you in love… It makes you better able to serve others, it makes you better able to serve yourself, and it makes life more fun.

Hagstrom’s book is necessarily abbreviated. This blog post even more so. Nonetheless, I’ve tried to capture many of the chief lessons put forth by Hagstrom.

Here’s the outline:

• A Latticework of Mental Models
• Physics
• Biology
• Sociology
• Psychology
• Philosophy
• Literature
• Mathematics
• Decision Making

(Image: Unfolding of the Mind, by Agsandrew)

 

A LATTICEWORK OF MENTAL MODELS

Charlie Munger has long maintained that in order to be able to solve a broad array of problems in life, you must have a latticework of mental models. This means you have to master the central models from various areas–physics, biology, social studies, psychology, philosophy, literature, and mathematics.

As you assimilate the chief mental models, those models will strengthen and support one another, notes Hagstrom. So when you make a decision–whether in investing or in any other area–that decision is more likely to be correct if multiple mental models have led you to the same conclusion.

Ultimately, a dedication to lifelong, multidiscipinary learning will make us better people–better leaders, citizens, parents, spouses, and friends.

In the summer of 1749, Ben Franklin put forward a proposal for the education of youth. The Philadelphia Academy–later called the University of Pennsylvania–would stress both classical (“ornamental”) and practical education. Hagstrom quotes Franklin:

As to their studies, it would be well if they could be taught everything that is useful and everything that is ornamental. But art is long and their time is short. It is therefore proposed that they learn those things that are likely to be most useful and most ornamental, regard being had to the several professions for which they are intended.

Franklin held that gaining the ability to think well required the study of philosophy, logic, mathematics, religion, government, law, chemistry, biology, health, agriculture, physics, and foreign languages. Moreover, says Hagstrom, Franklin viewed the opportunity to study so many subjects as a wonderful gift rather than a burden.

(Painting by Mason Chamberlin (1762) – Philadelphia Museum of Art, via Wikimedia Commons)

Franklin himself was devoted to lifelong, multidisciplinary learning. He remained open-minded and intellectually curious throughout his life.

Hagstrom also observes that innovation often depends on multidisciplinary thinking:

Innovative thinking, which is our goal, most often occurs when two or more mental models act in combination.

 

PHYSICS

Hagstrom remarks that the law of supply and demand in economics is based on the notion of equilibrium, a fundamental concept in physics.

(Research scientist writing physics diagrams and formulas, by Shawn Hempel)

Many historians consider Sir Isaac Newton to be the greatest scientific mind of all time, points out Hagstrom. When he arrived at Trinity College at Cambridge, Newton had no mathematical training. But the scientific revolution had already begun. Newton was influenced by the ideas of Johannes Kepler, Galileo Galilei, and René Descartes. Hagstrom:

The lesson Newton took from Kepler is one that has been repeated many times throughout history: Our ability to answer even the most fundamental aspects of human existence depends largely on measuring instruments available at the time and the ability of scientists to apply rigorous mathematical reasoning to the data.

Galileo invented the telescope, which then proved that the heliocentric model proposed by Nicolaus Copernicus was correct, rather than the geocentric model–first proposed by Aristotle and later developed by Ptolemy. Moreover, Galileo developed the mathematical laws that describe and predict falling objects.

Hagstrom then explains the influence of Descartes:

Descartes promoted a mechanical view of the world. He argued that the only way to understand how something works is to build a mechanical model of it, even if that model is constructed only in our imagination. According to Descartes, the human body, a falling rock, a growing tree, or a stormy night all suggested that mechanical laws were at work. This mechanical view provided a powerful research program for seventeenth century scientists. It suggested that no matter how complex or difficult the observation, it was possible to discover the underlying mechanical laws to explain the phenomenon.

In 1665, due to the Plague, Cambridge was shut down. Newton was forced to retreat to the family farm. Hagstrom writes that, in quiet and solitude, Newton’s genius emerged:

His first major discovery was the invention of fluxions or what we now call calculus. Next he developed the theory of optics. Previously it was believed that color was a mixture of light and darkness. But in a series of experiments using a prism in a darkened room, Newton discovered that light was made up of a combination of the colors of the spectrum. The highlight of that year, however, was Newton’s discovery of the universal law of gravitation.

(Copy of painting by Sir Godfrey Kneller (1689), via Wikimedia Commons)

Newton’s three laws of motion unified Kepler’s planetary laws with Galileo’s laws of falling bodies. It took time for Newton to state his laws with mathematical precision. He waited twenty years before finally publishingPrincipia Mathematica.

Newton’s three laws were central to a shift in worldview on the part of scientists. The evolving scientific view held that the future could be predicted based on present data if scientists could discover the mathematical, mechanical laws underlying the data.

Prior to the scientific worldview, a mystery was often described as an unknowable characteristic of an “ultimate entity,” whether an “unmoved mover” or a deity. Under the scientific worldview, a mystery is a chance to discover fundamental scientific laws. The incredible progress of physics–which now includes quantum mechanics, relativity, and the Big Bang–has depended in part on the belief by scientists that reality is comprehensible. Albert Einstein:

The most incomprehensible thing about the universe is that it is comprehensible.

Physics was–and is–so successful in explaining and predicting a wide range of phenomena that, not surprisingly, scientists from other fields have often wondered whether precise mathematical laws or ideas can be discovered to predict other types of phenomena. Hagstrom:

In the nineteenth century, for instance, certain scholars wondered whether it was possible to apply the Newtonian vision to the affairs of men. Adolphe Quetelet, a Belgian mathematician known for applying probability theory to social phenomena, introduced the idea of “social physics.” Auguste Comte developed a science for explaining social organizations and for guiding social planning, a science he calledsociology. Economists, too, have turned their attention to the Newtonian paradigm and the laws of physics.

After Newton, scholars from many fields focused their attention on systems that demonstrate equilibrium (whether static or dynamic), believing that it is nature’s ultimate goal. If any deviations in the forces occurred, it was assumed that the deviations were small and temporary–and the system would always revert back to equilibrium.

Hagstrom explains how the British economist Alfred Marshall adopted the concept of equilibrium in order to explain the law of supply and demand. Hagstrom quotes Marshall:

When demand and supply are in stable equilibrium, if any accident should move the scale of production from its equilibrium position, there will instantly be brought into play forces tending to push it back to that position; just as a stone hanging from a string is displaced from its equilibrium position, the force of gravity will at once tend to bring it back to its equilibrium position. The movements of the scale of production about its position of equilibrium will be of a somewhat similar kind.

(Alfred Marshall, via Wikimedia Commons)

Marshall’sPrinciples of Economics was the standard textbook until Paul Samuelson publishedEconomics in 1948, says Hagstrom. But the concept of equilibrium remained. Firms seeking to maximize profits translate the preferences of households into products. The logical structure of the exchange is a general equilibrium system, according to Samuelson.

Samuelson’s view of the stock market was influenced by the works of Louis Bachelier, Maurice Kendall, and Alfred Cowles, notes Hagstrom.

In 1932, Cowles founded the Cowles Commission for Research and Economics. Later on, Cowles studied 6,904 predictions of the stock market from 1929 to 1944. Cowles learned that no one had demonstrated any ability to predict the stock market.

Kendall, a professor of statistics at the London School of Economics, studied the histories of various individual stock prices going back fifty years. Kendall was unable to find any patterns that would allow accurate predictions of future stock prices.

Samuelson thought that stock prices jump around because of uncertainty about how the businesses in question will perform in the future. The intrinsic value of a given stock is determined by the future cash flow the business will produce. But that future cash flow is unknown.

Bachelier’s work showed that the mathematical expectation of a speculator is zero, meaning that the current stock price is in equilibrium based on an equal number of buyers and sellers.

Samuelson, building on Bachelier’s work, invented the rational expectations hypothesis. From the assumption that market participants are rational, it followed that the current stock price is the best collective guess of the intrinsic value of the business–based on estimated future cash flows.

Eugene Fama later extended Samuelson’s view into what came to be called the Efficient Markets Hypothesis (EMH). Stock prices fully reflect all available information, therefore it’s not possible–except by luck–for any individual investor to beat the market over the long term.

Many scientists have questioned the EMH. The stock market sometimes does not seem rational. People often behave irrationally.

In science, however, it’s not enough to show that the existing theory has obvious flaws. In order to supplant existing scientific theory, scientists must come up with a better theory–one that better predicts the phenomena in question.Rationalist economics, including EMH, is still the best approximation for a wide range of phenomena.

Some scientists are working with the idea of acomplex adaptive system as a possible replacement for more traditional ideas of the stock market. Hagstrom:

Every complex adaptive system is actually a network of many individual agents all acting in parallel and interacting with one another. The critical variable that makes a system both complex and adaptive is the idea that agents (neurons, ants, or investors) in the system accumulate experience by interacting with other agents and then change themselves to adapt to a changing environment. No thoughtful person, looking at the present stock market, can fail to conclude that it shows all the traits of a complex adaptive system. And this takes us to the crux of the matter. If a complex adaptive system is, by definition,continuously adapting, it is impossible for any such system, including the stock market, ever to reach a state of perfect equilibrium.

It’s much more widely accepted today that people often do behave irrationally. But Fama argues that an efficient market does not require perfect rationality or information.

Hagstrom concludes that, while the market is mostly efficient, rationalist economics is not the full answer. There’s much more to the story, although it will take time to work out the details.

 

BIOLOGY

(Photo by Ben Schonewille)

Robert Darwin, a respected physician, enrolled his son Charles at the University of Edinburgh. Robert wanted his son to study medicine. But Charles had no interest. Instead, he spent his time studying geology and collecting insects and specimens.

Robert realized his son wouldn’t become a doctor, so he sent Charles to Cambridge to study divinity. Although Charles got a bachelor’s degree in theology, he formed some important connections with scientists, says Hagstrom:

The Reverend John Stevens Henslow, professor of botany, permitted the enthusiastic amateur to sit in on his lectures and to accompany him on his daily walks to study plant life. Darwin spent so many hours in the professor’s company that he was known around the university as “the man who walks with Henslow.”

Later, Professor Henslow recommended Darwin for the position of naturalist on a naval expedition. Darwin’s father objected, but Darwin’s uncle, Josiah Wedgewood II, intervened. When the HMSBeagle set sail on December 27, 1831, from Plymouth, England, Charles Darwin was aboard.

Darwin’s most important observations happened at the Galapagos Islands, near the equator, six hundred miles west of Ecuador. Hagstrom:

Darwin, the amateur geologist, knew that the Galapagos were classified as oceanic islands, meaning they had arisen from the sea by volcanic action with no life forms aboard. Nature creates these islands and then waits to see what shows up. An oceanic island eventually becomes inhabited but only by forms that can reach it by wings (birds) or wind (spores and seeds)…

Darwin was particularly fascinated by the presence of thirteen types of finches. He first assumed these Galapagos finches, today called Darwin’s finches, were a subspecies of the South American finches he had studied earlier and had most likely been blown to sea in a storm. But as he studied distribution patterns, Darwin observed that most islands in the archipelago carried only two or three types of finches; only the larger central islands showed greater diversification. What intrigued him even more was that all the Galapagos finches differed in size and behavior. Some were heavy-billed seedeaters; others were slender billed and favored insects. Sailing through the archipelago, Darwin discovered that the finches on Hood Island were different from those on Tower Island and that both were different from those on Indefatigable Island. He began to wonder what would happen if a few finches on Hood Island were blown by high winds to another island. Darwin concluded that if the newcomers were pre-adapted to the new habitat, they would survive and multiply alongside the resident finches; if not, their number would ultimately diminish. It was one thread of what would ultimately become his famous thesis.

(Galapagos Islands, Photo by Hugoht)

Hagstrom continues:

Reviewing his notes from the voyage, Darwin was deeply perplexed. Why did the birds and tortoises on some islands of the Galapagos resemble the species found in South America while those on other islands did not? This observation was even more disturbing when Darwin learned that the finches he brought back from the Galapagos belonged to different species and were not simply different varieties of the same species, as he had previously believed. Darwin also discovered that the mockingbirds he had collected were three distinct species and the tortoises represented two species. He began referring to these troubling questions as “the species problem,” and outlined his observations in a notebook he later entitled “Notebook on the Transmutation of the Species.”

Darwin now began an intense investigation into the species variation. He devoured all the written work on the subject and exchanged voluminous correspondence with botanists, naturalists, and zookeepers–anyone who had information or opinions about species mutation. What he learned convinced him that he was on the right track with his working hypothesis that species do in fact change, whether from place to place or from time period to time period. The idea was not only radical at the time, it was blasphemous. Darwin struggled to keep his work secret.

(Photo by Maull and Polyblank (1855), via Wikimedia Commons)

It took several years–until 1838–for Darwin to put together his hypothesis. Darwin wrote in his notebook:

Being well-prepared to appreciate the struggle for existence which everywhere goes on from long-continued observation of the habits of animals and plants, it at once struck me that under these circumstances, favorable variations would tend to be preserved and unfavorable ones to be destroyed. The result of this would be the formation of new species. Here, then, I had at last got a theory–a process by which to work.

The struggle for survival was occurring not only between species, but also between individuals of the same species, Hagstrom points out. Favorable variations are preserved. After many generations, small gradual changes begin to add up to larger changes. Evolution.

Darwin delayed publishing his ideas, perhaps because he knew they would be highly controversial, notes Hagstrom. Finally, in 1859, Darwin publishedOn the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. The book sold out on its first day. By 1872,The Origin of Species was in its sixth edition.

Hagstrom writes that in the first edition of Alfred Marshall’s famous textbook,Principles of Economics, the economist put the following on the title page:

Natura non facit saltum

Darwin himself used the same phrase–which means “nature does not make leaps”–in his book,The Origin of Species. Although Marshall never explained his thinking explicitly, it seems Marshall meant to align his work with Darwinian thinking.

Less than two decades later, Austrian-born economist Joseph Schumpeter put forth his central idea ofcreative destruction. Hagstrom quotes British economist Christopher Freeman, who–after studying Schumpeter’s life–remarked:

The central point of his whole life work is that capitalism can only be understood as an evolutionary process of continuous innovation and creative destruction.

Hagstrom explains:

Innovation, said Schumpeter, is the profitable application of new ideas, including products, production processes, supply sources, new markets, or new ways in which a company could be organized. Whereas standard economic theory believed progress was a series of small incremental steps, Schumpeter’s theory stressed innovative leaps, which in turn caused massive disruption and discontinuity–an idea captured in Schumpeter’s famous phrase “the perennial gale of creative destruction.”

But all these innovative possibilities meant nothing without the entrepreneur who becomes the visionary leader of innovation. It takes someone exceptional, said Schumpeter, to overcome the natural obstacles and resistance to innovation. Without the entrepreneur’s desire and willingness to press forward, many great ideas could never be launched.

(Image from the Department of Economics, University of Freiburg, via Wikimedia Commons)

Moreover, Schumpeter held that entrepreneurs can thrive only in certain environments. Property rights, a stable currency, and free trade are important. And credit is even more important.

In the fall of 1987, a group of physicists, biologists, and economists held a conference at the Santa Fe Institute. The economist Brian Arthur gave a presentation on “New Economics.” A central idea was to apply the concept of complex adaptive systems to the science of economics. Hagstrom records that the Santa Fe group isolated four features of the economy:

Dispersed interaction: What happens in the economy is determined by the interactions of a great number of individual agents all acting in parallel. The action of any one individual agent depends on the anticipated actions of a limited number of agents as well as on the system they cocreate.

No global controller: Although there are laws and institutions, there is no one global entity that controls the economy. Rather, the system is controlled by the competition and coordination between agents of the system.

Continual adaptation: The behavior, actions, and strategies of agents, as well as their products and services, are revised continually on the basis of accumulated experience. In other words, the system adapts. It creates new products, new markets, new institutions, and new behavior. It is an ongoing system.

Out-of-equilibrium dynamics: Unlike the equilibrium models that dominate the thinking in classical economics, the Santa Fe group believed the economy, because of constant change, operates far from equilibrium.

Hagstrom argues that different investment or trading strategies throughout history have competed against one another. Those that have most accurately predicted the future for various businesses and their associated stock prices have survived, while less profitable strategies have disappeared.

But in any given time period, once a specific strategy becomes profitable, then more money flows into it, which eventually makes it less profitable. New strategies are then invented and compete against one another. As a result, a new strategy becomes dominant and then the process repeats.

Thus, economies and markets evolve over time. There is no stable equilibrium in a market except in the short term. To go from the language of biology to the language of business, Hagstrom refers to three important books:

• Creative Destruction: Why Companies That Are Built to Last Underperform the Market–and How to Successfully Transform Them, by Richard Foster and Sarah Kaplan of McKinsey & Company
• The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, by Clayton Christensen
• The Innovator’s Solution: Creating and Sustaining Successful Growth, by Clayton Christensen and Michael Raynor

Hagstrom sums up the lessons from biology as compared to the previous ideas from physics:

Indeed, the movement from the mechanical view of the world to the biological view of the world has been called the “second scientific revolution.” After three hundred years, the Newtonian world, the mechanized world operating in perfect equilibrium, is now the old science. The old science is about a universe of individual parts, rigid laws, and simple forces. The systems are linear: Change is proportional to the inputs. Small changes end in small results, and large changes make for large results. In the old science, the systems are predictable.

The new science is connected and entangled. In the new science, the system is nonlinear and unpredictable, with sudden and abrupt changes. Small changes can have large effects while large events may result in small changes. In nonlinear systems, the individual parts interact and exhibit feedback effects that may alter behavior. Complex adaptive systems must be studied as a whole, not in individual parts, because the behavior of the system is greater than the sum of the parts.

The old science was concerned with understanding the laws of being. The new science is concerned with the laws of becoming.

(Photo by Isabellebonaire)

Hagstrom then quotes the last passage from Darwin’sThe Origin of Species:

It is interesting to contemplate an entangled bank, clothed with many plants of many kinds, with birds singing on the bushes, with various insects flitting about, and with worms crawling through the damp earth, and to reflect that these elaborately constructed forms, so different from each other, and dependent on each other in so complex a manner, have all been produced by laws acting around us. These laws, taken in the largest sense, being Growth with Reproduction; Inheritance which is almost implied by reproduction; Variability from the indirect and direct action of the external conditions of life, and from use and disuse; a Ratio of Increase so high as to lead to a Struggle for Life, and as a consequence to Natural Selection, entailing divergence of Character and Extinction of less improved forms. Thus, from the war of nature, from famine and death, the most exalted object which we are capable of conceiving, namely, the production of higher animals, directly follows. There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.

 

SOCIOLOGY

Because significant increases in computer power are now making vast amounts of data about human behavior available, the social sciences may at some point get enough data to figure out more precisely and more generally the laws of human behavior. But we’re not there yet.

(Auguste Comte, via Wikimedia Commons)

The nineteenth century–despite the French philosopher Auguste Comte’s efforts to establish one unified social science–ended with several distinct specialties, says Hagstrom, including economics, political science, and anthropology.

Scottish economist Adam Smith published hisWealth of Nations in 1776. Smith argued for what is now called laissez-faire capitalism, or a system free from government interference, including industry regulation and protective tariffs. Smith also held that a division of labor, with individuals specializing in various tasks, led to increased productivity. This meant more goods at lower prices for consumers, but it also meant more wealth for the owners of capital. And it implied that the owners of capital would try to limit the wages of labor. Furthermore, working conditions would likely be bad without government regulation.

Predictably, political scientists appeared on the scene to study how the government should protect the rights of workers in a democracy. Also, the property rights of owners of capital had to be protected.

Social psychologists studied how culture affects psychology, and how the collective mind affects culture. Social biologists, meanwhile, sought to apply biology to the study of society, notes Hagstrom. Recently scientists, including Edward O. Wilson, have introducedsociobiology, which involves the attempt to apply the scientific principles of biology to social development.

Hagstrom writes:

Although the idea of a unified theory of social science faded in the late nineteenth century, here at the beginning of the twenty-first century, there has been a growing interest in what we might think of as a new unified approach. Scientists have now begun to study the behavior of whole systems–not only the behavior of individuals and groups but the interactions between them and the ways in which this interaction may in turn influence subsequent behavior. Because of this reciprocal influence, our social system is constantly engaged in a socialization process the consequence of which not only alters our individual behavior but often leads to unexpected group behavior.

To explain the formation of a social group, the theory of self-organization has been developed. Ilya Prigogine, the Russian chemist, was awarded the Nobel Prize in 1977 for his thermodynamic concept of self-organization.

Paul Krugman, winner of the 2008 Nobel Prize for Economics, studied self-organization as applied to the economy. Hagstrom:

Setting aside for the moment the occasional recessions and recoveries caused by exogenous events such as oil shocks or military conflicts, Krugman believes that economic cycles are in large part caused by self-reinforcing effects. During a prosperous period, a self-reinforcing process leads to greater construction and manufacturing until the return on investment begins to decline, at which point an economic slump begins. The slump in itself becomes a self-reinforcing effect, leading to lower production; lower production, in turn, will eventually cause return on investment to increase, which starts the process all over again.

Hagstrom notes that equity and debt markets are good examples of self-organizing, self-reinforcing systems.

If self-organization is the first characteristic of complex adaptive systems, thenemergence is the second characteristic. Hagstrom says that emergence refers to the way individual units–whether cells, neurons, or consumers–combine to create something greater than the sum of the parts.

(Collective Dynamics of Complex Systems, by Dr. Hiroki Sayama, via Wikimedia Commons)

One fascinating aspect of human collectives is that, in many circumstances–like finding the shortest way through a maze–the collective solution is better when there are both smart and not-so-smart individuals in the collective. This more diverse collective outperforms a group that is composed only of smart individuals.

This implies that the stock market may more accurately aggregate information when the participants include many different types of people, such as smart and not-so-smart, long-term and short-term, and so forth, observes Hagstrom.

There are many areas where a group of people is actually smarter than the smartest individual in the group. Hagstrom mentions that Francis Galton, the English Victorian-era polymath, wrote about a contest in which 787 people guessed at the weight of a large ox. Most participants in the contest were not experts by any means, but ordinary people. The ox actually weighed 1,198 pounds. The average guess of the 787 guessers was 1,197 pounds, which was more accurate than the guesses made by the smartest and the most expert guessers.

This type of experiment can easily be repeated. For example, take a jar filled with pennies, where only you know how many pennies are in the jar. Pass the jar around in a group of people and ask each person–independently (with no discussion)–to write down their guess of how many pennies are in the jar. In a group that is large enough, you will nearly always discover that the average guess is better than any individual guess. (That’s been the result when I’ve performed this experiment in classes I’ve taught.)

In order for the collective to be that smart, the members must be diverse and the members’ guesses must be independent from one another. So the stock market is efficient when these two conditions are satisfied. But if there is a breakdown in diversity, or if individuals start copying one another too much–what Michael Mauboussin calls an information cascade–then you could have a boom, fad, fashion, or crash.

There are some situations where an individual can be impacted by the group. Solomon Asch did a famous experiment in which the subject is supposed to match lines that have the same length. It’s an easy question that every subject–if left alone–gets right. But then Asch has seven out of eight participantsdeliberately choose the wrong answer, unbeknownst to the subject of the experiment, who is the eighth participant in the same room. When this experiment was repeated many times, roughly one-third of the subjects gave the same answer as the group, even though this answer is obviously wrong. Such can be the power of a group opinion.

Hagstrom asks about how crashes can happen. Danish theoretical physicist Per Bak developed the notion ofself-organized criticality.

According to Bak, large complex systems composed of millions of interacting parts can break down not only because of a single catastrophic event but also because of a chain reaction of smaller events. To illustrate the concept of self-criticality, Bak often used the metaphor of a sand pile… Each grain of sand is interlocked in countless combinations. When the pile has reached its highest level, we can say the sand is in a state of criticality. It is just on the verge of becoming unstable.

(Computer Simulation of Bak-Tang-Weisenfeld sandpile, with 28 million grains, by Claudio Rocchini, via Wikimedia Commons)

Adding one more grain starts an avalanche. Bak and two colleagues applied this concept to the stock market. They assumed that there are two types of agents,rational agents andnoise traders. Most of the time, the market is well-balanced.

But as stock prices climb, rational agents sell and leave the market, while more noise traders following the trend join. When noise traders–trend followers–far outnumber rational agents, a bubble can form in the stock market.

 

PSYCHOLOGY

The psychologists Daniel Kahneman and Amos Tversky did research together for over two decades. Kahneman was awarded the Nobel Prize in Economics in 2002. Tversky would also have been named had he not passed away.

(Daniel Kahneman, via Wikimedia Commons)

Much of their groundbreaking research is contained inJudgment Under Uncertainty: Heuristics and Biases (1982).

Here you will find all the customary behavioral finance terms we have come to know and understand: anchoring, framing, mental accounting, overconfidence, and overreaction bias. But perhaps the most significant insight into individual behavior wasloss aversion.

Kahneman and Tversky discovered that how choices are framed–combined with loss aversion–can materially impact how people make decisions. For instance, in one of their well-known experiments, they asked people to choose between the following two options:

• (a) Save 200 lives for sure.
• (b) Have a one-third chance of saving 600 lives and a two-thirds chance of saving no one.

In this scenario, people overwhelmingly chose (a)–to save 200 lives for sure. Kahneman and Tversky next asked the same people to choose between the following two options:

• (a) Have 400 people die for sure.
• (b) Have a two-thirds chance of 600 people dying and a one-third chance of no one dying.

In this scenario, people preferred (b)–a two-thirds chance of 600 people dying, and a one-third chance of no one dying.

But the two versions of the problem are identical. The number of people saved in the first version equals the number of people who won’t die in the second version.

What Kahneman and Tversky had demonstrated is that people are risk averse when considering potential gains, but risk seeking when facing the possibility of a certain loss. This is the essence of prospect theory, which is captured in the following graph:

(Value function in Prospect Theory, drawing by Marc Rieger, via Wikimedia Commons)

Loss aversion refers to the fact that people weigh a potential loss about 2.5 times more than an equivalent gain. That’s why the value function in the graph is steeper for losses.

Richard Thaler and Shlomo Benartzi researched loss aversion by hypothesizing that the less frequently an investor checks the price of a stock he or she owns, the less likely the investor will be to sell the stock because of temporary downward volatility. Thaler and Benartzi invented the termmyopic loss aversion.

Hagstrom writes:

In my opinion, the single greatest psychological obstacle that prevents investors from doing well in the stock market is myopic loss aversion. In my twenty-eight years in the investment business, I have observed firsthand the difficulty investors, portfolio managers, consultants, and committee members of large institutional funds have with internalizing losses (loss aversion), made all the more painful by tabulating losses on a frequent basis (myopic loss aversion). Overcoming this emotional burden penalizes all but a very few select individuals.

Perhaps it is not surprising that the one individual who has mastered myopic loss aversion is also the world’s greatest investor–Warren Buffett…

Buffett understands that as long as the earnings of the businesses you own move higher over time, there’s no reason to worry about shorter term stock price volatility. Because Berkshire Hathaway, Buffett’s investment vehicle, holds both public stocks and wholly owned private businesses, Buffett’s long-term outlook has been reinforced. Hagstrom quotes Buffett:

I don’t need a stock price to tell me what I already know about value.

Hagstrom mentions Berkshire’s investment in The Coca-Cola Company (KO), in 1988. Berkshire invested $1 billion, which was at that time the single largest investment Berkshire had ever made. Over the ensuing decade, KO stock went up ten times, while the S&P 500 Index only went up three times. But four out of those ten years, KO stock underperformed the market. Trailing the market 40 percent of the time didn’t bother Buffett a bit.

As Hagstrom observes, Benjamin Graham–the father of value investing, and Buffett’s teacher and mentor–made a distinction between the investor focused on long-term business value and the speculator who tries to predict stock prices in the shorter term. The true investor should never be concerned with shorter term stock price volatility.

(Ben Graham, Photo by Equim43, via Wikimedia Commons)

Hagstrom quotes Graham’s The Intelligent Investor:

The investor who permits himself to be stampeded or unduly worried by unjustified market declines in his holdings is perversely transforming his basic advantage into a basic disadvantage. That man would be better off if his stocks had no market quotation at all, for he would then be spared the mental anguish caused him by another person’s mistakes of judgment.

Terence Odean, a behavioral economist, has done extensive research on the investment decisions of individuals and households. Odean discovered that:

• Many investors trade often–Odean found a 78 percent portfolio turnover ratio in his first study, which tracked 97,483 trades from ten thousand randomly selected accounts.
• Over the subsequent 4 months, one year, and two years, the stocks that investors bought consistently trailed the market, while the stocks that investors sold beat the market.

Hagstrom mentions that people use mental models as a basis for understanding reality and making decisions. But we tend to assume that each mental model we have is equally probable, rather than working to assign different probabilities to different models.

Moreover, people typically can make models for what something is–or what is true–instead of what something is not–or what is false. Also, our mental models are usually quite incomplete. And we tend to forget details of our models, especially after time passes. Finally, writes Hagstrom, people tend to construct mental models based on superstition or unwarranted belief.

Hagstrom asks the question: Why do people tend to be so gullible in general? For instance, while there’s no evidence that market forecasts have any value, many otherwise intelligent people pay attention to them and even make decisions based on them.

The answer, states Hagstrom, is that we are wired to seek and to find patterns. We have two basic mental systems, System 1 (intuition) and System 2 (reason). System 1 operates automatically. It takes mental shortcuts which often work fine, but not always. System 1 is designed to find patterns. And System 1 seeks confirming evidence for its hypotheses (patterns).

But even System 2–which humans can use to do math, logic, and statistics–uses a positive test strategy, meaning that itseeks confirming evidence for its hypotheses (patterns), rather than disconfirming evidence.

 

PHILOSOPHY

Hagstrom introduces the chapter:

A true philosopher is filled with a passion to understand, a process that never ends.

(Socrates, J. Aars Platon (1882), via Wikimedia Commons)

Metaphysics is one area of philosophy. Aesthetics, ethics, and politics are other areas. But Hagstrom focuses his discussion of philosophy onepistemology, the study of knowledge.

Having spent a few years studying the history and philosophy of science, I would say thatepistemology includes the following questions:

• What different kinds of knowledge can we have?
• What constitutes scientific knowledge?
• Is any part of our knowledge certain, or can all knowledge be improved indefinitely?
• How does scientific progress happen?

In a sense, epistemology is thinking about thinking. Epistemology is also studying the history of science in great detail, because humans have made enormous progress in generating scientific knowledge.

Studying epistemology can help us to become better, more rigorous, and more coherent thinkers, which can make us better investors.

Hagstrom makes it clear in the Preface that his book is necessarily abbreviated, otherwise it would have been a thousand pages long. That said, had he been aware of Willard Van Orman Quine’s epistemology, Hagstrom likely would have mentioned it.

Here is a passage from Quine’sFrom A Logical Point of View:

The totality of our so-called knowledge or beliefs, from the most casual matters of geography and history to the profoundest laws of atomic physics or even of pure mathematics and logic, is a man-made fabric which impinges on experience only along the edges. Or, to change the figure, total science is like a field of force whose boundary conditions are experience. A conflict with experience at the periphery occasions readjustments in the interior of the field. Truth values have to be redistributed over some of our statements. Re-evaluation of some statements entails re-evaluation of others, because of their logical interconnections–the logical laws being in turn simply certain further statements of the system, certain further elements of the field. Having re-evaluated one statement we must re-evaluate some others, which may be statements logically connected with the first or may be the statements of logical connections themselves. But the total field is so underdetermined by its boundary conditions, experience, that there is much latitude of choice as to what statements to re-evaluate in the light of any single contrary experience. No particular experiences are linked with any particular statements in the interior of the field, except indirectly through considerations of equilibrium affecting the field as a whole.

If this view is right, it is misleading to speak of the empirical content of an individual statement–especially if it is a statement at all remote from the experiential periphery of the field. Furthermore it becomes folly to seek a boundary between synthetic statements, which hold contingently on experience, and analytic statements, which hold come what may. Any statement can be held true come what may, if we make drastic enough adjustments elsewhere in the system. Even a statement very close to the periphery can be held true in the face of recalcitrant experience by pleading hallucination or by amending certain statements of the kind called logical laws. Conversely, by the same token, no statement is immune to revision. Revision even of the logical law of the excluded middle has been proposed as a means of simplifying quantum mechanics…

(Image by Dmytro Tolokonov)

Quine continues:

For vividness I have been speaking in terms of varying distances from a sensory periphery. Let me now try to clarify this notion without metaphor. Certain statements, thoughabout physical objects and not sense experience, seem peculiarly germane to sense experience–and in a selective way: some statements to some experiences, others to others. Such statements, especially germane to particular experiences, I picture as near the periphery. But in this relation of “germaneness” I envisage nothing more than a loose association reflecting the relative likelihood, in practice, of our choosing one statement rather than another for revision in the event of recalcitrant experience. For example, we can imagine recalcitrant experiences to which we would surely be inclined to accomodate our system by re-evaluating just the statement that there are brick houses on Elm Street, together with related statements on the same topic. We can imagine other recalcitrant experiences to which we would be inclined to accomodate our system by re-evaluating just the statement that there are no centaurs, along with kindred statements. A recalcitrant experience can, I have urged, be accomodated by any of various alternative re-evaluations in various alternative quarters of the total system; but, in the cases which we are now imagining, our natural tendency to disturb the total system as little as possible would lead us to focus our revisions upon these specific statements concerning brick houses or centaurs. These statements are felt, therefore, to have a sharper empirical reference than highly theoretical statements of physics or logic or ontology. The latter statements may be thought of as relatively centrally located within the total network, meaning merely that little preferential connection with any particular sense data obtrudes itself.

As an empiricist, I continue to think of the conceptual scheme of science as a tool, ultimately, for predicting future experience in the light of past experience. Physical objects are conceptually imported into the situation as convenient intermediaries–not by definition in terms of experience, but simply as irreducible posits comparable, epistemologically, to the gods of Homer. For my part I do, qua lay physicist, believe in physical objects and not in Homer’s gods; and I consider it a scientific error to believe otherwise. But in point of epistemological footing the physical objects and the gods differ only in degree and not in kind. Both sorts of entities enter our conception only as cultural posits. The myth of physical objects is epistemologically superior to most in that it has proved more efficacious than other myths as a device for working a manageable structure into the flux of experience.

…

Physical objects, small and large, are not the only posits. Forces are another example; and indeed we are told nowadays that the boundary between energy and matter is obsolete. Moreover, the abstract entities which are the substance of mathematics–ultimately classes and classes of classes and so on up–are another posit in the same spirit. Epistemologically these are posits on the same footing with physical objects and gods, neither better nor worse except for differences in the degree to which they expedite our dealings with sense experiences.

Historically, philosophers distinguished between “analytic” statements, which were thought to be true by definition, and “synthetic” statements, which were thought to be true on the basis of certain empirical data or experiences. One of Quine’s chief points is that this distinction doesn’t hold.

Mathematics, logic, scientific theories, scientific laws, working hypotheses, ordinary language, and much else including simple observations, are all a part of science.The goal of science–which extends common sense–is to predict various future experiences–including experiments–on the basis of past experiences.

When predictions–including experiments–don’t turn out as expected, then any part of the totality of science is revisable. Often it makes sense to revise specific hypotheses, or specific statements that are close to experience. But sometimes highly theoretical statements or ideas–including the laws of mathematics, the laws of logic, and the most well-established scientific laws–are revised in order to make the overall system of science work better, i.e., predict phenomena (future experiences) better, with more generality or with more exactitude.

The chief way scientists have made–and continue to make–progress is by testing predictions that are implied by existing scientific theory or law, or that are implied by new hypotheses under consideration.

(Top quark and anti top quark pair decaying into jets, Collider Detector at Fermilab, via Wikimedia Commons)

Because of recent advances in computing power and because of the explosion of shared knowledge, ideas, and experiments on the internet, scientific progress is probably happening much faster than ever before. It’s a truly exciting time for all curious people and scientists. And once artificial intelligence passes thesingularity threshold, scientific progress is likely to skyrocket, even beyond what we can imagine.

 

LITERATURE

Critical reading is a crucial part of becoming a better thinker.

(Photo by VijayGES2, via Wikimedia Commons)

One excellent book about how to read analytically isHow to Read a Book, by Mortimer J. Adler. The goal of analytical reading is to improve your understanding–as opposed to only gaining information. To this end, Adler suggests active readers keep the following four questions in mind:

• What is the book about as a whole?
• What is being said in detail?
• Is the book true, in whole or part?
• What of it?

Before deciding to read a book in detail, it can be helpful to read the preface, table of contents, index, and bibliography. Also, read a few paragraphs at random. These steps will help you to get a sense of what the book is about as a whole. Next, you can skim the book to learn more about what is being said in detail, and whether it’s worth reading the entire book carefully.

Then, if you decide to read the entire book carefully, you should approach it like you would approach assigned reading for a university class. Figure out the main points and arguments. Take notes if that helps you learn. The goal is to understand the author’s chief arguments, and whether–or to what extent–those arguments are true.

The final step iscomparative reading, says Hagstrom. Adler considers this the hardest step. Here the goal is to learn about a specific subject. You want to determine which books on the subject are worth reading, and then compare and contrast these books.

Hagstrom points out that the three greatest detectives in fiction are Auguste Dupin, Sherlock Holmes, and Father Brown. We can learn much from studying the stories involving these sleuths.

Auguste Dupin was created by Edgar Allan Poe. Hagstrom remarks that we can learn the following from Dupin’s methods:

• Develop a skeptic’s mindset; don’t automatically accept conventional wisdom.
• Conduct a thorough investigation.

Sherlock Holmes was created by Sir Arthur Conan Doyle.

(Illustration by Sidney Paget (1891), via Wikimedia Commons)

From Holmes, we can learn the following, says Hagstrom:

• Begin an investigation with an objective and unemotional viewpoint.
• Pay attention to the tiniest details.
• Remain open-minded to new, even contrary, information.
• Apply a process of logical reasoning to all you learn.

Father Brown was created by G. K. Chesterton. From Father Brown, we can learn:

• Become a student of psychology.
• Have faith in your intuition.
• Seek alternative explanations and redescriptions.

Hagstrom ends the chapter by quoting Charlie Munger:

I believe in… mastering the best that other people have figured out [rather than] sitting down and trying to dream it up yourself… You won’t find it that hard if you go at it Darwinlike, step by step with curious persistence. You’ll be amazed at how good you can get… It’s a huge mistake not to absorb elementary worldly wisdom… Your life will be enriched–not only financially but in a host of other ways–if you do.

 

MATHEMATICS

Hagstrom quotes Warren Buffett:

…the formula for valuing ALL assets that are purchased for financial gain has been unchanged since it was first laid out by a very smart man in about 600 B.C.E. The oracle was Aesop and his enduring, though somewhat incomplete, insight was “a bird in the hand is worth two in the bush.” To flesh out this principle, you must answer only three questions. How certain are you that there are indeed birds in the bush? When will they emerge and how many will there be? What is the risk-free interest rate? If you can answer these three questions, you will know the maximum value of the bush–and the maximum number of birds you now possess that should be offered for it. And, of course, don’t literally think birds. Think dollars.

Hagstrom explains that it’s the same formula whether you’re evaluating stocks, bonds, manufacturing plants, farms, oil royalties, or lottery tickets. As long as you have the numbers needed for the calculation, the attractiveness of all investment opportunities can be evaluated and compared.

So to value any business, you have to estimate the future cash flows of the business, and then discount those cash flows back to the present. This is the DCF–discounted cash flows–method for determining the value of a business.

Although Aesop gave the general idea, John Burr Williams, inThe Theory of Investment Value (1938), was the first to explain the DCF approach explicitly. Williams had studied mathematics and chemistry as an undergraduate at Harvard University. After working as a securities analyst, Williams returned to Harvard to get a PhD in economics.The Theory of Investment Value was Williams’ dissertation.

Hagstrom writes that in 1654, the Chevalier de Méré, a French nobleman who liked to gamble, asked the mathematician Blaise Pascal the following question: “How do you divide the stakes of an unfinished game of chance when one of the players is ahead?”

(Photo by Rossapicci, via Wikimedia Commons)

Pascal was a child prodigy and a brilliant mathematician. To help answer de Méré’s question, Pascal turned to Pierre de Fermat, a lawyer who was also a brilliant mathematician. Hagstrom reports that Pascal and Fermat exchanged a series of letters which are the foundation of what is now calledprobability theory.

There are two broad categories of probabilities:

• frequency probability
• subjective probability

A frequency probability typically refers to a system that can generate a great deal of statistical data over time. Examples include coin flips, roulette wheels, cards, and dice, notes Hagstrom. For instance, if you flip a coin 1,000 times, you expect to get heads about 50 percent of the time. If you roll one 6-sided dice 1,000 times, you expect to get each number about 16.67 percent of the time.

If you don’t have a sufficient frequency of events, plus a long time period to analyze results, then you must rely on a subjective probability. A subjective probability, says Hagstrom, is often a reasonable assessment made by a knowledgeable person. It’s a best guess based a logical analysis of the given data.

When using a subjective probability, obviously you want to make sure you have all the available data that could be relevant. And clearly you have to use logic correctly.

But the key to using a subjective probability is to update your beliefs as you gain new data. The proper way to update your beliefs is by using Bayes’ Rule.

(Thomas Bayes, via Wikimedia Commons)

Bayes’ Rule

Eliezer Yudkowsky of the Machine Intelligence Research Institute provides an excellent intuitive explanation of Bayes’ Rule:http://www.yudkowsky.net/rational/bayes

Yudkowsky begins by discussing a situation that doctors often encounter:

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

Most doctors estimate the probability between 70% and 80%, which is wildly incorrect.

In order to arrive at the correct answer, Yudkowsky asks us to think of the question as follows. We know that 1% of women at age forty who participate in routine screening have breast cancer. So consider 10,000 women who participate in routine screening:

• Group 1: 100 womenwithbreast cancer.
• Group 2: 9,900 womenwithoutbreast cancer.

After the mammography, the women can be divided into four groups:

• Group A: 80 womenwith breast cancer, and apositive mammography.
• Group B: 20 womenwith breast cancer, and anegative mammography.
• Group C: 950 womenwithout breast cancer, and apositivemammography.
• Group D: 8,950 womenwithoutbreast cancer, and anegative mammography.

So the question again: If a woman out of this group of 10,000 women has a positive mammography, what is the probability that she actually has breast cancer?

The total number of women who hadpositive mammographies is 80 + 950 = 1,030. Of that total, 80 women hadpositive mammographies AND have breast cancer. In looking at the total number of positive mammographies (1,030), we know that 80 of them actually have breast cancer.

So if a woman out of the 10,000 has a positive mammography, the chances that she actually has breast cancer = 80/1030 or 0.07767 or 7.8%.

That’s the intuitive explanation. Now let’s look atBayes’Rule:

P(A|B) = [P(B|A)P(A)] /P(B)

Let’s apply Bayes’ Rule to the same question above:

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

P(A|B) = the probability that the woman has breast cancer (A), given a positive mammography (B)

Here is what we know:

P(B|A) = 80% – the probability of a positive mammography (B), given that the woman has breast cancer (A)

P(A) = 1% – the probability that a woman out of the 10,000 screened actually has breast cancer

P(B) = (80+950) / 10,000 = 10.3% – the probability that a woman out of the 10,000 screened has a positive mammography

Bayes’ Rule again:

P(A|B) = [P(B|A)P(A)] /P(B)

P(A|B) = [0.80*0.01] / 0.103 = 0.008 / 0.103 = 0.07767 or 7.8%

Derivation of Bayes’ Rule:

Bayesians consider conditional probabilities as more basic than joint probabilities. You can define P(A|B) without reference to the joint probability P(A,B). To see this, first start with the conditional probability formula:

P(A|B) P(B) = P(A,B)

but by symmetry youget:

P(B|A) P(A) = P(A,B)

It follows that:

P(A|B) = [P(B|A)P(A)] /P(B)

which isBayes’ Rule.

In conclusion, Hagstrom makes the important observation that there is much we still don’t know about nature and about ourselves. (The question mark below is by Khaydock, via Wikimedia Commons.)

Nothing is absolutely certain.

One clear lesson from history–whether the history of investing, the history of science, or some other area–is that very often people who are “absolutely certain” about something turn out to be wrong.

Economist and Nobel laureate Kenneth Arrow:

• Our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness. Vast ills have followed a belief in certainty.

Investor and author Peter Bernstein:

The recognition of risk management as a practical art rests on a simple cliché with the most profound consequences: when our world was created, nobody remembered to include certainty. We are never certain; we are always ignorant to some degree. Much of the information we have is either incorrect or incomplete.

 

DECISION MAKING

Take a few minutes and try answering these three problems:

• A bat and a ball cost $1.10. The bat costs one dollar more than the ball. How much does the ball cost?
• If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
• In a lake, there is a patch of lily pads. Every day the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long will it take for the patch to cover half the lake?

Roughly 75 percent of the Princeton and Harvard students got at least one problem wrong. These questions form the Cognitive Reflection Test, invented by Shane Frederick, an assistant professor of management science at MIT.

Recall that System 1 (intuition) is quick, associative, and operates automatically all the time. System 2 (reason) is slow and effortful–it requires conscious activation and sustained focus–and it can learn to solve problems involving math, statistics, or logic.

To understand the mental mistake that many people–including smart people–make, let’s consider the first of the three questions:

• A bat and a ball cost $1.10. The bat costs one dollar more than the ball. How much does the ball cost?

After we read the question, our System 1 (intuition) immediately suggests to us that the bat costs $1.00 and the ball costs 10 cents. But if we slow down just a moment and engage System 2, we realize that if the bat costs $1.00 and the ball costs 10 cents, then the bat costs only 90 cents more than the ball. This violates the condition stated in the problem that the bat costs one dollar more than the ball. If we think a bit more, we see that the bat must cost $1.05 and the ball must cost 5 cents.

System 1 takes mental shortcuts, which often work fine. But when we encounter a problem that requires math, statistics, or logic, we have to train ourselves to slow down and to think through the problem. If we don’t slow down in these situations, we’ll often jump to the wrong conclusion.

(Cognitive Bias Codex, by John Manoogian III, via Wikimedia Commons. For a closer look, try this link:https://upload.wikimedia.org/wikipedia/commons/1/18/Cognitive_Bias_Codex_-_180%2B_biases%2C_designed_by_John_Manoogian_III_%28jm3%29.jpg)

It’s possible to train your intuition under certain conditions, according to Daniel Kahneman. Hagstrom:

Kahneman believes there are indeed cases where intuitive skill reveals the answer, but that such cases are dependent on two conditions. First, “the environment must be sufficiently regular to be predictable” second, there must be an “opportunity to learn these regularities through prolonged practice.” For familiar examples, think about the games of chess, bridge, and poker. They all occur in regular environments, and prolonged practice at them helps people develop intuitive skill. Kahneman also accepts the idea that army officers, firefighters, physicians, and nurses can develop skilled intuition largely because they all have had extensive experience in situations that, while obviously dramatic, have been repeated many times over.

Kahneman concludes that intuitive skill exists mostly in people who operate in simple, predictable environments and that people in more complex environments are much less likely to develop this skill. Kahneman, who has spent much of his career studying clinicians, stock pickers, and economists, notes that evidence of intuitive skill is largely absent in this group. Put differently, intuition appears to work well in linear systems where cause and effect is easy to identify. But in nonlinear systems, including stock markets and economies, System 1 thinking, the intuitive side of our brain, is much less effectual.

Experts in fields such as investing, economics, and politics have, in general, not demonstrated the ability to make accurate forecasts or predictions with any reliable consistency.

Philip Tetlock, professor of psychology at the University of Pennsylvania, tracked 284 experts over fifteen years (1988-2003) as they made 27,450 forecasts. The results are no better than “dart-throwing chimpanzees,” as Tetlock describes inExpert Political Judgment: How Good Is It? How Can We Know? (Princeton University Press, 2005).

Hagstrom explains:

It appears experts are penalized, like the rest of us, by thinking deficiencies. Specifically, experts suffer from overconfidence, hindsight bias, belief system defenses, and lack of Bayesian process.

Hagstrom then refers to an essay by Sir Isaiah Berlin, “The Hedgehog and the Fox: An Essay on Tolstoy’s View of History.” Hedgehogs view the world using one large idea, while Foxes are skeptical of grand theories and instead consider a variety of information and experiences before making decisions.

(Photo of Hedgehog, by Nevit Dilmen, via Wikimedia Commons)

Tetlock found that Foxes, on the whole, were much more accurate than Hedgehogs. Hagstrom:

Why are hedgehogs penalized? First, because they have a tendency to fall in love with pet theories, which gives them too much confidence in forecasting events. More troubling, hedgehogs were too slow to change their viewpoint in response to discomfirming evidence. In his study, Tetlock said Foxes moved 59 percent of the prescribed amount toward alternate hypotheses, while Hedgehogs moved only 19 percent. In other words, Foxes were much better at updating their Bayesian inferences than Hedgehogs.

Unlike Hedgehogs, Foxes appreciate the limits of their knowledge. They have better calibration and discrimination scores than Hedgehogs. (Calibration, which can be thought of as intellectual humility, measures how much your subjective probabilities correspond to objective probabilities. Discrimination, sometimes called justified decisiveness, measures whether you assign higher probabilities to things that occur than to things that do not.)

(Photo of Fox, by Alan D. Wilson, via Wikimedia Commons)

Hagstrom comments thatFoxes have three distinct cognitive advantages, according to Tetlock:

• They begin with “reasonable starter” probability estimates. They have better “inertial-guidance” systems that keep their initial guesses closer to short-term base rates.
• They are willing to acknowledge their mistakes and update their views in response to new information. They have a healthy Bayesian process.
• They can see the pull of contradictory forces, and, most importantly, they can appreciate relevant analogies.

Hagstrom concludes that the Fox “is the perfect mascot for The College of Liberal Arts Investing.”

Many people with high IQ have difficulty making rational decisions. Keith Stanovich, professor of applied psychology at the University of Toronto, invented the term dysrationalia to refer to the inability to think and behave rationally despite high intelligence, remarks Hagstrom. There are two principal causes of dysrationalia:

• a processing problem
• a content problem

Stanovich explains that people are lazy thinkers in general, preferring to think in ways that require less effort, even if those methods are less accurate. As we’ve seen, System 1 operates automatically, with little or no effort. Its conclusions are often correct. But when the situation calls for careful reasoning, the shortcuts of System 1 don’t work.

Lack of adequate content is amindware gap, says Hagstrom.Mindware refers to rules, strategies, procedures, and knowledge that people possess to help solve problems. Harvard cognitive scientist David Perkins coined the termmindware. Hagstrom quotes Perkins:

What is missing is the metacurriculum–the ‘higher order’ curriculum that deals with good patterns of thinking in general and across subject matters.

Perkins’ proposed solution is amindware booster shot, which means teaching new concepts and ideas in “a deep and far-reaching way,” connected with several disciplines.

Of course, Hagstrom’s book,Investing: The Last Liberal Art, is a great example of amindware booster shot.

 

Hagstrom concludes by stressing the vital importance of lifelong, continuous learning. Buffett and Munger have always highlighted this as a key to their success.

(Statue of Ben Franklin in front of College Hall, Philadelphia, Pennsylvania, Photo by Matthew Marcucci, via Wikimedia Commons)

Hagstrom:

Although the greatest number of ants in a colony will follow the most intense pheromone trail to a food source, there are always some ants that are randomly seeking the next food source. When Native Americans were sent out to hunt, most of those in the party would return to the proven hunting grounds. However, a few hunters, directed by a medicine man rolling spirit bones, were sent in different directions to find new herds. The same was true of Norwegian fishermen. Each day most of the ships in the fleet returned to the same spot where the previous day’s catch had yielded the greatest bounty, but a few vessels were also sent in random directions to locate the next school of fish. As investors, we too must strike a balance between exploiting what is most obvious while allocating some mental energy to exploring new possibilities.

Hagstrom adds:

The process is similar to genetic crossover that occurs in biological evolution. Indeed, biologists agree that genetic crossover is chiefly responsible for evolution. Similarly, the constant recombination of our existing mental building blocks will, over time, be responsible for the greatest amount of investment progress. However, there are occasions when a new and rare discovery opens up new opportunities for investors. In much the same way that mutation can accelerate the evolutionary process, so too can new ideas speed us along in our understanding of how markets work. If you are able to discover a new building block, you have the potential to add another level to your model of understanding.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

February 20, 2022

Erik Brynjolfsson and Andrew McAfee are the authors ofThe Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies(Norton, 2014). It’s one of the best books I’ve read in the past few years.

The second machine age is going to bring enormous progress to economies and societies. Total wealth – whether defined narrowly or much more broadly – will increase significantly. But we do have to ensure that political and social structures are properly adjusted so that everyone can benefit from massive technological progress.

The first six chapters – starting with The Big Stories, and going thru Artificial and Human Intelligence in the Second Machine Age – give many examples of recent technological progress.

The five subsequent chapters – four of which are covered here – discuss the bounty and the spread.

Bounty is the increase in volume, variety, and quality and the decrease in cost of the many offerings brought on by modern technological progress. It’s the best economic news in the world today.

Spread is differences among people in economic success.

The last four chapters discuss what interventions could help maximize the bounty while mitigating the effects of the spread.

Here are the chapters covered:

• The Big Stories
• The Skills of the New Machines: Technology Races Ahead
• Moore’s Law and the Second Half of the Chessboard
• The Digitization of Just About Everything
• Innovation: Declining or Recombining?
• Artificial and Human Intelligence in the Second Machine Age
• Computing Bounty
• Beyond GDP
• The Spread
• Implications of the Bounty and the Spread
• Learning to RaceWith Machines: Recommendations for Individuals
• Policy Recommendations
• Long-term Recommendations
• Technology and the Future

 

THE BIG STORIES

Freeman Dyson:

Technology is a gift of God. After the gift of life, it is perhaps the greatest of God’s gifts. It is the mother of civilizations, of arts and of sciences.

James Watt’s brilliant tinkering with the steam engine in 1775 and 1776 was central to the Industrial Revolution:

The Industrial Revolution, of course, is not only the story of steam power, but steam started it all. More than anything else, it allowed us to overcome the limitations of muscle power, human and animal, and generate massive amounts of useful energy at will. This led to factories and mass production, to railways and mass transportation. It led, in other words, to modern life. The Industrial Revolution ushered in humanity’s first machine age – the first time our progress was driven primarily by technological innovation – and it was the most profound time of transformation the world has ever seen. (pages 6-7)

(Note that Brynjolfsson and McAfee refer to the Industrial Revolution as “the first machine age.” And they refer to the late nineteenth and early twentieth century as the Second Industrial Revolution.)

Brynjolfsson and McAfee continue:

Now comes the second machine age. Computers and other digital advances are doing for mental power – the ability to use our brains to understand and shape our environments – what the steam engine and its descendants did for muscle power. They’re allowing us to blow past previous limitations and taking us into new territory. How exactly this transition will play out remains unknown, but whether or not the new machine age bends the curve as Watt’s steam engine, it is a very big deal indeed. This book explains how and why.

For now, a very short and simple answer: mental power is at least as important for progress and development – for mastering our physical and intellectual environment to get things done – as physical power. So a vast and unprecedented boost to mental power should be a great boost to humanity, just as the earlier boost to physical power so clearly was. (pages 7-8)

Brynjolfsson and McAfee admit that recent technological advances surpassed their expectations:

We wrote this book because we got confused. For years we have studied the impact of digital technologies like computers, software, and communications networks, and we thought we had a decent understanding of their capabilities and limitations. But over the past few years they started surprising us. Computers started diagnosing diseases, listening and speaking to us, and writing high-quality prose, while robots started scurrying around warehouses and driving cars with minimal or no guidance. Digital technologies had been laughably bad at a lot of these things for a long time – then they suddenly got very good. How did this happen? And what were the implications of this progress, which was astonishing and yet came to be considered a matter of course?

Brynjolfsson and McAfee did a great deal of reading. But they learned the most by speaking with inventors, investors, entrepreneurs, engineers, scientists, and others making or using technology.

Brynjolfsson and McAfee report that they reached three broad conclusions:

The first is that we’re living at a time of astonishing progress with digital technologies – those that have computer hardware, software, and networks at their core. These technologies are not brand-new; businesses have been buying computers for more than half a century… But just as it took generations to improve the steam engine to the point that it could power the Industrial Revolution, it’s also taken time to refine our digital engines.

We’ll show why and how the full force of these technologies has recently been achieved and give examples of its power. ‘Full,’ though, doesn’t mean ‘mature.’ Computers are going to continue to improve and do new and unprecedented things. By ‘full force,’ we mean simply that the key building blocks are already in place for digital technologies to be as important and transformational for society and the economy as the steam engine. In short, we’re at an inflection point – a point where the curve starts to bend a lot – because of computers. We are entering a second machine age.

Our second conclusion is that the transformations brought about by digital technology will be profoundly beneficial ones. We’re heading into an era that won’t just be different; it will be better, because we’ll be able to increase both the variety and the volume of our consumption… we don’t just consume calories and gasoline. We also consume information from books and friends, entertainment from superstars and amateurs, expertise from teachers and doctors, and countless other things that are not made of atoms. Technology can bring us more choice and even freedom.

When these things are digitized – when they’re converted into bits that can be stored on a computer and sent over a network – they acquire some weird and wonderful properties. They’re subject to different economics, where abundance is the norm rather than scarcity. As we’ll show, digital goods are not like physical ones, and these differences matter.

…Digitization is improving the physical world, and these improvements are only going to become more important. Among economic historians, there’s wide agreement that, as Martin Weitzman puts it, ‘the long-term growth of an advanced economy is dominated by the behavior of technical progress.’ As we’ll show, technical progress is improving exponentially.

Our third conclusion is less optimistic: digitization is going to bring with it some thorny challenges… Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. As we’ll demonstrate, there’s never been a better time to be a worker with special skills or the right education, because these people can use technology to create and capture value. However, there’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate. (pages 9-11)

 

THE SKILLS OF THE NEW MACHINES: TECHNOLOGY RACES AHEAD

Arthur C. Clarke:

Any sufficiently advanced technology is indistinguishable from magic.

Computers are symbol processors, noteBrynjolfsson and McAfee: Their circuitry can be interpreted in the language of ones and zeroes, or as true or false, or as yes or no.

Computers are especially good at following rules or algorithms. So computers are especially well-suited for arithmetic, logic, and similar tasks.

Historically, computers have not been very good at pattern recognition compared to humans. Brynjolfsson and McAfee:

Our brains are extraordinarily good at taking in information via our senses and examining it for patterns, but we’re quite bad at describing or figuring out how we’re doing it, especially when a large volume of fast-changing information arrives at a rapid pace. As the philosopher Michael Polanyi famously observed, ‘We know more than we can tell.’ (page 18)

Driving a car is an example where humans’ ability to recognize patterns in a mass of sense data was thought to be beyond a computer’s ability. DARPA – the Defense Advanced Research Projects Agency – held a Grand Challenge for driverless cars in 2004. It was a 150-mile course through the Mojave Desert in California. There were fifteen entrants. Brynjolfsson and McAfee:

The results were less than encouraging. Two vehicles didn’t make it to the starting area, one flipped over in the starting area, and three hours into the race, only four cars were still operational. The ‘winning’ Sandstorm car from Carnegie Mellon University covered 7.4 miles (less than 5 percent of the total) before veering off the course during a hairpin turn and getting stuck on an embankment. The contest’s $1 million prize went unclaimed, and Popular Science called the event ‘DARPA’s Debacle in the Desert.’ (page 19)

Within a few years, however, driverless cars became far better. By 2012, Google driverless cars had covered hundreds of thousands of miles with only two accidents (both caused by humans). Brynjolfsson and McAfee:

Progress on some of the oldest and toughest challenges associated with computers, robots, and other digital gear was gradual for a long time. Then in the past few years it became sudden; digital gear started racing ahead, accomplishing tasks it had always been lousy at and displaying skills it was not supposed to acquire any time soon. (page 20)

Another example of an area where it was thought fairly recently that computers wouldn’t become very good is complex communication. But starting around 2011, computers suddenly seemed to get much better at using human languages to communicate with humans. Robust natural language processing has become available to people with smartphones.

For instance, there are mobile apps that show you an accurate map and that tell you the fastest way for getting somewhere. Also, Google’s translations on twitter have gotten much better recently (as of mid-2017).

In 2011, IBM’s supercomputer Watson beat Ken Jennings and Brad Rutter atJeopardy! This represented another big advance in which a computer combined pattern matching with complex communication. The game involves puns, rhymes, wordplay, and more.

Jennings had won a record seventy-four times in a row in 2004. Rutter beat Jennings in the 2005 Ultimate Tournament of Champions. The early versions of IBM’s Watson came nowhere close to winningJeopardy! But when Watson won in 2011, it had three times as much money as either human opponent. Jennings later remarked:

Just as factory jobs were eliminated in the twentieth century by new assembly-line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. (page 27)

Robotics is another area where progress had been gradual, but recently became sudden, observeBrynjolfsson and McAfee. Robot entered the English language via a 1921 Czech play, R.U.R. (Rossum’s “Universal” Robots), by Karel Capek. Isaac Asimov coined the term robotics in 1941.

Robots have still lagged in perception and mobility, while excelling in many computational tasks. This dichotomy is known as Moravec’s paradox, described on Wikipedia as:

the discovery by artificial intelligence and robotics researchers that, contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources. (pages 28-29)

Brynjolfsson and McAfee write that, at least until recently, most robots in factories could only handle items that showed up in exactly the same location and configuration each time. To perform a different task, these robots would need to be reprogrammed.

In 2008, Rodney Brooks founded Rethink Robotics. Brooks would like to create robots that won’t need to be programmed by engineers. These new robots could be taught to do a task, or re-taught a new one, by shop floor workers. At the company’s headquarters in Boston,Brynjolfsson and McAfee got a sneak peak at a new robot – Baxter. It has two arms with claw-like grips. The head is an LCD face. It has wheels instead of legs. Each arm can be manually trained to do a wide variety of tasks.

Brynjolfsson and McAfee:

Kiva, another young Boston-area company, has taught its automatons to move around warehouses safely, quickly, and effectively. Kiva robots look like metal ottomans or squashed R2-D2’s. They scuttle around buildings at about knee-height, staying out of the way of humans and one another. They’re low to the ground so they can scoot underneath shelving units, lift them up, and bring them to human workers. After these workers grab the products they need, the robot whisks the shelf away and another shelf-bearing robot takes its place. Software tracks where all the products, shelves, robots, and people are in the warehouse, and orchestrates the continuous dance of the Kiva automatons. In March of 2012, Kiva was acquired by Amazon – a leader in advanced warehouse logistics – for more than $750 million in cash. (page 32)

Boston Dynamics, another New England startup, has built dog-like robots to support troops in the field.

A final example is the Double, which is essentially an iPad on wheels. It allows the operator to see and hear what the robot does.

Brynjolfsson and McAfee present more evidence of technological progress:

On the Star Trek television series, devices called tricorders were used to scan and record three kinds of data: geological, meteorological, and medical. Today’s consumer smartphones serve all these purposes; they can be put to work as seismographs, real-time weather radar maps, and heart- and breathing-rate monitors. And, of course, they’re not limited to these domains. They also work as media players, game platforms, reference works, cameras, and GPS devices. (page 34)

Recently, the company Narrative Science was contracted with by Forbes.com in order to write earnings previews that are indistinguishable from human writing.

Brynjolfsson and McAfee conclude:

Most of the innovations described in this chapter have occurred in just the past few years. They’ve taken place where improvement had been frustratingly slow for a long time, and where the best thinking often led to the conclusion that it wouldn’t speed up. But then digital progress became sudden after being gradual for so long. This happened in multiple areas, from artificial intelligence to self-driving cars to robotics.

How did this happen? Was it a fluke – a confluence of a number of lucky, one-time advances? No, it was not. The digital progress we’ve seen recently certainly is impressive, but it’s just a small indication of what’s to come. It’s the dawn of the second machine age. To understand why it’s unfolding now, we need to understand the nature of technological progress in the era of digital hardware, software, and networks. In particular, we need to understand its three characteristics: that it is exponential, digital, and combinatorial. The next three chapters will discuss each of these in turn. (page 37)

 

MOORE’S LAW AND THE SECOND HALF OF THE CHESSBOARD

Moore’s Law roughly says that computing power per dollar doubles about every eighteen months. It’s not a law of nature, noteBrynjolfsson and McAfee, but a statement about the continued productivity of the computer industry’s engineers and scientists. Moore first made his prediction in 1965. He thought it would only last for ten years. But it’s now lasted almost fifty years.

Brynjolfsson and McAfee point out that this kind of sustained progress hasn’t happened in any other field. It’s quite remarkable.

Inventor and futurist Ray Kurzweil has told the story of the inventor and the emperor. In the 6th century in India, a clever man invented the game of chess. The man went to the capital city, Pataliputra, to present his invention to the emperor. The emperor was so impressed that he asked the man to name his reward.

The inventor praised the emperor’s generosity and said, “All I desire is some rice to feed my family.” The inventor then suggested they use the chessboard to determine the amount of rice he would receive. He said to place one grain of rice on the first square, two grains on the second square, four grains on the third square, and so forth. The emperor readily agreed.

If you were to actually do this, you would end up with more than eighteen quintillion grains of rice. Brynjolfsson and McAfee:

A pile of rice this big would dwarf Mount Everest; it’s more rice than has been produced in the history of the world. (pages 45-46)

Kurzweil points out that when you get to the thirty-second square – which is the first half of the chessboard – you have about 4 billion grains of rice, or one large field’s worth. But when you get to the second half of the chessboard, the result of sustained exponential growth becomes clear.

Brynjolfsson and McAfee:

Our quick doubling calculation also helps us understand why progress with digital technologies feels so much faster these days and why we’ve seen so many recent examples of science fiction becoming business reality. It’s because the steady and rapid exponential growth of Moore’s Law has added up to the point that we’re now in a different regime of computing: we’re now in the second half of the chessboard. The innovations we described in the previous chapter – cars that drive themselves in traffic; Jeopardy!-champion supercomputers; auto-generated news stories; cheap, flexible factory robots; and inexpensive consumer devices that are simultaneously communicators, tricorders, and computers – have all appeared since 2006, as have countless other marvels that seem quite different from what came before.

One of the reasons they’re all appearing now is that the digital gear at their hearts is finally both fast and cheap enough to enable them. This wasn’t the case just a decade ago. (pages 47-48)

Brynjolfsson and McAfee later add:

It’s clear that many of the building blocks of computing – microchip density, processing speed, storage capacity, energy efficiency, download speed, and so on – have been improving at exponential rates for a long time. (page 49)

For example, in 1996, the ASCI Red supercomputer cost $55 million to develop and took up 1,600 square feet of floor space. It was the first computer to score above one teraflop – one trillion floating point operations per second – on the standard test for computer speed, note Brynjolfsson and McAfee. It used eight hundred kilowatts per hour, roughly as much as eight hundred homes. By 1997, it reached 1.8 teraflops.

Nine years later, the Sony PlayStation 3 hit 1.8 teraflops. It cost five hundred dollars, took up less than a tenth of a square meter, and used about two hundred watts, observe Brynjolfsson and McAfee.

Exponential progress has made possible many of the advances discussed in the previous chapter. IBM’s Watson draws on a plethora of clever algorithms, but it would be uncompetitive without computer hardware about one hundred times more powerful than Deep Blue, its chess-playing predecessor that beat the human world champion, Garry Kasparov, in a 1997 match. (page 50)

SLAM

Researchers in artificial intelligence have long been interested in SLAM – simultaneous localization and mapping. As of 2008, computers weren’t able to do this well for large areas.

In November 2010, Microsoft offered Kinect – a $150 accessory – as an addition to its Xbox gaming platform.

The Kinect could keep track of two active players, monitoring as many as twenty joints on each. If one player moved in front of the other, the device made a best guess about the obscured person’s movements, then seamlessly picked up all joints once he or she came back into view. Kinect could also recognize players’ faces, voices, and gestures and do so across a wide range of lighting and noise conditions. It accomplished this with digital sensors including a microphone array (which pinpointed the source of sound better than a single microphone could), a standard video camera, and a depth perception system that both projected and detected infrared light. Several onboard processors and a great deal of proprietary software converted the output of these sensors into information that game designers could use. (page 53)

After its release, Kinect sold more than eight million units in sixty days, which makes it the fastest-selling consumer electronics device of all time. But the Kinect system could do far more than its video game applications. In August 2011, at the SIGGRAPH (the Association of Computing Machinery’s Special Interest Group on Graphics and Interactive Techniques) in Vancouver, British Columbia, a Microsoft team introduced KinestFusion as a solution to SLAM.

In a video shown at SIGGRAPH 2011, a person picks up a Kinect and points it around a typical office containing chairs, a potted plant, and a desktop computer and monitor. As he does, the video splits into multiple screens that show what the Kinect is able to sense. It immediately becomes clear that if the Kinect is not completely solving the SLAM for the room, it’s coming close. In real time, Kinect draws a three-dimensional map of the room and all the objects in it, including a coworker. It picks up the word DELL pressed into the plastic on the back of the computer monitor, even though the letters are not colored and only one millimeter deeper than the rest of the monitor’s surface. The device knows where it is in the room at all times, and even knows how virtual ping-pong balls would bounce around if they were dropped into the scene. (page 54)

Microsoft made available (in June 2011) a Kinect software development kit. Less than a year later, a team led by John Leonard of MIT’s Computer Science and Artificial Intelligence Lab announced Kintinuous, a ‘spatially extended’ version of KinectFusion. Users could scan large indoor volumes and even outdoor environments.

Another fascinating example of powerful digital sensors:

A Google autonomous car incorporates several sensing technologies, but its most important ‘eye’ is a Cyclopean LIDAR (a combination of ‘LIght’ and ‘raDAR’) assembly mounted on the roof. This rig, manufactured by Velodyne, contains sixty-four separate laser beams and an equal number of detectors, all mounted in a housing that rotates ten times a second. It generates about 1.3 million data points per second, which can be assembled by onboard computers into a real-time 3D picture extending one hundred meters in all directions. Some early commercial LIDAR systems around the year 2000 cost up to $35 million, but in mid-2013 Velodyne’s assembly for self-navigating vehicles was priced at approximately $80,000, a figure that will fall much further in the future. David Hall, the company’s founder and CEO, estimates that mass production would allow his product’s price to ‘drop to the level of a camera, a few hundred dollars.’ (page 55)

 

THE DIGITIZATION OF JUST ABOUT EVERYTHING

As of March 2017, Android users could choose from 2.8 million applications while Apple users could choose from 2.2 million. One example of a free but powerful app – a version of which is available from several companies – is one that gives you a map plus driving directions. The app tells you the shortest route available now.

Digitization is turning all kinds of information and bits into the language of computers – ones and zeroes. What’s crucial about digital information is that it’s non-rival and it has close to zero marginal cost of reproduction. In other words, it can be used over and over – it doesn’t get ‘used up’ – and it’s extremely cheap to make another copy. (Rival goods, by contrast, can only be used by one person at a time.)

In 1991, at the Nineteenth General Conference on Weights and Measures, the set of prefixes was expanded to include ayotta, representing one septillion, or 10^24. As of 2012, there were 2.7 zettabytes of digital data – or 2.7 sextillion bytes. This is only one prefix away from ayotta.

The explosion of digital information, while obviously not always useful, can often lead to scientific advances – i.e., understanding and predicting phenomena more accurately or more simply. Some search terms have been found to have predictive value. Same with some tweets. Culturonomics makes use of digital information – like scanned copies of millions of books written over the centuries – to study human culture.

 

INNOVATION: DECLINING OR RECOMBINING?

Linus Pauling:

If you want to have good ideas, you must have many ideas.

Innovation is the essential long-term driver of progress. As Paul Krugman said:

Productivity isn’t everything, but in the long run it is almost everything. (page 72)

Improving the standard of living over time depends almost entirely on raising output per worker, Krugman explained. Brynjolfsson and McAfee write that most economists agree with Joseph Schumpeter’s observation:

Innovation is the outstanding fact in the economic history of capitalist society… and also it is largely responsible for most of what we would at first sight attribute to other factors. (page 73)

The original Industrial Revolution resulted in large part from the steam engine. The Second Industrial Revolution depended largely on three innovations: electricity, the internal combustion engine, and indoor plumbing with running water.

Economist Robert Gordon, a widely respected researcher on productivity and economic growth, has concluded that by 1970, economic growth stalled out. The three main inventions of the Second Industrial Revolution had their effect from 1870 to 1970. But there haven’t been economically significant innovations since 1970, according to Gordon. Some other economists, such as Tyler Cowen, agree with Gordon’s basic view.

The most economically important innovations are calledgeneral purposes technologies (GPTs). GPTs, according to Gavin Wright, are:

deep new ideas or techniques that have the potential for important impacts on many sectors of the economy. (page 76)

‘Impacts,’ note Brynjolfsson and McAfee, mean significant boosts in output due to large productivity gains. They noticeably accelerate economic progress. GPTs, economists have concurred, should be pervasive, improving over time, and should lead to new innovations.

Isn’t information and communication technology (ICT) a GPT? Most economic historians think so. Economist Alexander Field compiled a list of candidates for GPTs, and ICT was tied with electricity as the second most common GPT. Only the steam engine was ahead of ICT.

Not everyone agrees. Cowen argues basically that ICT is coming up short on the revenue side of things.

The ‘innovation-as-fruit’ view, say Brynjolfsson and McAfee, is that there are discrete inventions followed by incremental improvements, but those improvements stop being significant after a certain point. The original inventions have been used up.

Another way to look at innovation, however, is not coming up with something big and new, but recombining things that already exist. Complexity scholar Brian Arthur holds this view of innovation. So does economist Paul Romer, who has written that we, as humans, nearly always underestimate how many new ideas have yet to be discovered.

The history of physics may serve as a good illustration of Romer’s point. At many different points in the history of physics, at least some leading physicists have asserted that physics was basically complete. This has always been dramatically wrong. As of 2017, is physics closer to 10% complete or 90% complete? Of course, no one knows for sure. But how much more will be discovered and invented if we have AI with IQ 1,000,000+ being handled by genetically engineered scientists? In my view, physics is probably closer to 10% complete.

Brynjolfsson and McAfee point out that ICT leads to recombinant innovation, perhaps like nothing else has.

…digital innovation is recombinant innovation in its purest form. Each development becomes a building block for future innovations. Progress doesn’t run out; it accumulates… Moore’s Law makes computing devices and sensors exponentially cheaper over time, enabling them to be built economically into more and more gear, from doorknobs to greeting cards. Digitization makes available massive bodies of data relevant to almost any situation, and this information can be infinitely reproduced and reused because it is non-rival. As a result of these two forces, the number of potentially valuable building blocks is exploding around the world, and the possibilities are multiplying as never before. We’ll call this the ‘innovation-as-building-block’ view of the world; it’s the one held by Arthur, Romer, and the two of us. From this perspective, unlike the innovation-as-fruit view, building blocks don’t ever get eaten or otherwise used up. In fact, they increase the opportunities for future recombinations.

…In his paper, ‘Recombinant Growth,’ the economist Martin Weitzman developed a mathematical model of new growth theory in which the ‘fixed factors’ in an economy – machine tools, trucks, laboratories, and so on – are augmented over time by pieces of knowledge that he calls ‘seed ideas,’ and knowledge itself increases over time as previous seed ideas are recombined into new ones. (pages 81-82)

As the number of seed ideas increases, the combinatorial possibilities explode quickly. Weitzman:

In the early stages of development, growth is constrained by number of potential new ideas, but later on it is constrained only by the ability to process them.

ICT connects nearly everyone, and computing power continues to follow Moore’s Law. Brynjolfsson and McAfee:

We’re interlinked by global ICT, and we have affordable access to masses of data and vast computing power. Today’s digital environment, in short, is a playground for large-scale recombination. (page 83)

…

…The innovation scholars Lars Bo Jeppesen and Karim Lakhani studied 166 scientific problems posted to Innocentive, all of which had stumped their home organizations. They found that the crowd assembled around Innocentive was able to solve forty-nine of them, for a success rate of nearly 30 percent. They also found that people whose expertise was far away from the apparent domain of the problem were more likely to submit winning solutions. In other words, it seemed to actually help a solver to be ‘marginal’ – to have education, training, and experience that were not obviously relevant for the problem. (page 84)

Kaggle is similar to Innocentive, but Kaggle is focused on data-intensive problems with the goal being to improve the baseline prediction. The majority of Kaggle contests, says Brynjolfsson and McAfee, are won by people who are marginal to the domain of the challenge. In one problem involving artificial intelligence – computer grading of essays – none of the top three finishers had any formal training in artificial intelligence beyond a free online course offered by Stanford AI faculty, open to anyone in the world.

 

ARTIFICIAL AND HUMAN INTELLIGENCE IN THE SECOND MACHINE AGE

Previous chapters discussed three forces – sustained exponential improvement in most aspects of computing, massive amounts of digitized information, and recombinant invention – that are yielding significant innovations. But, state Brynjolfsson and McAfee, when you consider also that most people on the planet are connected via the internet and that useful artificial intelligence (AI) is emerging, you have to be even more optimistic about future innovations.

Digital technologies will restore hearing to the deaf via cochlear implants. Digital technologies will likely restore sight to the fully blind, perhaps by retinal implants. That’s just the beginning, to say nothing of advances in biosciences. Dr. Watson will become the best diagnostician in the world. Another supercomputer will become the best surgeon in the world.

Brynjolfsson and McAfee summarize:

The second machine age will be characterized by countless instances of machine intelligence and billions of interconnected brains working together to better understand and improve our world. (page 96)

 

COMPUTING BOUNTY

Milton Friedman:

Most economic fallacies derive from the tendency to assume that there is a fixed pie, that one party can gain only at the expense of another.

Productivity growth comes from technological innovation and from improvements in production techniques. The 1940s, 1950s, and 1960s were a time of rapid productivity growth. The technologies of the first machine age, such as electricity and the internal combustion engine, were largely responsible.

But in 1973, things slowed down. What’s interesting is that computers were becoming available during this decade. But like the chief innovations of the first machine age, it would take a few decades before computing would begin to impact productivity growth significantly.

The internet started impacting productivity within a decade after its invention in 1989. And even more importantly, enterprise-wide IT systems boosted productivity in the 1990s. Firms that used IT throughout the 1990s were noticeably more productive as a result.

Brynjolfsson and McAfee:

The first five years of the twenty-first century saw a renewed wave of innovation and investment, this time less focused on computer hardware and more focused on a diversified set of applications and process innovations… In a statistical study of over six hundred firms that Erik did with Lorin Hitt, he found that it takes an average of five to seven years before full productivity benefits of computers are visible in the productivity of the firms making the investments. This reflects the time and energy required to make the other complementary investments that bring a computerization effort success. In fact, for every dollar of investment in computer hardware, companies need to invest up to another nine dollars in software, training, and business process redesign. (pages 104-105)

Brynjolfsson and McAfee conclude:

The explanation for this productivity surge is in the lags that we always see when GPTs are installed. The benefits of electrification stretched for nearly a century as more and more complementary innovations were implemented. The digital GPTs of the second machine age are no less profound. Even if Moore’s Law ground to a halt today, we could expect decades of complementary innovations to unfold and continue to boost productivity. However, unlike the steam engine or electricity, second machine age technologies continue to improve at a remarkably rapid exponential pace, replicating their power with digital perfection and creating even more opportunities for combinatorial innovation. The path won’t be smooth… but the fundamentals are in place for bounty that vastly exceeds anything we’ve ever seen before. (page 106)

 

BEYOND GDP

Brynjolfsson and McAfee note that President Hoover had to rely on data such as freight car loadings, commodity prices, and stock prices in order to try to understand what was happening during the Great Depression.

The first set of national accounts was presented to Congress in 1937 based on the pioneering work of Nobel Prize winner Simon Kuznets, who worked with researchers at the National Bureau of Economic Research and a team at the U.S. Department of Commerce. The resulting set of metrics served as beacons that helped illuminate many of the dramatic changes that transformed the economy throughout the twentieth century.

But as the economy has changed, so, too, must our metrics. More and more what we care about in the second machine age are ideas, not things – mind, not matter; bits, not atoms; and interactions, not transactions. The great irony of this information age is that, in many ways, we know less about the sources of value in the economy than we did fifty years ago. In fact, much of the change has been invisible for a long time simply because we did not know what to look for. There’s a huge layer of the economy unseen in the official data and, for that matter, unaccounted for on the income statements and balance sheets of most companies. Free digital goods, the sharing economy, intangibles and changes in our relationships have already had big effects on our well-being. They also call for new organizational structures, new skills, new institutions, and perhaps even a reassessment of some of our values. (pages 108-109)

Brynjolfsson and McAfee write:

In addition to their vast library of music, children with smartphones today have access to more information in real time via the mobile web than the president of the United States had twenty years ago. Wikipedia alone claims to have over fifty times as much information as Encyclopaedia Britannica, the premier compilation of knowledge for most of the twentieth century. Like Widipedia but unlike Britannica, much of the information and entertainment available today is free, as are over one million apps on smartphones.

Because they have zero price, these services are virtually invisible in the official statistics. They add value to the economy but not dollars to GDP. And because our productivity data are, in turn, based on GDP metrics, the burgeoning availability of free goods does not move the productivity dial. There’s little doubt, however, that they have real value. (pages 110-111)

Free products can push GDP downward. A free online encyclopedia available for pennies instead of thousands of dollars makes you better off, but it lowers GDP, observe Brynjolfsson and McAfree. GDP was a good measure of economic growth throughout most of the twentieth century. Higher levels of production generally led to greater well-being. But that’s no longer true to the same extent due to the proliferation of digital goods that do not have a dollar price.

One way to measure the value of goods that are free or nearly free is to find out how much people would be willing to pay for them. This is known asconsumer surplus, but in practice it’s extremely difficult to measure.

New goods and services have not been fully captured in GDP figures.

For the overall economy, the official GDP numbers miss the value of new goods and services added to the tune of about 0.4 percent of additional growth each year, according to economist Robert Gordon. Remember that productivity growth has been in the neighborhood of 2 percent per year for most of the past century, so contribution of new goods is not a trivial portion. (pages 117-118)

GDP misses the full value of digital goods and services. Similarly, intangible assets are not fully measured.

Just as free goods rather than physical products are an increasingly important share of consumption, intangibles also make up a growing share of the economy’s capital assets. Production in the second machine age depends less on physical equipment and structures and more on the four categories of intangible assets: intellectual property, organizational capital, user-generated content, and human capital. (page 119)

Paul Samuelson and Bill Nordhaus have observed that GDP is one of the great inventions of the twentieth century. But as Brynjolfsson and McAfee indicate, digital innovation means that we also need innovation in our economic metrics.

The new metrics will differ both in conception and execution. We can build on some of the existing surveys and techniques researchers have been using. For instance, the human development index uses health and education statistics to fill in some of the gaps in official GDP statistics; the multidimensional poverty index uses ten different indicators – such as nutrition, sanitation, and access to water – to assess well-being in developing countries. Childhood death rates and other health indicators are recorded in other periodic household surveys like the Demographic and Health Surveys.

There are several promising projects in this area. Joe Stiglitz, Amartya Sen, and Jean-Paul Fitoussi have created a detailed guide for how we can do a comprehensive overhaul of our economic statistics. Another promising project is the Social Progress Index that Michael Porter, Scott Stern, Roberto Lauria, and their colleagues are developing. In Bhutan, they’ve begun measuring ‘Gross National Happiness.’ There is also a long-running poll behind the Gallup-Healthways Well-Being Index.

These are all important improvements, and we heartily support them. But the biggest opportunity is in using the tools of the second machine age itself: the extraordinary volume, variety, and timeliness of data available digitally. The Internet, mobile phones, embedded sensors in equipment, and a plethora of other sources are delivering data continuously. For instance, Roberto Rigobon and Alberto Cavallo measure online prices from around the world on a daily basis to create an inflation index that is far timelier and, in many cases, more reliable, than official data gathered via monthly surveys with much smaller samples. Other economists are using satellite mapping of nighttime artificial light sources to estimate economic growth in different parts of the world, and assessing the frequency of Google searches to understand changes in unemployment and housing. Harnessing this information will produce a quantum leap in our understanding of the economy, just as it has already changed marketing, manufacturing, finance, retailing, and virtually every other aspect of business decision-making.

As more data become available, and the economy continues to change, the ability to ask the right questions will become even more vital. No matter how bright the light is, you won’t find your keys by searching under a lamppost if that’s not where you lost them. We must think hard about what it is we really value, what we want more of, and what we want less of. GDP and productivity growth are important, but they are a means to an end and not ends in and of themselves. Do we want to increase consumer surplus? Then lower prices or more leisure might be signs of progress, even if they result in a lower GDP. And, of course, many of our goals are nonmonetary. We shouldn’t ignore the economic metrics, but neither should we let them crowd out our other values simply because they are more measurable.

In the meantime, we need to bear in mind that the GDP and productivity statistics overlook much of what we value, even when using a narrow economic lens. What’s more, the gap between what we measure and what we value grows every time we gain access to a new good or service that never existed before, or when existing goods become free as they so often do when they are digitized. (pages 123-124)

 

THE SPREAD

Brynjolfsson and McAfee:

…Advances in technology, especially digital technologies, are driving an unprecedented reallocation of wealth and income. Digital technologies can replicate valuable ideas, insights, and innovations at very low cost. This creates bounty for society and wealth for innovators, but diminishes the demand for previously important types of labor, which can leave many people with reduced incomes.

The combination of bounty and spread challenges two common though contradictory worldviews. One common view is that advances in technology always boost incomes. The other is that automation hurts workers’ wages as people are replaced by machines. Both of these have a kernel of truth, but the reality is more subtle. Rapid advances in our digital tools are creating unprecedented wealth, but there is no economic law that says all workers, or even a majority of workers, will benefit from these advances.

For almost two hundred years, wages did increase alongside productivity. This created a sense of inevitability that technology helped (almost) everyone. But more recently, median wages have stopped tracking productivity, underscoring the fact that such a decoupling is not only a theoretical possibility but also an empirical fact in our current economy. (page 128)

Statistics on how the median worker is doing versus the top 1 percent are revealing:

…The year 1999 was the peak year for real (inflation-adjusted) income of the median American household. It reached $54,932 that year, but then started falling. By 2011, it had fallen nearly 10 percent to $50,054, even as overall GDP hit a record high. In particular, wages of unskilled workers in the United States and other advanced countries have trended downward.

Meanwhile, for the first time since the Great Depression, over half the total income in the United States went to the top 10 percent of Americans in 2012. The top 1 percent earned over 22 percent of income, more than doubling their share since the early 1980s. The share of income going to the top hundredth of one percent of Americans, a few thousand people with annual incomes over $1 million, is now at 5.5 percent, after increasing more between 2011 and 2012 than any year since 1927-1928. (page 129)

Technology is changing economics. Brynjolfsson and McAfee point out two examples: digital photography and TurboTax.

At one point, Kodak employed 145,300 people. But recently, Kodak filed for bankruptcy. Analog photography peaked in the year 2000. As of 2014, over 2.5 billion people had digital cameras and the vast majority of photos are digital. At the same time, Facebook has a market value many times what Kodak ever did. And Facebook has created at least several billionaires, each of whom has a net worth more than ten times what George Eastman – founder of Kodak – had. Also, in 2012, Facebook had over one billion users, despite employing only 4,600 people (roughly 1,000 of whom are engineers).

Just as digital photography has made it far easier for many people to take and store photos, so TurboTax software has made it much more convenient for many people to file their taxes. Meanwhile, tens of thousands of tax preparers – including those at H&R Block – have had their jobs and incomes threatened. But the creators of TurboTax have done very well – one is a billionaire.

The crucial reality from the standpoint of economics is that it takes a relatively small number of designers and engineers to create and update a program like TurboTax. As we saw in chapter 4, once the algorithms are digitized they can be replicated and delivered to millions of users at almost zero cost. As software moves to the core of every industry, this type of production process and this type of company increasingly populates the economy. (pages 130-131)

Brynjolfsson and McAfee report that most Americans have become less wealthy over the past several decades.

Between 1983 and 2009, Americans became vastly wealthier overall as the total value of their assets increased. However, as noted by economists Ed Wolff and Sylvio Allegretto, the bottom 80 percent of the income distribution actually saw a net decrease in their wealth. Taken as a group, the top 20 percent got not 100 percent of the increase, but more than 100 percent. Their gains included not only the trillions of dollars of wealth newly created in the economy but also some additional wealth that was shifted in their direction from the bottom 80 percent. The distribution was also highly skewed even among relatively wealthy people. The top 5 percent got 80 percent of the nation’s wealth increase; the top 1 percent got over half of that, and so on for ever-finer subdivisions of the wealth distribution…

Along with wealth, the income distribution has also shifted. The top 1 percent increased their earnings by 278 percent between 1979 and 2007, compared to an increase of just 35 percent for those in the middle of the income distribution. The top 1 percent earned over 65 percent of the income between 2002 and 2007. (page 131)

Brynjolfsson and McAfee then add:

As we discussed in our earlier book Race Against the Machine, these structural economic changes have created three overlapping pairs of winners and losers. As a result, not everyone’s share of the economic pie is growing. The first two sets of winners are those who have accumulated significant quantities of the right capital assets. These can be either nonhuman capital (such as equipment, structures, intellectual property, or financial assets), or human capital (such as training, education, experience, and skills). Like other forms of capital, human capital is an asset that can generate a stream of income. A well-trained plumber can earn more each year than an unskilled worker, even if they both work the same number of hours. The third group of winners is made up of the superstars among us who have special talents – or luck. (pages 133-134)

The most basic economic model, write Brynjolfsson and McAfee, treats technology as a simple multiplier on everything else, increasing overall productivity evenly for everyone. In other words, all labor is affected equally by technology. Every hour worked produces more value than before.

A slightly more complex model allows for the possibility that technology may not affect all inputs equally, but rather may be ‘biased’ toward some and against others. In particular, in recent years, technologies like payroll processing software, factory automation, computer-controlled machines, automated inventory control, and word processing have been deployed for routine work, substituting for workers in clerical tasks, on the factory floor, and doing rote information processing.

By contrast, technologies like big data and analytics, high-speed communications, and rapid prototyping have augmented the contributions made by more abstract and data-driven reasoning, and in turn have increased the value of people with the right engineering, creative, and design skills. The net effect has been to decrease demand for less skilled labor while increasing the demand for skilled labor. Economists including David Autor, Lawrence Katz and Alan Krueger, Frank Levy and Richard Murnane, Daren Acemoglu, and many others have documented this trend in dozens of careful studies. They call it skill-biased technical change. By definition, skill-biased technical change favors people with more human capital. (page 135)

Skill-biased technical change can be seen in the growing income gaps between people with different levels of education.

Furthermore, organizational improvements related to technical advances may be even more significant than the technical advances themselves.

…Work that Erik did with Stanford’s Tim Bresnahan, Wharton’s Lorin Hitt, and MIT’s Shinkyu Yang found that companies used digital technologies to reorganize decision-making authority, incentives systems, information flows, hiring systems, and other aspects of their management and organizational processes. This coinvention of organization and technology not only significantly increased productivity but tended to require more educated workers and reduce demand for less-skilled workers. This reorganization of production affected those who worked directly with computers as well as workers who, at first glance, seemed to be far from the technology…

Among the industries in the study, each dollar of computer capital was often the catalyst for more than ten dollars of complementary investments in ‘organizational capital,’ or investments in training, hiring, and business process redesign. The reorganization often eliminates a lot of routine work, such as repetitive order entry, leaving behind a residual set of tasks that require relatively more judgment, skills, and training.

Companies with the biggest IT investments typically made the biggest organizational changes, usually with a lag of five to seven years before seeing the full performance benefits. These companies had the biggest increase in the demand for skilled work relative to unskilled work….

This means that the best way to use new technologies is usually not to make a literal substitution of a machine for each human worker, but to restructure the process. Nonetheless, some workers (usually the less skilled ones) are still eliminated from the production process and others are augmented (usually those with more education and training), with predictable effects on the wage structure. Compared to simply automating existing tasks, this kind of organizational coinvention requires more creativity on the part of entrepreneurs, managers, and workers, and for that reason it tends to take time to implement the changes after the initial invention and introduction of new technologies. But once the changes are in place, they generate the lion’s share of productivity improvements.(pages 137-138)

Brynjolfsson and McAfee explain thatskill-biased technical change can be somewhat misleading in the context of jobs eliminated as companies have reorganized. It’s more accurate to say that routine tasks – whether cognitive or manual – have been replaced the most by computers. One study by Nir Jaimovich and Henry Siu found that the demand for routine cognitive tasks such as cashiers, mail clerks, and bank tellers and routine manual tasks such as machine operators, cement masons, and dressmakers was not only falling, but falling at accelerating rate.

These jobs fell by 5.6 percent between 1981 and 1991, 6.6 percent between 1991 and 2001, and 11 percent between 2001 and 2011. In contrast, both nonroutine cognitive work and nonroutine manual work grew in all three decades. (pages 139-140)

Since the early 1980s, when computers began to be adopted, the share of income going to labor has declined while the share of income going to owners of physical capital has increased. However, as new capital is added cheaply at the margin, the rewards earned by capitalists may not automatically grow relative to labor, observe the authors.

 

IMPLICATIONS OF THE BOUNTY AND THE SPREAD

Franklin D. Roosevelt:

The test of our progress is not whether we add more to the abundance of those who have much; it is whether we provide enough for those who have little.

Like productivity, state Brynjolfsson and McAfee, GDP, corporate investment, and after-tax profits are also at record highs. Yet the employment-to-population ratio is lower than at any time in at least two decades. This raises three questions:

• Will the bounty overcome the spread?
• Can technology not only increase inequality but also create structural unemployment?
• What about globalization, the other great force transforming the economy – could it explain recent declines in wages and employment?

Thanks to technology, we will keep getting ever more output from fewer inputs like raw materials, capital, and labor. We will benefit from higher productivity, but also from free digital goods. Brynjolfsson and McAfee:

… ‘Bounty’ doesn’t simply mean more cheap consumer goods and empty calories. As we noted in chapter 7, it also means simultaneously more choice, greater variety, and higher quality in many areas of our lives. It means heart surgeries performed without cracking the sternum and opening the chest cavity. It means constant access to the world’s best teachers combined with personalized self-assessments that let students know how well they’re mastering the material. It means that households have to spend less of their total budget over time on groceries, cars, clothing, and utilities. It means returning hearing to the deaf and, eventually, sight to the blind. It means less need to work doing boring, repetitive tasks and more opportunity for creative, interactive work. (page 166)

However, technological progress is also creating ever larger differences in important areas – wealth, income, standards of living, and opportunities for advancement. If the bounty is large enough, do we need to worry about the spread? If all people’s economic lives are improving, then is increasing spread really a problem? Harvard economist Greg Mankiw has argued that the enormous income of the ‘one percent’ may reflect – in large part – the rewards of creating value for everyone else. Innovators improve the lives of many people, and the innovators often get rich as a result.

The high-tech industry offers many examples of this happy phenomenon in action. Entrepreneurs create devices, websites, apps, and other goods and services that we value. We buy and use them in large numbers, and the entrepreneurs enjoy great financial success…

We particularly want to encourage it because, as we saw in chapter 6, technological progress typically helps even the poorest people around the world. Careful research has shown that innovations like mobile telephones are improving people’s incomes, health, and other measures of well-being. As Moore’s Law continues to simultaneously drive down the cost and increase the capability of these devices, the benefits they bring will continue to add up. (pages 167-168)

Those who believe in the strong bounty argument think that unmeasured price decreases, quality improvements, and other benefits outweigh the lost ground in other areas, such as the decline in the median real income.

Unfortunately, however, some important items such as housing, health care, and college have gotten much more expensive over time. Brynjolfsson and McAfee cite research by economist Jared Bernstein, who found that while median family income grew by 20 percent between 1990 and 2008, prices for housing and college grew by about 50 percent, and health care by more than 150 percent. Moreover, median incomes have been falling in recent years.

Brynjolfsson and McAfee then add:

That many Americans face stagnant and falling income is bad enough, but it is now combined with decreasing social mobility – an ever lower chance that children born at the bottom end of the spread will escape their circumstances and move upward throughout their lives and careers…This is exactly what we’d expect to see as skill-biased technical changes accelerates. (pages 170-171)

Based on economic theory and supported by most of the past two hundred years, economists have generally agreed that technological progress has created more jobs than it has destroyed. Some workers are displaced by new technologies, but the increase in total output creates more than enough new jobs.

Regarding economic theory, there are three possible arguments: inelastic demand, rapid change, and severe inequality.

If lower costs leads to lower prices of goods, and if lower prices leads to increased demand for the goods, then this may lead to an increase in the demand for labor. It depends on the elasticity of demand.

For some goods, such as lighting, demand is relatively inelastic: price declines have not led to a proportionate increase in demand. For other goods, demand has been relatively elastic: price declines have resulted in an even greater increase in demand. One example, write Brynjolfsson and McAfee, is the Jevons paradox: more energy efficiency can sometimes lead to greater total demand for energy.

If elasticity is exactly equal to one – so a 1 percent decline in price leads to a 1 percent increase in demand – then total revenues (price times quantity) are unchanged, explain Brynjolfsson and McAfee. In this case, an increase in productivity, meaning less labor needed for each unit of output, will be exactly offset by an increase in total demand, so that the overall demand for labor is unchanged. Elasticity of one, it can be argued, is what happens in the overall economy.

Brynjolfsson and McAfee remark that the second, more serious, argument for technological unemployment is that our skills, organizations, and institutions cannot keep pace with technological change. What if it takes ten years for displaced workers to learn new skills? What if, by then, technology has changed again?

Faster technological progress may ultimately bring greater wealth and longer lifespans, but it also requires faster adjustments by both people and institutions. (page 178)

The third argument is that ongoing technological progress will lead to a continued decline in real wages for many workers. If there’s technological progress where only those with specific skills, or only those who own a certain kind of capital, benefit, then the equilibrium wage may indeed approach a dollar an hour or even zero. Over history, many inputs to production, from whale oil to horses, have reached a point where they were no longer needed even at zero price.

Although job growth has stopped tracking productivity upward in the past fifteen years or so, it’s hard to know what the future holds, say the authors.

Brynjolfsson and McAfee then ask: What if there were an endless supply of androids that never break down and that could do all the jobs that humans can do, but at essentially no cost? There would be an enormous increase in the volume, variety, and availability of goods.

But there would also be severe dislocations to the labor force. Entrepreneurs would continue to invent new products and services, but they would staff these companies with androids. The owners of androids and other capital assets or natural resources would capture all the value in the economy. Those with no assets would have only labor to sell, but it would be worthless. Brynjolfsson and McAfee sum it up: you don’t want to compete against close substitutes when those substitutes have a cost advantage.

But in principle, machines can have very different strengths and weaknesses than humans. When engineers work to amplify these differences, building on the areas where machines are strong and humans are weak, then the machines are more likely to complement humans rather than substitute for them. Effective production is more likely to require both human and machine inputs, and the value of the human inputs will grow, not shrink, as the power of the machines increases. A second lesson of economics and business strategy is that it’s great to be a complement to something that’s increasingly plentiful. Moreover, this approach is more likely to create opportunities to produce goods and services that could never have been created by unaugmented humans, or machines that simply mimicked people, for that matter. These new goods and services provide a path for productivity growth based on increased output rather than reduced inputs.

Thus in a very real sense, as long as there are unmet needs and wants in the world, unemployment is a loud warning that we simply aren’t thinking hard enough about what needs doing. We aren’t being creative enough about solving the problems we have using the freed-up time and energy of the people whose old jobs were automated away. We can do more to invent technologies and business models that augment and amplify the unique capabilities of humans to create new sources of value, instead of automating the ones that already exist. As we will discuss further in the next chapters, this is the real challenge facing our policy makers, our entrepreneurs, and each of us individually. (page 182)

 

LEARNING TO RACE WITH MACHINES: RECOMMENDATIONS FOR INDIVIDUALS

Pablo Picasso on computers:

But they are useless. They can only give you answers.

Even where digital machines are far ahead of humans, humans still have important roles to play. IBM’s Deep Blue beat Garry Kasparov in a chess match in 1997. And nowadays even cheap chess programs are better than any human. Does that mean humans no longer have anything to contribute to chess? Brynjolfsson and McAfee quote Kasparov’s comments on ‘freestyle’ chess (which involves teams of humans plus computers):

The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and ‘coaching’ their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably superior to a strong human + machine + inferior process. (pages 189-190)

Brynjolfsson and McAfee explain:

The key insight from freestyle chess is that people and computers don’t approach the same task the same way. If they did, humans would have had nothing to add after Deep Blue beat Kasparov; the machine, having learned how to mimic human chess-playing ability, would just keep riding Moore’s Law and racing ahead. But instead we see that people still have a great deal to offer the game of chess at its highest levels once they’re allowed to race with machines, instead of purely against them.

Computers are not as good as people at being creative:

We’ve never seen a truly creative machine, or an entrepreneurial one, or an innovative one. We’ve seen software that could create lines of English text that rhymed, but none that could write a true poem… Programs that can write clean prose are amazing achievements, but we’ve not yet seen one that can figure out what to write about next. We’ve also never seen software that could create good software; so far, attempts at this have been abject failures.

These activities have one thing in common: ideation, or coming up with new ideas or concepts. To be more precise, we should probably say good new ideas or concepts, since computers can easily be programmed to generate new combinations of preexisting elements like words. This however, is not recombinant innovation in any meaningful sense. It’s closer to the digital equivalent of a hypothetical room full of monkeys banging away randomly on typewriters for a million years and still not reproducing a single play of Shakespeare’s.

Ideation in its many forms is an area today where humans have a comparative advantage over machines. Scientists come up with new hypotheses. Journalists sniff out a good story. Chefs add a new dish to the menu. Engineers on a factory floor figure out why a machine is no longer working properly. [Workers at Apple] figure out what kind of tablet computer we actually want. Many of these activities are supported or accelerated by computers, but none are driven by them.

Picasso’s quote at the head of this chapter is just about half right. Computers are not useless, but they’re still machines for generating answers, not posing interesting new questions. That ability still seems to be uniquely human, and still highly valuable. We predict that people who are good at idea creation will continue to have a comparative advantage over digital labor for some time to come, and will find themselves in demand. In other words, we believe that employers now and for some time to come will, when looking for talent, follow the advice attributed to the Enlightenment sage Voltaire: ‘Judge a man by his questions, not his answers.’

Ideation, innovation, and creativity are often described as ‘thinking outside the box,’ and this characterization indicates another large and reasonably sustainable advantage of human over digital labor. Computers and robots remain lousy at doing anything outside the frame of their programming… (pages 191-192)

Futurist Kevin Kelly:

You’ll be paid in the future based on how well you work with robots. (page 193)

Brynjolfsson and McAfee sum it up:

So ideation, large-frame pattern recognition, and the most complex forms of communication are cognitive areas where people still seem to have the advantage, and also seem likely to hold onto it for some time to come. Unfortunately, though, these skills are not emphasized in most educational environments today. (page 194)

Sociologists Richard Arum and Josipa Roksa have found in their research that many American college students today are not good at critical thinking, written communication, problem solving, and analytic reasoning. In other words, many college students are not good at ideation, pattern recognition, and complex communication. Arum and Roksa came to this conclusion after testing college students’ ability to read background documents and write an essay on them. A major reason for this shortcoming, say Arum and Roksa, is that college students spend only 9 percent of their time studying, while spending 51 percent of their time socializing, recreating, etc.

Brynjolfsson and McAfee emphasize that the future is uncertain:

We have to stress that none of our predictions and recommendations here should be treated as gospel. We don’t project that computers and robots are going to acquire the general skills of ideation, large-frame pattern recognition, and highly complex communication any time soon, and we don’t think that Moravec’s paradox is about to be fully solved. But one thing we’ve learned about digital progress is never say never. Like many other observers, we’ve been surprised over and over as digital technologies demonstrated skills and abilities straight out of science fiction.

In fact, the boundary between uniquely human creativity and machine capabilities continues to change. Returning to the game of chess, back in 1956, thirteen-year-old child prodigy Bobby Fischer made a pair of remarkably creative moves against grandmaster Donald Byrne. First he sacrificed his knight, seemingly for no gain, and then exposed his queen to capture. On the surface, these moves seemed insane, but several moves later, Fischer used these moves to win the game. His creativity was hailed at the time as the mark of genius. You today if you program that same position into a run-of-the-mill chess program, it will immediately suggest exactly the moves that Fischer played. It’s not because the computer has memorized the Fischer-Byrne game, but rather because it searches far enough ahead to see that these moves really do pay off. Sometimes, one man’s creativity is another machine’s brute-force analysis.

We’re confident that more surprises are in store. After spending time working with leading technologists and watching one bastion of human uniqueness after another fall before the inexorable onslaught of innovation, it’s becoming harder and harder to have confidence that any given task will be indefinitely resistant to automation. That means people will need to be more adaptable and flexible in their career aspirations, ready to move on from areas that become subject to automation, and seize new opportunities where machines complement and augment human capabilities. Maybe we’ll see a program that can scan the business landscape, spot an opportunity, and write up a business plan so good it’ll have venture capitalists ready to invest. Maybe we’ll see a computer that can write a thoughtful and insightful report on a complicated topic. Maybe we’ll see an automatic medical diagnostician with all the different kinds of knowledge and awareness of a human doctor. And maybe we’ll see a computer that can walk up the stairs to an elderly woman’s apartment, take her blood pressure, draw blood, and ask if she’s been taking her medication, all while putting her at ease instead of terrifying her. We don’t think any of these advances is likely to come any time soon, but we’ve also learned that it’s very easy to underestimate the power of digital, exponential, and combinatorial innovation. So never say never. (pages 202-204)

 

POLICY RECOMMENDATIONS

Brynjolfsson and McAfee affirm that Economics 101 still applies because digital labor is still far from a complete substitute for human labor.

For now the best way to tackle our labor force challenges is to grow the economy. As companies see opportunity for growth, the great majority will need to hire people to seize them. Job growth will improve, and so will workers’ prospects. (page 207)

Brynjolfsson and McAfee also note that there is broad agreement among conservative and liberal economists when it comes to the government policies recommended by Economics 101.

(1) Education

The more educated the populace is, the more innovation tends to occur, which leads to more productivity growth and thus faster economic growth.

The educational system can be improved by using technology. Consider massive open online courses (MOOCs), which have two main economic benefits.

• The first and most obvious one is that MOOCs enable low-cost replication of the best teachers, content, and methods. Just as we can all listen to the best pop singer or cellist in the world today, students will soon have access to the most exciting geology demonstrations, the most insightful explanations of Renaissance art, and the most effective exercises for learning statistical techniques.
• The second, subtler benefit from the digitization of education is ultimately more important. Digital education creates an enormous stream of data that makes it possible to give feedback to both teacher and student. Educators can run controlled experiments on teaching methods and adopt a culture of continuous improvement. (pages 210-211)

Brynjolfsson and McAfee then add:

The real impact of MOOCs is mostly ahead of us, in scaling up the reach of the best teachers, in devising methods to increase the overall level of instruction, and in measuring and finding ways to accelerate student improvement… We can’t predict exactly which methods will be invented and which will catch on, but we do see a clear path for enormous progress. The enthusiasm and optimism in this space is infectious. Given the plethora of new technologies and techniques that are now being explored, it’s a certainty that some of them – in fact, we think many of them – will be significant improvements over current approaches to teaching and learning. (pages 211-212)

On the question of how to improve the educational system – in addition to using technology – it’s what you might expect: attract better teachers, lengthen school years, have longer school days, and implement a no-excuses philosophy that regularly tests students. Surprise, surprise: This is what has helped places like Singapore and South Korea to rank near the top in terms of education. Of course, while some teachers should focus on teaching testable skills, other teachers should be used to teach hard-to-measure skills like creativity and unstructured problem solving, observe Brynjolfsson and McAfee.

(2) Startups

Brynjolfsson and McAfee:

We champion entrepreneurship, but not because we think everyone can or should start a company. Instead, it’s because entrepreneurship is the best way to create jobs and opportunity. As old tasks get automated away, along with demand for their corresponding skills, the economy must invent new jobs and industries. Ambitious entrepreneurs are best at this, not well-meaning government leaders or visionary academics. Thomas Edison, Henry Ford, Bill Gates, and many others created new industries that more than replaced the work that was eliminated as farming jobs vanished over the decades. The current transformation of the economy creates an equally large opportunity. (page 214)

Joseph Schumpeter argued that innovation is central to capitalism, and that it’s essentially a recombinant process. Schumpeter also held that innovation is more likely to take place in startups rather than in incumbent companies.

…Entrepreneurship, then, is an innovation engine. It’s also a prime source of job growth. In America, in fact, it appears to be the only thing that’s creating jobs. In a study published in 2010, Tim Kane of the Kauffman Foundation used Census Bureau data to divide all U.S. companies into two categories: brand-new startups and existing firms (those that had been around for at least a year). He found that for all but seven years between 1977 and 2005, existing firms as a group were net job destroyers, losing an average of approximately one million jobs annually. Startups, in sharp contrast, created on average a net three million jobs per year. (pages 214-215)

Entrepreneurship in America remains the best in the world, but it appears to have stagnated recently. One factor may be a decline in would-be immigrants. Immigrants have been involved in a high percentage of startups, but this trend appears to have slowed recently. Moreover, excessive regulation seems to be stymieing startups.

(3) Job Matching

It should be easier to match people with jobs. Better databases can be developed. So can better algorithms for identifying the needed skills. Ratings like TopCoder scores can provide objective metrics of candidate skills.

(4) Basic Science

Brynjolfsson and McAfee:

After rising for a quarter-century, U.S. federal government support for basic academic research started to fall in 2005. This is cause for concern because economics teaches that basic research has large beneficial externalities. This fact creates a role for government, and the payoff can be enormous. The Internet, to take one famous example, was born out of U.S. Defense Department research into how to build bomb-proof networks. GPS systems, touchscreen displays, voice recognition software like Apple’s Siri, and many other digital innovations also arose from basic research sponsored by the government. It’s pretty safe to say, in fact, that hardware, software, networks, and robots would not exist in anything like the volume, variety, and forms we know today without sustained government funding. This funding should be continued, and the recent dispiriting trend of reduced federal funding for basic research in America should be reduced. (pages 218-219)

For some scientific challenges, offering prizes can help:

Many innovations are of course impossible to describe in advance (that’s what makes them innovations). But there are also cases where we know exactly what we’re looking for and just want somebody to invent it. In these cases, prizes can be especially effective. Google’s driverless car was a direct outgrowth of a Defense Advanced Research Projects Agency (DARPA) challenge that offered a one-million-dollar prize for a car that could navigate a specific course without a human driver. Tom Kalil, Deputy Director for Policy of the United States Office of Science and Technology Policy, provides a great playbook for how to run a prize:

• Shine a spotlight on a problem or opportunity
• Pay only for results
• Target an ambitious goal without predicting which team or approach is most likely to succeed
• Reach beyond usual suspects to tap top talent
• Stimulate private-sector investment many times greater than the prize purse
• Bring out-of-discipline perspectives to bear
• Inspire risk-taking by offering a level playing field
• Establish clear target metrics and validation protocols

Over the past decade, the total federal and private funds earmarked for large prizes have more than tripled and now surpass $375 million. This is great, but it’s just a tiny fraction of overall government spending on government research. There remains great scope for increasing the volume and variety of innovation competitions. (pages 219-220)

(5) Upgrade Infrastructure

Brynjolfsson and McAfee write that, like education and scientific research, infrastructure has positive externalities. That’s why nearly all economists agree that the government should be involved in building and maintaining infrastructure – streets and highways, bridges, ports, dams, airports and air traffic control systems, and so on.

Excellent infrastructure makes a country a more pleasant place to live, and also a more productive place in which to do business. Ours, however, is not in good shape. The American Society of Civil Engineers (ASCE) gave the United States an overall infrastructure grade of D+ in 2013, and estimated that the country has a backlog of over $3.6 trillion in infrastructure investment…

Bringing U.S. infrastructure up to an acceptable grade would be one of the best investments the country could make in its own future. (pages 220-221)

Economists also agree on the importance of maximizing the potential inflow of legal immigrants, especially those who are highly skilled.

Any policy shift advocated by both the libertarian Cato Institute and the progressive Center for American Progress can truly be said to have diverse support. Such is the case for immigration reform, a range of proposed changes with the broad goal of increasing the number of legal foreign-born workers and citizens in the United States. Generous immigration policies really are part of the Econ 101 playbook; there is wide agreement among economists that they benefit not only the immigrants themselves but also the economy of the country they move to. (page 222)

Brynjolfsson and McAfee continue:

…Since 2007, it appears that net illegal immigration to the United States is approximately zero, or actually negative. And a study by the Brookings Institution found that highly educated immigrants now outnumber less educated ones; in 2010, 30 percent had at least a college education, while only 28 percent lacked the equivalent of a high school degree.

Entrepreneurship in America, particularly in technology-intensive sectors of the economy, is fueled by immigration to an extraordinary degree… As economist Michael Kremer demonstrated in a now classic paper, increasing the number of immigrant engineers actually leads to higher, not lower, wages for native-born engineers because immigrants help creative ecosystems flourish. It’s no wonder that wages are higher for good software designers in Silicon Valley, where they are surrounded by others with similar and generally complementary skills, rather than in more isolated parts of the world.

Today, immigrants are having this large and beneficial effect on the country not because of America’s processes and policies but often despite them. Immigration to the United States is often described as slow, complex, inefficient, and highly bureaucratic… (pages 222-223)

A green card should be stapled to every advanced diploma awarded to an immigrant, say Brynjolfsson and McAfee. Furthermore, a separate ‘startup visa’ category should be created making it easier for entrepreneurs – especially those who have already attracted funding – to launch their ventures in the United States.

(6) Tax Wisely

Obviously we should tax pollution, which is a negative externality. Same goes for things like traffic congestion. Singapore has implemented an Electronic Road Pricing System that has virtually eliminated congestion, note the authors.

Also, land could be taxed more. So could government-owned oil and gas leases. Finally, the top marginal income tax could be increased without harming the economy.

 

LONG-TERM RECOMMENDATIONS

Voltaire:

Work saves a man from three great evils: boredom, vice, and need.

Brynjolfsson and McAfee first point out that technological progress shouldn’t be opposed. Productivity growth is central to economic growth. Overall, things continue to get better. So we should encourage ongoing innovation and deal with the associated challenges as they come up.

We are also skeptical of efforts to come up with fundamental alternatives to capitalism. By ‘capitalism’ here, we mean a decentralized economic system of production and exchange in which most of the means of production are in private hands (as opposed to belonging to the government), where most exchange is voluntary (no one can force you to sign a contract against your will), and where most goods have prices that vary based on relative supply and demand instead of being fixed by a central authority. All of these features exist in most economies around the world today. Many are even in place in today’s China, which is still officially communist.

These features are so widespread because they work so well. Capitalism allocates resources, generates innovation, rewards effort, and builds affluence with high efficiency, and these are extraordinarily important things to do well in a society. As a system, capitalism is not perfect, but it’s far better than the alternatives. Winston Churchill said that, ‘Democracy is the worst form of government except for all those others that have been tried.’ We believe the same about capitalism. (page 231)

What’s likely to change, though, remark Brynjolfsson and McAfee, are concepts related to income and money.

The idea of a basic income is that everyone receives a minimum standard of living. People are free to improve on it by working, investing, starting a company, or other such activities. English-American activist Thomas Paine argued for a form of basic income. Later supporters have included the philosopher Bertrand Russell and civil rights leader Martin Luther King, Jr.

Many economists on both the left and the right have agreed with King. Liberals including James Tobin, Paul Samuelson, and John Kenneth Galbraith and conservatives like Milton Friedman and Friedrich Hayek have all advocated income guarantees in one form or another, and in 1968 more than 1,200 economists signed a letter in support of the concept addressed to the U.S. Congress.

The president elected that year, Republican Richard Nixon, tried throughout his first term in office to enact it into law. In a 1969 speech he proposed a Family Assistance Plan that had many features of a basic income program. The plan had support across the ideological spectrum, but it also faced a large and diverse group of opponents. (page 233)

In any case, basic income – especially on its own – is not the answer. Referring to Voltaire’s quote, basic income saves a person from need, but not from boredom or vice. Work is extremely important for human beings. Brynjolfsson and McAfee mention that Daniel Pink, inDrive, identifies three major motivations: mastery, autonomy, and purpose.

It seems that all around the world, people want to escape the evils of boredom, vice, and need and instead find mastery, autonomy, and purpose by working. (page 235)

Work gives a great many individuals their sense of meaning. What’s true for individuals is also true for communities. Research has shown that people are happier and better off in communities where people work.

Brynjolfsson and McAfee then point out that economists have developed reliable ways to encourage and reward work. Moreover, innovators and entrepreneurs are developing technologies that not only substitute for human labor, but also complement it. The bottom line is that we should continue to try to create and maintain as many jobs as possible.

Perhaps a better way to help the poor is with a ‘negative income tax,’ which the conservative economist Milton Friedman suggested. Say the negative income tax was 50%. Friedman gave an example (in 1968) of $3,000 in income as the cutoff. Someone making $3,000 (again in 1968 dollars) would neither pay a tax nor receive a negative income tax. If a person made only $1,000, then they would get an additional $1,000 as a negative income tax, for a total of $2,000. If the same person made $2,000, they would get an additional $500, for a total of $2,500. Overall, the negative income tax combines a guaranteed minimum income with an incentive to work.

Brynjolfsson and McAfee also point out that taxes on labor are not ideal because they discourage labor. Of course, we need some income taxes. But it may be possible to raise other kinds of taxes – including Pigovian taxes on pollution and other negative externalities, consumption taxes, and the value-added tax (VAT). With a VAT, companies pay based on the difference between their costs (labor, raw materials, etc.) and the prices they charge customers. A VAT is easy to collect, and it’s adjustable and lucrative, observe the authors. The United States is the only country out of the thirty-four in the OECD that doesn’t have a VAT.

 

TECHNOLOGY AND THE FUTURE

Brynjolfsson and McAfee:

After surveying the landscape, we are convinced that we are at an inflection point – the early stages of a shift as profound as that brought on by the Industrial Revolution. Not only are the new technologies exponential, digital, and combinatorial, but most of the gains are still ahead of us…

Our generation will likely have the good fortune to experience two of the most amazing events in history: the creation of true machine intelligence and the connection of all humans via a common digital network, transforming the planet’s economics. Innovators, entrepreneurs, scientists, tinkerers, and many other types of geeks will take advantage of this cornucopia to build technologies that astonish us, delight us, and work for us. Over and over again, they’ll show how right Arthur C. Clarke was when he observed that a sufficiently advanced technology can be indistinguishable from magic. (page 251)

Material needs and wants will become less important over time. Brynjolfsson and McAfee:

We will increasingly be concerned with questions about catastrophic events, genuine existential risks, freedom versus tyranny, and other ways that technology can have unintended or unexpected side effects…

Until recently, our species did not have the ability to destroy itself. Today it does. What’s more, that power will reach the hands of more and more individuals as technologies become both more powerful and cheaper – and thus more ubiquitous. Not all of those individuals will be both sane and well intentioned. As Bill Joy and others have noted, genetic engineering and artificial intelligence can create self-replicating entities. That means that someone working in a basement laboratory might someday use one of these technologies to unleash destructive forces that affect the entire planet. The same scientific breakthroughs in genome sequencing that can be used to cure disease can also be used to create a weaponized version of the smallpox virus. Computer programs can also self-replicate, becoming digital viruses, so the same global network that spreads ideas and innovations can also spread destruction. The physical limits on how much damage any individual or small group could do are becoming less and less constrained. Will our ability to detect and counteract destructive uses of technology advance rapidly enough to keep us safe? That will be an increasingly important question to answer. (pages 252-253)

Is the Singularity Near?

In utopian versions of digital consciousness, we humans don’t fight with machines; we join with them, uploading our brains into the cloud and otherwise becoming part of a ‘technological singularity.’ This is a term coined in 1983 by science-fiction author Vernor Vinge, who predicted that, ‘We will soon create intelligences greater than our own… When this happens, human history will have reached a kind of singularity, an intellectual transition as impenetrable as the knotted space-time at the center of a black hole, and the world will move far beyond our understanding.’

Progress towards such a singularity, Vinge and others have argued, is driven by Moore’s Law. Its accumulated doubling will eventually yield a computer with more processing and storage capacity than the human brain. Once this happens, things become highly unpredictable. Machines could become self-aware, humans and computers could merge seamlessly, or other fundamental transitions could occur… (pages 254-255)

As far as when such a singularity may happen, we simply don’t know. Many have predicted the occurrence of such a singularity in 2050 or later. But as Brynjolfsson and McAfee remind us, with all things digital, never say never. If a supercomputer learns to re-write its own source code repeatedly – thus evolving rapidly – then what?

However, note Brynjolfsson and McAfee, the science-fiction of supercomputers and autonomous cars can be misleading:

…We humans build machines to do things that we see being done in the world by animals and people, but we typically don’t build them the same way that nature built us. As AI trailblazer Frederick Jelinek put it beautifully, ‘Airplanes don’t flap their wings.’

It’s true that scientists, engineers, and other innovators often take cues from biology as they’re working, but it would be a mistake to think that this is always the case, or that major recent AI advances have come about because we’re getting better at mimicking human thought. Journalist Stephen Baker spent a year with the Watson team to research his bookFinal Jeopardy!. He found that, ‘The IBM team paid little attention to the human brain while programming Watson. Any parallels to the brain are superficial, and only the result of chance.’

As we were researching this book we heard similar sentiments from most of the innovators we talked to. Most of them weren’t trying to unravel the mysteries of human consciousness or understand exactly how we think; they were trying to solve problems and seize opportunities. As they did so, they sometimes came up with technologies that had human-like skills and abilities. But these tools themselves were not like humans at all. Current AI, in short, looks intelligent, but it’s an artificial resemblance. That might change in the future. We might start to build digital tools that more closely mimic our minds, perhaps even drawing on our rapidly improving capabilities for scanning and mapping brains. And if we do so, those digital minds will certainly augment ours and might even eventually merge with them, or become self-aware on their own. (pages 255-256)

Brynjolfsson and McAfee remain optimistic about the future:

Even in the face of all these challenges – economic, infrastructural, biological, societal, and existential – we’re still optimistic. To paraphrase Martin Luther King, Jr., the arc of history is long but it bends towards justice. We think the data support this. We’ve seen not just vast increases in wealth but also, on the whole, more freedom, more social justice, less violence, and less harsh conditions for the least fortunate and greater opportunities for more and more people.

Of course, our values and choices will determine our future:

In the second machine age, we need to think much more deeply about what it is we really want and what we value, both as individuals and as a society. Our generation has inherited more opportunities to transform the world than any other. That’s a cause for optimism, but only if we’re mindful of our choices. (page 257)

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here:https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

February 13, 2022

The Most Important Thing Illuminated (Columbia Business School, 2013) is an update of Howard Marks’ outstanding book on value investing,The Most Important Thing (2011). The revision includes the original text plus comments from top value investors Christopher Davis, Joel Greenblatt, and Seth Klarman. There are also notes from Howards Marks himself and from Columbia professor Paul Johnson.

The sections covered here are:

• Second-Level Thinking
• Understanding Market Efficiency
• Value
• The Relationship Between Price and Value
• Understanding Risk
• Recognizing Risk
• Controlling Risk
• Being Attentive to Cycles
• Combating Negative Influences
• Contrarianism
• Finding Bargains
• Patient Opportunism
• Knowing What You Don’t Know
• Appreciating the Role of Luck
• Investing Defensively
• Reasonable Expectations

 

SECOND-LEVEL THINKING

Nearly everyone can engage in first-level thinking, which is fairly simplistic. But few can engage in second-level thinking. Second-level thinking incorporates a variety of considerations, says Marks:

• What is the range of likely future outcomes?
• Which outcome do I think will occur?
• What’s the probability I’m right?
• What does the consensus think?
• How does my expectation differ from the consensus?
• How does the current price of the asset comport with the consensus view of the future, and with mine?
• Is the consensus psychology that’s incorporated in the price too bullish or too bearish?
• What will happen to the asset’s price if the consensus turns out to be right, and what if I’m right?

In order to do better than the market index, you must have an unconventional approach that works. Joel Greenblatt comments:

The idea is that agreeing with the broad consensus, while a very comfortable place for most people to be, is not generally where above-average profits are found. (page 7)

You can do better than the market over time if you use a proven method for betting against the consensus. One way to achieve this is using a quantitative value investing strategy, which – for most of us – will produce better long-term results than trying to pick individual stocks.

 

UNDERSTANDING MARKET EFFICIENCY

Market prices are generally efficient and incorporate relevant information. Assets sell at prices that offer fair risk-adjusted returns relative to other assets. Marks says:

I agree that because investors work hard to evaluate every new piece of information, asset prices immediately reflect the consensus view of the information’s significance. I do not, however, believe the consensus view is necessarily correct. In January 2000, Yahoo sold at $237. In April 2001 it was at $11. Anyone who argues that the market was right both times has his or her head in the clouds; it has to have been wrong on at least one of those occasions. But that doesn’t mean many investors were able to detect and act on the market’s error. (page 9)

Marks then explains:

The bottom line for me is that, although the more efficient markets often misvalue assets, it’s not easy for any one person – working with the same information as everyone else and subject to the same psychological influences – to consistently hold views that are different from the consensus and closer to being correct.

That’s what makes the mainstream markets awfully hard to beat – even if they aren’t always right. (page 10)

Moreover, notes Marks, some asset classes are rather efficient. In most of these:

• the asset class is widely known and has a broad following;
• the class is socially acceptable, not controversial or taboo;
• the merits of the class are clear and comprehensible, at least on surface; and
• information about the class and its components is distributed widely and evenly.

The Boole Microcap Fund is a quantitative value fund focused on micro caps. Micro caps – because they are largely either ignored or misunderstood – are far more inefficient than small caps, mid caps, and large caps. See: https://boolefund.com/best-performers-microcap-stocks/

Value investing – properly applied – is a way to invest systematically in underpriced stocks. For details, see: https://boolefund.com/notes-on-value-investing/

Joel Greenblatt explains why value investing works:

Investments that are out of favor, that don’t look so attractive in the near term, are avoided by most professionals, who feel the need to add performance right now. (page 17)

Marks decided to focus in his career on distressed debt because it was a noticeably less efficient asset class.

 

VALUE

Marks points out that you can either look at the fundamental attributes of the company – such as earnings and cash flows – or you can look at the associated stock price, and how it has moved in the past. Value investing is the systematic purchase of businesses below their likely intrinsic values.

When you buy stock, you become a part owner of the underlying business. So you would like to figure out what the business is worth, and then pay a price well below that. Imagine if you were going to buy a laundromat or a farm. You would want to figure out how much it earned in a normal year. And you would want to estimate any future growth in those earnings.

• Many businesses are difficult to value. The trick, says Buffett, is to stay in your circle of competence: If you focus on those businesses that you can value, you have a chance to find a few investments that will beat the market. There are thousands of tiny businesses (public and private) – like laundromats – that you probably can value.

For most of us, a more reliable way to beat the market is by adopting a quantitative value strategy, which systematically buys stocks below intrinsic value, on average. Lakonishok, Shleifer, and Vishny give a good explanation of quantitative value investing in their 1994 paper, “Contrarian Investment, Extrapolation, and Risk.” Link:http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

If you do spend time analyzing individual businesses that might be good long-term investments, then another trick is to find companies that have a sustainable competitive advantage. Buffett uses the termmoat. A business with a moat has a sustainably high ROE (return on equity), which can make for a rewarding long-term investment if you pay a reasonable price. See: https://boolefund.com/notes-on-value-investing/

Marks distinguishes between value and growth.

• For many value investors, including Buffett, the future growth of a company’s cash flows is simply a component of its value today.

Marks points out that some investors look for a business that can grow a great deal in the future; other investors focus on the value of a business today, and buying well below that value. Marks comments that the “value” approach is more consistent, while the “growth” approach – when it works – can lead to more dramatic results. Marks identifies himself as a value investor because he cherishes consistency above drama.

For value investing to work, not only do you have to buy consistently below intrinsic value; but you also have to hold each stock long enough for the stock price to approach intrinsic value. This can often take 3 to 5 years. Meanwhile, you are very likely to be down from your initial purchase price, as Greenblatt explains:

Unless you buy at the exact bottom tick (which is next to impossible), you will be down at some point after you make every investment. (page 26)

It’s challenging to own shares of a business that remains out-of-favor for an extended period of time. One advantage of a quantitative value strategy is that it’s largely (or entirely) automated, which thereby minimizes psychological errors.

 

THE RELATIONSHIP BETWEEN PRICE AND VALUE

Marks explains:

For a value investor, price has to be the starting point. It has been demonstrated time and time again that no asset is so good that it can’t become a bad investment if bought at too high a price. And there are few assets so bad that they can’t be a good investment when bought cheap enough. (page 29)

Marks later adds:

Investor psychology can cause a security to be priced just about anywhere in the short run, regardless of its fundamentals. (page 32)

Overpriced investments are often “priced for perfection.” In this situation, investors frequently overpay and then later discover that the investment is not perfect and has flaws. By contrast, hated investments are often low risk:

The safest and most potentially profitable thing is to buy something when no one likes it. Given time, its popularity, and thus its price, can only go one way: up. (page 33)

 

UNDERSTANDING RISK

Marks quotes Elroy Dimson:

Risk means more things can happen than will happen. (page 39)

Because the market is mostly efficient, riskier investments have to offer higher potential returns in order to attract capital. However, writes Marks, riskier investments don’t always produce higher returns, otherwise they wouldn’t be riskier. In other words, riskier investments involve greater uncertainty: there are some possible scenarios – with some probability of occurring – that involve lower returns or even a loss.

Following Buffett and Munger, Marks definesrisk as the potential for permanent loss, which must be compared to the potential gain. Risk is not volatility per se, but the possibility of downward volatility where the price never rebounds.

Like other value investors, Marks believes that the lower the price you pay relative to intrinsic value the higher the potential return:

Theory says high return is associated with high risk because the former exists to compensate for the latter. But pragmatic value investors feel just the opposite: They believe high return and low risk can be achieved simultaneously by buying things for less than they’re worth. In the same way, overpaying implies both low return and high risk.

Dull, ignored, possibly tarnished and beaten-down securities – often bargains exactly because they haven’t been performing well – are often the ones value investors favor for high returns. Their returns in bull markets are rarely at the top of the heap, but their performance is generally excellent on average, more consistent than that of ‘hot’ stocks and characterized by low variability, low fundamental risk and smaller losses when markets do badly. (pages 47-48)

Risk ultimately is a subjective measure, says Marks. People have different time horizons and different concerns (for instance, worried about trailing a benchmark versus worried about a permanent loss). Marks quotes Graham and Dodd:

…the relation between different kinds of investments and the risk of loss is entirely too indefinite, and too variable with changing conditions, to permit of sound mathematical formulation.

Risk is just as uncertain after the fact, notes Marks:

A few years ago, while considering the difficulty of measuring risk prospectively, I realized that because of its latent, nonquantitative and subjective nature, the risk of an investment – defined as the likelihood of loss – can’t be measured in retrospect any more than it can a priori.

Let’s say you make an investment that works out as expected. Does that mean it wasn’t risky? Maybe you buy something for $100 and sell it a year later for $200. Was it risky? Who knows? Perhaps it exposed you to great potential uncertainties that didn’t materialize. Thus, its real riskiness might have been high. Or let’s say the investment produces a loss. Does that mean it was risky? Or that it should have been perceived as risky at the time it was analyzed and entered into?

If you think about it, the response to these questions is simple: The fact that something – in this case, loss – happened, doesn’t mean it was bound to happen, and the fact that something didn’t happen doesn’t mean it was unlikely. (page 50)

It’s essential to model the future based on possible scenarios:

The possibility of a variety of outcomes means we mustn’t think of the future in terms of a single result but rather as a range of possibilities. The best we can do is fashion a probability distribution that summarizes the possibilities and describes their relative likelihood. We must think about the full range, not just the ones that are most likely to materialize. (page 52)

Many investors make two related mistakes:

• Assuming that the most likely scenario is certain;
• Not imagining all possible scenarios, even highly unlikely ones (whether good or bad).

Marks describes investment results as follows:

For the most part, I think it’s fair to say that investment performance is what happens when a set of developments – geopolitical, macro-economic, company-level, technical and psychological – collide with an extent portfolio. Many futures are possible, to paraphrase Dimson, but only one future occurs. The future you get may be beneficial to your portfolio or harmful, and that may be attributable to your foresight, prudence or luck. (page 54)

Marks refers to Nassim Taleb’s concept of “alternative histories.” How your portfolio performs under the scenario that actually unfolds doesn’t tell you how it would have done under other possible scenarios.

As humans, we are subject to a set of related cognitive biases. See: https://boolefund.com/cognitive-biases/

Hindsight bias causes us to view the past as much more predictable than it actually was. The brain changes its own memories:

• If some possible event actually happens, our brains tend to think, “I always thought that was likely.”
• If some possible event doesn’t happen, our brains tend to think, “I always thought that was unlikely.“

The fact that we view the past as more predictable than it actually was makes as view the future as more predictable than it actually is. We feel comforted – and usually overconfident – because of our tendency to view both future and past as more predictable than they actually are.

Hindsight bias not only makes us overconfident about the future. It also feeds into confirmation bias, which causes us to search for, remember, and interpret information in a way that confirms our pre-existing beliefs or hypotheses.

Thus, one of the most important mental habits for us to develop – as investors and in general – is always to seek disconfirming evidence for our hypotheses. The more we like a hypothesis, the more important it is to look for disconfirming evidence.

Charlie Munger mentions Charles Darwin in “The Psychology of Human Misjudgment” (seePoor Charlie’s Alamanack: The Wit and Wisdom of Charles T. Munger, expanded 3^rdedition):

One of the most successful users of an antidote to first conclusion bias was Charles Darwin. He trained himself, early, to intensively consider any evidence tending to disconfirm any hypothesis of his, more so if he thought his hypothesis was a particularly good one…He provides a great example of psychological insight correctly used to advance some of the finest mental work ever done.

Munger sums up the lesson thus:

Any year in which you don’t destroy a best-loved idea is probably a wasted year.

 

RECOGNIZING RISK

Marks:

Recognizing risk often starts with understanding when investors are paying it too little heed, being too optimistic and paying too much for an asset as a result. High risk, in other words, comes primarily from high prices. Whether it be an individual security or other asset that is overrated and thus overpriced, or an entire market that’s been borne aloft by bullish sentiment and thus is sky-high, participating when prices are high rather than shying away is the main source of risk. (page 58)

Marks interjects a comment:

Too-high prices come from investor psychology that’s too positive, and too-high investor sentiment often stems from a dearth of risk aversion. Risk-averse investors are conscious of the potential for loss and demand compensation for bearing it – in the form of reasonable prices. When investors aren’t sufficiently risk-averse, they’ll pay prices that are too high. (page 59)

Christopher Davis points out that there are more traffic fatalities among drivers and passengers of SUVs. Because drivers of SUVs feel safer, they drive riskier. Most of us, as investors, feel more confident and less worried when prices have been going up for an extended period. Since prices that are too high are the main source of investment risk, we have to learn how to overcome our psychological tendencies. Marks elucidates:

The risk-is-gone myth is one of the most dangerous sources of risk, and a major contributor to any bubble. At the extreme of the pendulum’s upswing, the belief that risk is low and that the investment in question is sure to produce profits intoxicates the herd and causes its members to forget caution, worry, and fear of loss, and instead to obsess about the risk of missing opportunity. (page 62)

Marks again:

Investment risk comes primarily from too-high prices, and too-high prices often come from excessive optimism and inadequate skepticism and risk aversion. Contributing underlying factors can include low prospective returns on safer investments, recent good performance by risky ones, strong inflows of capital, and easy availability of credit. The key lies in understanding what impact things like these are having. (page 63)

Investors generally overvalue what seems to have low risk, while undervaluing what seems to have high risk:

• When everyone believes something is risky, their unwillingness to buy usually reduces its price to the point where it’s not risky at all. Broadly negative opinion can make it the least risky thing, since all optimism has been driven out of its price.
• And, of course, as demonstrated by the experience of Nifty Fifty investors, when everyone believes something embodies no risk, they usually bid it up to the point where it’s enormously risky. No risk is feared, and thus no reward for risk-bearing – no ‘risk premium’ – is demanded or provided. That can make the thing that’s most esteemed the riskiest. (page 69)

The reason for this paradox, says Marks, is that most investors believe that quality, not price, determines whether an asset is risky. However, low quality assets can be safe if their prices are low enough, while high quality assets can be risky if their prices are too high. Chris Davis adds:

I agree – there are a number of dangers that come from using a term like ‘quality.’ First, investors tend to equate ‘high-quality asset’ with ‘high-quality investment.’ As a result, there’s an incorrect presumption or implication of less risk when taking on ‘quality’ assets. As Marks rightly points out, quite often ‘high-quality’ companies sell for high prices, making them poor investments. Second, ‘high-quality’ tends to be a phrase that incorporates a lot of hindsight bias or ‘halo effect.’ Usually, people referring to a ‘high-quality’ company are describing a company that has performed very well in the past. The future is often quite different. There is a long list of companies that were once described as ‘high quality’ or ‘built to last’ that are no longer around! For this reason, investors should avoid using the word ‘quality.’ (pages 69-70)

 

CONTROLLING RISK

Risk control is generally invisible during good times. But that doesn’t mean it isn’t desirable, says Marks. No one can consistently predict the timing of bull markets or bear markets. Therefore, risk control is always important, even during long bull markets. Marks:

Bearing risk unknowingly can be a huge mistake, but it’s what those who buy the securities that are all the rage and most highly esteemed at a particular point in time – to which ‘nothing bad can possibly happen’ – repeatedly do. On the other hand, intelligent acceptance of recognized risk for profit underlies some of the wisest, most profitable investments – even though (or perhaps due to the fact that) most investors dismiss them as dangerous speculations. (page 75)

Marks later writes:

Even if we realize that unusual, unlikely things can happen, in order to act we make reasoned decisions and knowingly accept that risk when well paid to do so. Once in a while, a ‘black swan’ will materialize. But if in the future we always said, ‘We can’t do such-and-such, because the outcome could be worse than we’ve ever seen before,’ we’d be frozen in inaction. (page 79)

You can’t avoid risk altogether as an investor or you’d get no return. Therefore, you have to take risks intelligently, when you’re well paid to do so. Marks concludes:

Over a full career, most investors’ results will be determined more by how many losers they have, and how bad they are, than by the greatness of their winners. (page 80)

Daniel Pecaut and Corey Wrenn, inThe University of Berkshire Hathaway,point out a central fact about how Buffett and Munger have achieved such a remarkable track record:

More than two-thirds of Berkshire’s performance over the S&P was earned during down years. This is the fruit of Buffett and Munger’s ‘Don’t lose’ philosophy. It’s the losing ideas avoided, as much as the money made in bull markets that has built Berkshire’s superior wealth over the long run.(page xxi)

Buffett has made the same point. His best ideas have not outperformed the best ideas of other great value investors. However, his worst ideas have not been as bad, and have lost less over time, as compared with the worst ideas of other top value investors.

See:https://boolefund.com/university-berkshire-hathaway/

 

BEING ATTENTIVE TO CYCLES

Marks explains how the credit cycle works when times are good:

• The economy moves into a period of prosperity.
• Providers of capital thrive, increasing their capital base.
• Because bad news is scarce, the risks entailed in lending and investing seem to have shrunk.
• Risk averseness disappears.
• Financial institutions move to expand their businesses – that is, to provide more capital.
• They compete for market share by lowering demanded returns (e.g., cutting interest rates), lowering credit standards, providing more capital for a given transaction and easing covenants. (page 83)

This is a cyclical process. Overconfidence based on recent history leads to the disappearance of risk aversion. Providers of capital make bad loans. This causes the cycle to reverse:

• Losses cause lenders to become discouraged and shy away.
• Risk averseness rises, and along with it, interest rates, credit restrictions and covenant requirements.
• Less capital is made available – and at the trough of the cycle, only to the most qualified of borrowers, if anyone.
• Companies become starved for capital. Borrowers are unable to roll over their debts, leading to defaults and bankruptcies.
• This process contributes to and reinforces the economic contraction. (page 84)

People and financial institutions become overly pessimistic based on recent history, which leads to excessive risk aversion. Many solid loans are not made. This causes the cycle to reverse again.

Marks, in agreement with Lakonishok, Shleifer, and Vishny (1994), explains why value investing can continue to work:

Investors will overvalue companies when they’re doing well and undervalue them when things get difficult. (page 86)

Marks:

When things are going well, extrapolation introduces great risk. Whether it’s company profitability, capital availability, price gains, or market liquidity, things that inevitably are bound to regress toward the mean are often counted on to improve forever. (page 87)

It’s important to point out that there can be structural changes in the economy and the stock market. For instance, interest rates may stay relatively low for a long time, in which case stocks may even be cheap today (with the S&P 500 Index over 2400).

Also, profit margins may be structurally higher:

• There is a good Barron’s interview of Bruce Greenwald, “Channeling Graham and Dodd”. Professor Greenwald indicated that Apple, Alphabet, Microsoft, Amazon, and Facebook – the five largest U.S. companies – have far higher normalized profit margins and ROE, as a group, than most large U.S. companies in history.
• In brief, software and related technologies are becoming much more important in the global economy. This is another key reason why U.S. stocks may not be overvalued, and may even be cheap. See: http://www.barrons.com/articles/bruce-greenwald-channeling-graham-and-dodd-1494649404

 

COMBATING NEGATIVE INFLUENCES

Marks discusses the importance of psychology:

The desire for more, the fear of missing out, the tendency to compare against others, the influence of the crowd and the dream of a sure thing – these factors are near universal. Thus they have a profound collective impact on most investors and most markets. The result is mistakes, and those mistakes are frequent, widespread, and recurring. (page 97)

Marks observes that the biggest mistakes in investing are not analytical or informational, but psychological. At the extremes, people get too greedy or too fearful:

Greed is an extremely powerful force. It’s strong enough to overcome common sense, risk aversion, prudence, caution, logic, memory of painful past lessons, resolve, trepidation, and all the other elements that might otherwise keep investors out of trouble. Instead, from time to time greed drives investors to throw in their lot with the crowd in pursuit of profit, and eventually they pay the price.

The counterpart of greed is fear – the second psychological factor we must consider. In the investment world, the term doesn’t mean logical, sensible risk aversion. Rather fear – like greed – connotes excess. Fear, then, is more like panic. Fear is overdone concern that prevents investors from taking constructive action when they should. (page 99)

The third factor Marks mentions is the willing suspension of disbelief. We are all prone to overconfidence, and in general, we think we’re better than we actually are. Charlie Munger quotes Demosthenes:

Nothing is easier than self-deceit. For what each man wishes, that he also believes to be true.

Or as the physicist Richard Feynman put it:

The first principle is that you must not fool yourself, and you are the easiest person to fool.

Marks later quotes Warren Buffett’s remark to Congress on June 2, 2010:

Rising prices are a narcotic that affects the reasoning power up and down the line.

The fourth psychological tendency is conformity with the crowd. Swarthmore’s Solomon Asch conducted a famous experiment in the 1950’s. The subject is shown two lines of obviously different lengths. There are a few other people – shills – pretending to be subjects.

All the participants are asked if the lines are the same length. (In fact, they obviously aren’t.) The shills all say yes. In a high percentage of the cases, the actual subject of the experiment disregards the obvious evidence of his own eyes and conforms to the view of the crowd.

So it is with the consensus view of the market. Most people simply go along with the view of the crowd. That’s not to say the crowd is necessarily wrong. Often the crowd is right when it comes to the stock market. But occasionally the crowd is very wrong about specific stocks, or even about the market itself.

The fifth psychological influence Marks notes is envy. As Buffett remarked, “It’s not greed that drives the world, but envy.” Munger has observed that envy is particularly stupid because there’s no upside. Buffett agrees, joking: “Gluttony is a lot of fun. Lust has its place, too, but we won’t get into that.” Marks:

People who might be perfectly happy with their lot in isolation become miserable when they see others do better. In the world of investing, most people find it terribly hard to sit by and watch while others make more money than they do. (page 102)

The sixth psychological influence is ego. Investment results are compared. In good times, aggressive and imprudent decisions often lead to the best results. And the best results bring the greatest ego rewards, observes Marks.

Finally, Marks highlights the phenomenon of capitulation. Consider the tech bubble in the late 90’s:

…The guy sitting next to you in the office tells you about an IPO he’s buying. You ask what the company does. He says he doesn’t know, but his broker told him it’s going to double on its day of issue. So you say that’s ridiculous. A week later he tells you it didn’t double… it tripled. And he still doesn’t know what it does. After a few more of these, it gets hard to resist. You know it doesn’t make sense, but you want protection against continuing to feel like an idiot. So, in a prime example of capitulation, you put in for a few hundred shares of the next IPO… and the bonfire grows still higher on the buying from converts like you. (page 106)

Technological innovation drives economic progress. But that doesn’t mean every innovative company is a good investment. Joel Greenblatt comments:

Buffett’s famous line about the economics of airlines comes to mind. Aviation is a huge and valuable innovation. That’s not the same thing as saying it’s a good business. (page 108)

 

CONTRARIANISM

Sir John Templeton:

To buy when others are despondently selling and to sell when others are euphorically buying takes the greatest courage, but provides the greatest profit.

Most investors are basically trend followers, writes Marks. This works as long as the trend continues.

Marks quotes David Swensen’s Pioneering Portfolio Management (2000):

Investment success requires sticking with positions made uncomfortable by their variance with popular opinion. Casual commitments invite casual reversal, exposing portfolio managers to the damaging whipsaw of buying high and selling low. Only with the confidence created by a strong decision-making process can investors sell speculative excess and buy despair-driven value.

…Active management strategies demand uninstitutional behavior from institutions, creating a paradox that few can unravel. Establishing and maintaining an unconventional investment profile requires acceptance of uncomfortably idiosyncratic portfolios, which frequently appear downright imprudent in the eyes of conventional wisdom. (page 115)

Marks sums it up:

The ultimately most profitable investment actions are by definition contrarian: you’re buying when everyone else is selling (and thus the price is low) or you’re selling when everyone else is buying (and thus the price is high). (page 116)

Marks concludes:

The one thing I’m sure of is that by the time the knife has stopped falling, the dust has settled and the uncertainty has been resolved, there’ll be no great bargains left. When buying something has become comfortable again, its price will no longer be so low that it’s a great bargain.

Thus, a hugely profitable investment that doesn’t begin with discomfort is usually an oxymoron.

It’s our job as contrarians to catch falling knives, hopefully with care and skill. That’s why the concept of intrinsic value is so important. If we hold a view of value that enables us to buy when everyone else is selling – and if our view turns out to be right – that’s the route to the greatest rewards earned with the least risk. (page 121)

It’s important to emphasize again what can happen when certain assets become widely ignored or despised: The lower the price goes below probable intrinsic value, the lower the risk and the higher the reward. For value investors, some of the highest-returning investments can simultaneously have the lowest risk. Modern finance theory regards this situation as impossible. According to modern finance, higher rewardsalways require higher risk.

 

FINDING BARGAINS

Marks repeats an important concept:

Our goal isn’t to find good assets, but good buys. Thus, it’s not what you buy; it’s what you pay for it.

A high-quality asset can constitute a good or bad buy, and a low-quality asset can constitute a good or bad buy. The tendency to mistake objective merit for investment opportunity, and the failure to distinguish between good assets and good buys, get most investors into trouble. (pages 124-125)

What creates bargains? Marks answers:

• Unlike assets that become the subject of manias, potential bargains usually display some objective defect. An asset class may have weaknesses, a company may be a laggard in its industry, a balance sheet may be over-levered, or a security may afford its holders inadequate structural protection.
• Since the efficient-market process of setting fair prices requires the involvement of people who are analytical and objective, bargains usually are based on irrationality or incomplete understanding. Thus, bargains are often created when investors either fail to consider an asset fairly, or fail to look beneath the surface to understand it thoroughly, or fail to overcome some non-value-based tradition, bias or stricture.
• Unlike market darlings, the orphan asset is ignored or scorned. To the extent it’s mentioned at all by the media and at cocktail parties, it’s in unflattering terms.
• Usually its price has been falling, making the first-level thinker ask, ‘Who would want to own that?’ (It bears repeating that most investors extrapolate past performance, expecting the continuation of trends rather than the far-more-dependable regression to the mean. First-level thinkers tend to view past price weakness as worrisome, not as a sign that the asset has gotten cheaper.)
• As a result, a bargain asset tends to be one that’s highly unpopular. Capital stays away from it or flees, and no one can think of a reason to own it. (pages 125-126)

Marks continues by explaining that to find an undervalued asset, a good place to start looking is among things that are:

• little known and not fully understood;
• fundamentally questionable on the surface;
• controversial, unseemly or scary;
• deemed inappropriate for ‘respectable’ portfolios;
• unappreciated, unpopular and unloved;
• trailing a record of poor returns; and
• recently the subject of disinvestment, not accumulation. (pages 127-128)

In brief:

To boil it all down to just one sentence, I’d say the necessary condition for the existence of bargains is that perception has to be considerably worse than reality. That means the best opportunities are usually found among things most others won’t do. (page 128)

Seth Klarman:

Generally, the greater the stigma or revulsion, the better the bargain.

 

PATIENT OPPORTUNISM

Buffett has always stressed that, over time, you should compare the results of what you do as an investor to what would have happened had you done absolutely nothing. Often the best thing to do as a long-term value investor is absolutely nothing.

Buffett has also observed that investing is like baseball except that in investing, there are no called strikes. You can wait for as long as it takes until a fat pitch appears. Absent a fat pitch, there’s no reason to swing. Buffett mentioned in Berkshire Hathaway’s 1997 Letter to Shareholders that Ted Williams, one of the greatest hitters ever, studied his hits and misses carefully. Williams broke the strike zone into 77 baseball-sized ‘cells’ and analyzed his results accordingly. Buffett explained:

Swinging only at balls in his ‘best’ cell, he knew, would allow him to bat .400; reaching for balls in his ‘worst’ spot, the low outside corner of the strike zone, would reduce him to .230. In other words, waiting for the fat pitch would mean a trip to the Hall of Fame; swinging indiscriminately would mean a ticket to the minors.

See:http://berkshirehathaway.com/letters/1997.html

 

KNOWING WHAT YOU DON’T KNOW

John Kenneth Galbraith:

There are two classes of forecasters: Those who don’t know – and those who don’t know they don’t know.

Marks studied forecasts. Some forecasters always extrapolate the recent past. But that’s not useful. Outside of that, there are virtually no forecasters who are both non-consensus and regularly correct. Marks writes:

One way to get to be right sometimes is to always be bullish or always be bearish; if you hold a fixed view long enough, you may be right sooner or later. And if you’re always an outlier, you’re likely to eventually be applauded for an extremely unconventional forecast that correctly foresaw what no one else did. But that doesn’t mean your forecasts are regularly of any value…

It’s possible to be right about the macro-future once in a while, but not on a regular basis. It doesn’t do any good to possess a survey of sixty-four forecasts that includes a few that are accurate; you have to know which ones they are. And if the accurate forecasts each six months are made by different economists, it’s hard to believe there’s much value in the collective forecasts. (page 145)

Marks restates the case in the following points:

• Most of the time, people predict a future that is a lot like the recent past.
• They’re not necessarily wrong: most of the time, the future largely is a rerun of the recent past.
• On the basis of these two points, it’s possible to conclude that forecasts will prove accurate much of the time: They’ll usually extrapolate recent experience and be right.
• However, the many forecasts that correctly extrapolate past experience are of little value. Just as forecasters usually assume a future that’s a lot like the past, so do markets, which usually price in a continuation of recent history. Thus if the future turns out to be like the past, it’s unlikely big money will be made, even by those who foresaw correctly that it would.
• Once in a while, however, the future turns out to be very different from the past.
• It’s at these times that accurate forecasts would be of great value.
• It’s also at these times that forecasts are least likely to be correct.
• Some forecasters may turn out to be correct at these pivotal moments, suggesting that it’s possible to correctly forecast key events, but it’s unlikely to be the same people consistently.
• The sum of this discussion suggests that, on balance, forecasts are of very little value. (pages 145-146)

As an example, Marks asks who correctly predicted the credit crisis and bear market of 2007-2008. Of those who correctly predicted it, how many of them also correctly predicted the recovery and massive bull market starting in 2009? Very, very few. Marks:

…Those who got 2007-2008 right probably did so at least in part because of a tendency toward negative views. As such, they probably stayed negative for 2009. The overall usefulness of those forecasts wasn’t great… even though they were partially right about some of the most momentous financial events in the last eighty years.

So the key question isn’t ‘are forecasts sometimes right?’ but rather ‘are forecasts as a whole – or any one person’s forecasts – consistently actionable and valuable?’ No one should bet much on the answer being affirmative.

Marks then notes that you could have found some people predicting a bear market before 2007-2008. But if these folks had a negative bias, as well as a track record full of incorrect predictions, then you wouldn’t have had much reason to listen. Or as Buffett put it in the Berkshire Hathaway 2016 Letter to Shareholders:

American business – and consequently a basket of stocks – is virtually certain to be worth far more in the years ahead. Innovation, productivity gains, entrepreneurial spirit and an abundance of capital will see to that. Ever-present naysayers may prosper by marketing their gloomy forecasts. But heaven help them if they act on thenonsense they peddle. (page 6)

See:http://berkshirehathaway.com/letters/2016ltr.pdf

Marks has a description for investors who believe in the value of forecasts. They belong to the “I know” school, and it’s easy to identify them:

• They think knowledge of the future direction of economies, interest rates, markets and widely followed mainstream stocks is essential for investment success.
• They’re confident it can be achieved.
• They know they can do it.
• They’re aware that lots of other people are trying to do it too, but they figure either (a) everyone can be successful at the same time, or (b) only a few can be, but they’re among them.
• They’re comfortable investing based on their opinions regarding the future.
• They’re also glad to share their views with others, even though correct forecasts should be of such great value that no one would give them away gratis.
• They rarely look back to rigorously assess their record as forecasters. (page 147)

Marks contrasts theconfident“I know” folks with theguarded“I don’t know” folks. The latter believe you can’t predict the macro-future, and thus the proper goal for investing is to do the best possible job analyzing individual securities. If you belong to the “I don’t know” school, eventually everyone will stop asking you where you think the market’s going.

You’ll never get to enjoy that one-in-a-thousand moment when your forecast comes true and theWall Street Journalruns your picture. On the other hand, you’ll be spared all those times when forecasts miss the mark, as well as the losses that can result from investing based on overrated knowledge of the future. (page 148)

Marks continues by noting that no one likes investing on the assumption that the future is unknowable. But if the futureISlargely unknowable, then it’s far better as an investor to acknowledge that fact than to pretend otherwise.

Furthermore, says Marks, the biggest problems for investors tend to happen when investors forget the difference between probability and outcome (i.e., the limits of foreknowledge):

• when they believe the shape of the probability distribution is knowable with certainty (and that they know it),
• when they assume the most likely outcome is the one that will happen,
• when they assume the expected result accurately represents the actual result, or
• perhaps most important, when they ignore the possibility of improbable outcomes. (pages 148-149)

Marks sums it up:

Overestimating what you’re capable of knowing or doing can be extremely dangerous – in brain surgery, transocean racing or investing. Acknowledging the boundaries of what you can know – and working within those limits rather than venturing beyond – can give you a great advantage. (page 150)

 

APPRECIATING THE ROLE OF LUCK

Professor Paul Johnson explains the main point:

Learn to be honest with yourself about your successes and failures [as an investor]. Learn to recognize the role luck has played in all outcomes. Learn to decide which outcomes came because of skill and which because of luck. Until one learns to identify the true source of success, one will be fooled by randomness. (page 161)

Once again, we consider Nassim Taleb’s concept of “alternative histories.” Marks quotes Taleb:

Clearly my way of judging matters is probabilistic in nature; it relies on the notion of what could have probably happened…

If we have heard of [history’s great generals and inventors], it is simply because they took considerable risks, along with thousands of others, and happened to win. They were intelligent, courageous, noble (at times), had the highest possible obtainable culture in their day – but so did thousands of others who live in the musty footnotes of history. (pages 162-163)

In investing, you probably need many decades of results before you can determine how much is due to skill. And here we’re talking mainly about long-term value investing, where stocks are held for at least a year on average.

Similarly, to judge individual investment decisions, you have to know much more than whether a specific decision worked or not. You have to understand the process by which the investor made the decision. You have to know which facts were available and which were used in the decision. You have to estimate the probability of success of the investment decision, whether or not it actually worked. This means you have to account for all the possible scenarios that could have unfolded, not just the one scenario that did unfold.

Marks gives the example of backgammon. A certain aggressive player may need to roll double sixes in order to win. The probability of that happening is one out of thirty-six. Say the player accepts the cube – doubling the stakes – and gets his boxcars. Many will consider the player brilliant. But was it a wise bet?

You could find similar situations in other games of chance, such as bridge or poker. There are many situations in which you can calculate the probabilities of various scenarios. So you can figure out if the player is making the most profitable decision, averaged out over time. Some percentage of the time the decision will work. Some percentage of the time it won’t. A skillful player will consistently make the the most profitable long-term decision.

Value investing is similar. Good value investors are right 60% of the time and wrong 40% of the time. If their process for selecting cheap stocks is solid, then risks and losses will be minimized while gains are simultaneously maximized.

Marks writes:

The actions of the ‘I know’ school are based on a view of a single future that is knowable and conquerable. My ‘I don’t know’ school thinks of future events in terms of a probability distribution. That’s a big difference. In the latter case, we may have an idea which outcome is most likely to occur, but we also know there are many other possibilities, and those other outcomes may have a collective likelihood much higher than the one we consider most likely. (page 168)

As Buffett advised, we have to focus on what’s knowable and important. That means focusing on individual companies and industries within our circle of competence. Many companies may be beyond our ability to value. They go in the “too hard” pile. Focus on those companies we can understand and value.

 

INVESTING DEFENSIVELY

As in some sports, in investing you have to decide if you want to emphasize offense, emphasize defense, or use a balanced approach. Marks:

…investors should commit to an approach – hopefully one that will serve them through a variety of scenarios. They can be aggressive, hoping they’ll make a lot on the winners and not give it back on the losers. They can emphasize defense, hoping to keep up in good times and excel by losing less than others in bad times. Or they can balance offense and defense, largely giving up on tactical timing but aiming to win through superior security selection in both up and down markets. (page 174)

The vast majority of investors should invest in quantitative value funds or in low-cost broad market index funds. Most of us will probably maximize our multi-decade results using one or both of these approaches. Buffett:https://boolefund.com/warren-buffett-jack-bogle/

Regarding the balance of offense versus defense, Marks observes:

And, by the way, there’s no right choice between offense and defense. Lots of possible routes can bring you to success, and your decision should be a function of your personality and leanings, the extent of your belief in your ability, and the peculiarities of the markets you work in and the clients you work for. (page 175)

Marks believes that a focus on avoiding losses will lead more dependably to consistently good returns over time. As we noted earlier, Buffett has said that his best ideas have not outperformed the best ideas of other value investors; but his worst ideas have not done as poorly as the worst ideas of other value investors. So minimizing losses – especially avoiding big losses – has been central to Buffett becoming arguably the best value investor of all time.

 

REASONABLE EXPECTATIONS

Setting reasonable expectations can play a pivotal role in designing and applying your investment strategy. Marks points out that you can’t simply think about high returns without also considering risk. In investing, if you aim too high, you’ll end up taking too much risk.

Similarly, when buying assets that are declining in price, you should have a reasonable strategy. Marks:

I try to look at it logically. There are three times to buy an asset that has been declining: on the way down, at the bottom, or on the way up. I don’t believe we ever know when the bottom has been reached, and even if we did, there might not be much for sale there.

If we wait until the bottom has passed and the price has started to rise, the rising price often causes others to buy, just as it emboldens holders and encourages them from selling. Supply dries up and it becomes hard to buy in size. The would-be buyer finds it’s too late.

That leaves buying on the way down, which we should be glad to do. The good news is that if we buy while the price is collapsing, that fact alone often causes others to hide behind the excuse that ‘it’s not our job to catch falling knives.’ After all, it’s when knives are falling that the greatest bargains are available.

There’s an important saying attributed to Voltaire: ‘The perfect is the enemy of the good.’ This is especially applicable to investing, where insisting on participating only when conditions are perfect – for example, buying only at the bottom – can cause you to miss out on a lot. Perfection in investing is generally unobtainable; the best we can hope for is to make a lot of good investments and exclude most of the bad ones. (page 212)

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here:https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

February 6, 2022

Today we review some of the central concepts in value investing.  In order to learn, some repetition is required, especially when the subject may be difficult or counter-intuitive for many.

Here’s the outline:

• Index Funds or Quant Value Funds
• The Dangers of DCF
• Notes on Ben Graham
• Value vs. Growth
• The Superinvestors of Graham-and-Doddsville

 

INDEX FUNDS OR QUANT VALUE FUNDS

The first important point is that the vast majority of investors are best off buying and holding a broad market, low-cost index fund.  Warren Buffett has repeatedly made this observation.  See: https://boolefund.com/warren-buffett-jack-bogle/

In other words, most of us who believe that we can outperform the market over the long term (decades) are wrong.  The statistics on this point are clear.  For instance, see pages 21-25 of Buffett’s 2016 Berkshire Hathaway Shareholder Letter: http://berkshirehathaway.com/letters/2016ltr.pdf

A quantitative value investment strategy””especially if focused on micro caps””is likely to do better than an index fund over time.  If you understand why this is the case, then you could adopt such an approach, at least for part of your portfolio.  (The Boole Microcap Fund is a quantitative value fund.)  But you have to be able to stick with it over the long term even though there will sometimes be multi-year periods of underperforming the market.  Easier said than done.  Know Thyself.

We all like to think we know ourselves.  But in many ways we know ourselves much less than we believe we do.  This is especially true when it comes to probabilistic decisions or complex computations.  In these areas, we suffer from cognitive biases which generally cause us to make suboptimal or erroneous choices.  See: https://boolefund.com/cognitive-biases/

The reason value investing””if properly implemented””works over time is due to the behavioral errors of many investors.  Lakonishok, Shleifer, and Vishny give a good explanation of this in their 1994 paper, “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

Lakonishok, Shleifer, and Vishny (LSV) offer three reasons why investors follow “naive” strategies:

• Investors often extrapolate high past earnings growth too far into the future.  Similarly, investors extrapolate low past earnings growth too far into the future.
• Investors overreact to good news and to bad news.
• Investors think a well-run company is automatically a good investment.

LSV then state that, for whatever reason, investors overvalue stocks that have done well in the past, causing these “glamour” or “growth” stocks to be overpriced in general.  Similarly, investors undervalue stocks that have done poorly in the past, causing these “value” stocks to be underpriced in general.

Important Note:  Cognitive biases””such as overconfidence, confirmation bias, and hindsight bias””are the main reason why investors extrapolate past trends too far into the future.  For simple and clear descriptions of cognitive biases, see: https://boolefund.com/cognitive-biases/

 

THE DANGERS OF DCF

For most businesses, it’s very difficult””and often impossible””to predict future earnings and free cash flows.  One reason Warren Buffett and Charlie Munger have produced such an outstanding record at Berkshire Hathaway is because they focus on businesses that are highly predictable.  These types of businesses usually have a sustainable competitive advantage, which is what makes their future earnings and cash flows more certain.  As Buffett put it:

The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage.

Most businesses do not have a sustainable competitive advantage, and thus are not predictable 5 or 10 years into the future.

Buffett calls a sustainable competitive advantage a moat, which defends the economic “castle.”  Here’s how he described it at the Berkshire Hathaway Shareholder Meeting in 2000:

So we think in terms of that moat and the ability to keep its width and its impossibility of being crossed as the primary criterion of a great business.  And we tell our managers we want the moat widened every year.  That doesn’t necessarily mean the profit will be more this year than it was last year because it won’t be sometimes.  However, if the moat is widened every year, the business will do very well.  When we see a moat that’s tenuous in any way – it’s just too risky.  We don’t know how to evaluate that.  And, therefore, we leave it alone.  We think that all of our businesses – or virtually all of our businesses – have pretty darned good moats.

There’s a great book, The Art of Value Investing (Wiley, 2013), by John Heins and Whitney Tilson, which is filled with quotes from top value investors.  Here’s a quote from Bill Ackman, which shows that he strives to invest like Buffett and Munger:

We like simple, predictable, free-cash-flow generative, resilient and sustainable businesses with strong profit-growth opportunities and/or scarcity value.  The type of business Warren Buffett would say has a moat around it.  (page 131)

If the future earnings and cash flows of a business are not predictable, then DCF valuation may not be very reliable.  Moreover, it’s often hard to calculate the cost of capital (the discount rate).

• DCF refers to “discounted cash flows.”  You can value any business if you can estimate future free cash flow with reasonable accuracy.  To get the present value of the business, the free cash flow in each future year must be discounted back to the present by the cost of capital.

To determine the cost of capital, Buffett and Munger use the opportunity cost of capital, which is the next best investment with a similar level of risk.

• To illustrate, say they’re considering an investment in Company A, which they feel quite certain will return 15% per year.  To figure out the value of this potential investment, they will find their next best investment – which they may already own – that has a similar level of risk.  Perhaps they own Company N and they feel equally certain that its future returns will be 17% per year.  In that case, if possible, they would prefer to buy more of Company N rather than buying any of Company A.  (Often there are other considerations.  But that’s the gist of it.)

The academic definition of cost of capital includes “beta,” which measures how volatile a stock price has been in the past.  But for value investors like Buffett and Munger, all that matters is how much free cash flow the business will produce in the future.  The degree of volatility of a stock in the past generally has no logical relationship with the next 20-30 years of cash flows.

• To see Buffett’s argument against “beta,” see page 14 (out of 15) in The Superinvestors of Graham-and-Doddsville: http://www8.gsb.columbia.edu/rtfiles/cbs/hermes/Buffett1984.pdf

If a business lacks a true moat and if, therefore, DCF probably won’t work, is there any other way to evaluate a business?  James Montier, in Value Investing (Wiley, 2009), mentions three alternatives to DCF that do not require forecasting:

• Reverse-engineered DCF
• Asset Value
• Earnings Power

In a reverse-engineered DCF, instead of forecasting future growth, you take the current share price and figure out what that implies about future growth.  Then you compare the implied future growth of the business against some reasonable benchmark, like growth of a close competitor.  (You still have to determine a cost of capital.)

As for asset value and earnings power, these were the two methods of valuation suggested by Ben Graham.  For asset value, Graham often suggested using liquidation value, which is usually a conservative estimate of asset value.  If the business could be sold as a going concern, then the assets would probably have a higher value than liquidation value.

Regarding earnings power, Montier quotes Graham from Security Analysis:

What the investor chiefly wants to learn… is the indicated earnings power under the given set of conditions, i.e., what the company might be expected to earn year after year if the business conditions prevailing during the period were to continue unchanged.

Montier again quotes Graham:

It combines a statement of actual earnings, shown over a period of years, with a reasonable expectation that these will be approximated in the future, unless extraordinary conditions supervene.  The record must be over a number of years, first because a continued or repeated performance is always more impressive than a single occurrence, and secondly because the average of a fairly long period will tend to absorb and equalize the distorting influences of the business cycle.

Montier mentions Bruce Greenwald’s excellent book, Value Investing: From Graham to Buffett and Beyond (Wiley, 2004), for a modern take on asset value and earnings power.

 

NOTES ON BEN GRAHAM

When studying Graham’s methods as presented in Security Analysis””first published in 1934″”it’s important to bear in mind that Graham invented value investing during the Great Depression.  Therefore, some of Graham’s methods are arguably overly conservative.  Particularly if you think the Great Depression was caused in part by mistakes in fiscal and monetary policy that are unlikely to be repeated.  Charlie Munger put it as follows:

I don’t love Ben Graham and his ideas the way Warren does.  You have to understand, to Warren””who discovered him at such a young age and then went to work for him””Ben Graham’s insights changed his whole life, and he spent much of his early years worshiping the master at close range.

But I have to say, Ben Graham had a lot to learn as an investor.  His ideas of how to value companies were all shaped by how the Great Crash and the Depression almost destroyed him, and he was always a little afraid of what the market can do.  It left him with an aftermath of fear for the rest of his life, and all his methods were designed to keep that at bay.

That being said, Warren Buffett has always maintained that Chapters 8 and 20 of Ben Graham’s The Intelligent Investor””first published in 1949″”contain the three fundamental precepts of value investing:

• Owning stock is part ownership of the underlying business.
• Market prices are there to serve you, not to instruct you.  When prices drop a great deal, it may be a good opportunity to buy.  When prices rise quite a bit, it may be a good time to sell.  At all other times, it’s best to focus on the operating results of the businesses you own.
• The margin of safety is the difference between the price you pay and your estimate of the intrinsic value of the business.  Price is what you pay;  value is what you get.  If you think the business is worth $40 per share, then you would like to pay $20 per share.  (Value investors refer to a stock that’s selling for half its intrinsic value as a “50-cent dollar.”)

The purpose of the margin of safety is to minimize the effects of bad luck, human error, and the vagaries of the future.  Good value investors are right about 60% of the time and wrong 40% of the time.  By systematically minimizing the impact of inevitable mistakes and bad luck, a solid value investing strategy will beat the market over time.  Why?

Here’s why:  As you increase your margin of safety, you simultaneously increase your potential return.  The lower the risk, the higher the potential return.  When you’re wrong, you lose less on average.  When you’re right, you make more on average.

For instance, assume again that you estimate the intrinsic value of the business at $40 per share.

• If you can pay $20 per share, then you have a good margin of safety.  And if you are right about intrinsic value, then you will make 100% on your investment when the price eventually moves from $20 to $40.
• What if you managed to pay $10 per share for the same stock?  Then you have an even larger margin of safety relative to the estimated intrinsic value of $40.  As well, if you’re right about intrinsic value, then you will make 300% on your investment when the price eventually moves from $10 to $40.

The notion that you can increase your safety and your potential returns at the same time runs directly contrary to what is commonly taught in modern finance.  In modern finance, you can only increase your potential return by increasing your risk.

A final point about Buffett and Munger’s evolution as investors.  Munger realized early in his career that it was better to buy a high-quality business at a reasonable price, rather than a low-quality business at a cheap price.  Buffett also realized this””partly through Munger’s influence””after experiencing a few failed investments in bad businesses purchased at cheap prices.  Ever since, Buffett and Munger have expressed the lesson as follows:

It’s better to buy a wonderful company at a fair price than a fair company at a wonderful price.

The idea is to pay a reasonable price for a company with a high ROE (return on equity) that can be sustained””due to a moat.  If you hold a high-quality business like this, then over time your returns as an investor will approximate the ROE.  High-quality businesses can have sustainably high ROE’s that range from 20% to over 100%.

Note:  Buffett and Munger also insist that the companies they invest in have low debt (or no debt).  Even a great business can fail if it has too much debt.

 

VALUE vs. GROWTH

One of the seminal academic papers on value investing””which was mentioned earlier””is Lakonishok, Shleifer, and Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

Lakonishok, Shleifer, and Vishny (LSV) show that value investing””buying stocks at low multiples (P/B, P/CF, and P/E)””outperformed glamour (growth) investing by about 10-11% per year from 1968 to 1989.

Here’s why, say LSV:  Investors expect the poor recent performance of value stocks to continue, causing these stocks to trade at lower multiples than is justified by subsequent performance.  And investors expect the recent good performance of glamour stocks to continue, causing these stocks to trade at higher multiples than is justified by subsequent performance.

Interestingly, La Porta (1993 paper) shows that contrarian value investing based directly on analysts’ forecasts of future growth can produce even larger excess returns than value investing based on low multiples.  In other words, betting on the stocks for which analysts have the lowest expectations can outperform the market by an even larger margin.

Moreover, LSV demonstrate that value investing is not riskier.  First, excess returns from value investing cannot be explained by excess volatility.  Furthermore, LSV show that value investing does not underperform during market declines or recessions.  If anything, value investing outperforms during down markets, which makes sense because value investing involves paying prices that are, on average, far below intrinsic value.

In conclusion, LSV ask why countless investors continue to buy glamour stocks and to ignore value stocks.  One chief reason is that buying glamour stocks””generally stocks that have been doing well””may seem “prudent” to many professional investors.  After all, glamour stocks are unlikely to become financially distressed in the near future, whereas value stocks are often already in distress.

In reality, a basket of glamour stocks is not prudent because it will far underperform a basket of value stocks over a sufficiently long period of time.  However, if professional investors choose a basket of value stocks, then they will not only own many stocks experiencing financial distress, but they also risk underperforming for several years in a row.  These are potential career risks that most professional investors would rather avoid.  From that point of view, it may indeed be “prudent” to stick with glamour stocks, despite the lower long-term performance of glamour compared to value.

• An individual value stock is likely to be more distressed””and thus riskier””than either a glamour stock or an average stock.  But LSV have shown that value stocks, as a group, far outperform both glamour stocks and the market in general, and do so with less risk.  This finding that value stocks, as a group, outperform has been confirmed by many academic studies, including Fama and French (1992).
• If you follow a quantitative value strategy focused on micro caps, one of the best ways to improve long-term performance is by using the Piotroski F_Score.  It’s a simple measure that strengthens a micro-cap value portfolio by reducing the number of “cheap but weak” companies and increasing the number of “cheap and strong” companies.  See: https://boolefund.com/joseph-piotroski-value-investing/

 

THE SUPERINVESTORS OF GRAHAM-AND-DODDSVILLE

Buffett gave a talk at Columbia Business School in 1984 entitled, “The Superinvestors of Graham-and-Doddsville.”  Link: http://www8.gsb.columbia.edu/rtfiles/cbs/hermes/Buffett1984.pdf

According to the EMH (Efficient Markets Hypothesis), investors who beat the market year after year are just lucky.  In his talk, Buffett argues as follows:  fifteen years before 1984, he knew a group of people who had learned the value investing approach from Ben Graham and David Dodd.  Now in 1984, fifteen years later, all of these individuals have produced investment track records far in excess of the S&P 500 Index.  Moreover, each of these investors applied the value investing approach in his own way””there was very little overlap in terms of which companies these investors bought.  Buffett simply asks whether this could be due to pure chance.

As a way to think about the issue, Buffett says to imagine a national coin-flipping contest in which all 225 million Americans (the population in 1984) participate.  It is one dollar per flip on the first day, so roughly half the people lose and pay one dollar to the other half who won.  Each day the contest is repeated, but the stakes build up based on all previous winnings.  After 10 straight mornings of this contest, there will be about 220,000 flippers left, each with a bit over $1,000.  Buffett jokes:

Now this group will probably start getting a little puffed up about this, human nature being what it is.  They may try to be modest, but at cocktail parties they will occasionally admit to attractive members of the opposite sex what their technique is, and what marvelous insights they bring to the field of flipping.  (page 5)

In another 10 days, there will be about 215 people left who had correctly called the toss of a coin 20 times in a row.  Each would have a little over $1,000,000.  Buffett quips:

By then, this group will really lose their heads.  They will probably write books on ‘How I Turned a Dollar into a Million Working Thirty Seconds a Morning.’  Worse yet, they’ll probably start jetting around the country attending seminars on efficient coin-flipping and tackling skeptical professors with, ‘If it can’t be done, why are there 215 of us?’

But then some business school professor will probably be rude enough to bring up the fact that if 225 million orangutans had engaged in a similar exercise, the results would be much the same””215 egotistical orangutans with 20 straight winning flips.

But assume that the original 225 million orangutans were distributed roughly like the U.S. population.  Buffett then asks:  what if 40 of the 215 winning orangutans were discovered to all be from the same zoo in Omaha?  This would lead one to want to identify common factors for these 40 orangutans.  Buffett says (humorously) that you’d probably ask the zookeeper about their diets, what books they read, etc.  In short, you’d try to identify causal factors.

Buffett remarks that scientific inquiry naturally follows this pattern.  He gives another example:  If there was a rare type of cancer, with 1,500 cases a year in the United States, and 400 of these cases happened in a little mining town in Montana, you’d investigate the water supply there or other variables.  Buffett explains:

You know that it’s not random chance that 400 come from a small area.  You would not necessarily know the causal factors, but you would know where to search.  (page 6)

Buffett then draws the simple, logical conclusion:

I think you will find that a disproportionate number of successful coin-flippers in the investment world came from a very small intellectual village that could be called Graham-and-Doddsville.  A concentration of winners that simply cannot be explained by chance can be traced to this particular intellectual village.

Again, Buffett stresses that the only thing these successful investors had in common was adherence to the value investing philosophy.  Each investor applied the philosophy in his own way.  Some, like Walter Schloss, used a very diversified approach with over 100 stocks chosen purely on the basis of quantitative cheapness (low P/B).  Others, like Buffett or Munger, ran very concentrated portfolios and included stocks of companies with high ROE.  And looking at this group on the whole, there was very little overlap in terms of which stocks each value investor decided to put in his portfolio.

Buffett observes that all these successful value investors were focused only on one thing:  price vs. value.  Price is what you pay;  value is what you get.  There was no need to use any academic theories about covariance, beta, the EMH, etc.  Buffett comments that the combination of computing power and mathematical training is likely what led many academics to study the history of prices in great detail.  There have been many useful discoveries, but some things (like beta or the EMH) have been overdone.

Buffett goes through the nine different track records of the market-beating value investors.  Then he summarizes:

So these are nine records of ‘coin-flippers’ from Graham-and-Doddsville.  I haven’t selected them with hindsight from among thousands.  It’s not like I am reciting to you the names of a bunch of lottery winners””people I had never heard of before they won the lottery.  I selected these men years ago based upon their framework for investment decision-making”¦ It’s very important to understand that this group has assumed far less risk than average;  note their record in years when the general market was weak”¦.

Buffett concludes that, in his view, the market is far from being perfectly efficient:

I’m convinced that there is much inefficiency in the market.  These Graham-and-Doddsville investors have successfully exploited gaps between price and value.  When the price of a stock can be influenced by a ‘herd’ on Wall Street with prices set at the margin by the most emotional person, or the greediest person, or the most depressed person, it is hard to argue that the market always prices rationally.  In fact, market prices are frequently nonsensical.

Buffett also states that value investors view risk and reward in opposite terms to the way academics view risk and reward.  The academic view is that a higher potential reward always requires taking greater risk.  But (as discussed in above in “Notes on Ben Graham”) value investors, having made the distinction between price and value, hold that the lower the risk, the higher the potential reward.  Buffett:

If you buy a dollar bill for 60 cents, it’s riskier than if you buy a dollar bill for 40 cents, but the expectation of reward is greater in the latter case.  The greater the potential for reward in the value portfolio, the less risk there is.

Buffett offers an example:

The Washington Post Company in 1973 was selling for $80 million in the market.  At the time, that day, you could have sold the assets to any one of ten buyers for not less than $400 million, probably appreciably more”¦

Now, if the stock had declined even further to a price that made the valuation $40 million instead of $80 million, it’s beta would have been greater.  And to people who think beta [or, more importantly, downside volatility] measures risk, the cheaper price would have made it look riskier.  This is truly Alice in Wonderland.  I have never been able to figure out why it’s riskier to buy $400 million worth of properties for $40 million than $80 million”¦.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

January 31, 2022

Robert G. Hastrom has written quite a few excellent books on business and investing. His best book may beThe Warren Buffett Way (Wiley, 2014), which I summarized here: https://boolefund.com/warren-buffett-way/

The Detective and the Investor (Texere, 2002) is another outstanding book by Hagstrom, which I wrote about here: https://boolefund.com/invest-like-sherlock-holmes/

The first edition ofThe Warren Buffett Way was published in 1994. A few years later, Hagstrom publishedThe Nascar Way: The Business That Drives the Sport (Wiley, 1998). NASCAR is the National Association of Stock Car Auto Racing.

Following the tenets of The Warren Buffett Way,Hagstrom explains that he first came across NASCAR from a business and investing point of view:

First, I look for companies that are simple and understandable and have a consistent operating history. Second, for a company to succeed, it must have a management team that is honest and candid and forever works to rationally allocate the capital of the business. Third, the most valuable companies tend to generate a high return on invested capital and consistently produce cash earnings for their shareholders. (pages ix-x)

Specifically, Hagstrom had came across the International Speedway Corporation. Running a racetrack is a simple and understandable business. Hagstrom also learned that the company generated high returns on invested capital. And the managers, Bill France, Jr. and Jim France, have done an excellent job managing the business, including capital allocation.

Although the book was written nearly 20 years ago, if you enjoy business and investing, or car racing, it’s a good read.

In the process of learning about International Speedway, Hagstrom had to learn about NASCAR. Here’sthe wiki entry on NASCAR: https://en.wikipedia.org/wiki/NASCAR

NASCAR is second to the National Football League in terms of television views in the United States. NASCAR’s races are broadcast in over 150 different countries. As of 2004, the company holds 17 of the top 20 regularly attended single-day sporting events in the world.

Hagstrom says he aims, in the book, to capture some of this excitement of “this superb, uniquely American sport.”

This blog briefly outlines the book based its chapters:

• Riding with Elmo
• Rules of the Road
• It Takes Money to Race
• Prime Time
• The Meanest Mile
• True American Heroes
• Forty-Two Teams on the Same Field at the Same Time

 

RIDING WITH ELMO

Hagstrom introduces the sport:

Stock car drivers do things in cars that would make the rest of us faint. Try to imagine driving 100 miles an hour, then 120, then 160. Imagine keeping up that pace for three and a half hours; that’s how long it will take to drive 500 miles… Now imagine forty-one other cars around you, all doing the same thing, just inches away from you, scraping against the side of your car and nudging your bumper as they try to pass you. And you can never slack off.

…To win a stock car race means that you are willing to drive faster than anybody else on the track. It means that you drive as fast as your nerves will let you go – and then faster. (page 3)

Hagstrom got the chance to ride with Elmo Langley, NASCAR’s pace car official (in the late 90’s) before the Southern 500 at Darlington Raceway in South Carolina. Harold Brasington, a retired racer in 1948, spent a year building the track. He agreed, upon purchasing the property, not to disturb the minnow pond at the west end of the property. This led Brasington to make that corner of the track tighter and more steeply banked. The overall result isan egg-shaped track. Crews have always found it difficult to set up the cars’ handling to be effective at both ends of the track.

At Darlington, notes Hagstrom, drivers must find the right balance of aggressiveness and patience in order to succeed. If you’re too aggressive, you’ll crash. If you’re too smooth, you’ll get passed.

Hagstrom also observes that specialized engine builders learned to take the standard 358-cubic-inch V8 motor and make it into a 700-horsepower engine. Nearly everything else about the car is also carefully engineered for each particular racetrack. (Today, in 2017, computers receive huge amounts of data from these racing machines.)

The original idea of “stock car” racing was that the cars look–on the outside at least–just like American cars that anybody could buy. Historically, NASCAR fans have followed with great passion not only specific drivers, but specific car brands, such as Chevrolets, Fords, or Pontiacs.

Darlington Raceway has a special charm:

According to many regulars, there is no more beautiful place to entertain clients and guests than Darlington Raceway. The hospitality village itself is outlined in white picket fences that surround beautifully appointed white and yellow striped tends. Flower boxes hang at each entrance. (page 9)

In addition to the hospitality tents, there are air-conditioned corporate suites high above the track. A catered dinner is served on linen-covered tables. As of the late 90’s, there were four corporate suites renting for $100,000 a year to PepsiCo, RJR Nabisco, Unocal, and Anheuser-Busch.

Hagstrom writes that the makeup of race fans has changed over the years. As of the late 90’s, 30 percent stock car fans have an annual income of over $50,000; 38 percent are female. Hagstrom says stock car racing at the beginning was for “the rowdiest and roughest,” but today’s stock car races are family events.

Hagstrom continues:

The infield, the open area inside the track itself, has become the last bastion of stock car racing’s most passionate fans. They travel hundreds of miles in their recreational vehicles, campers, pickup trucks, and vans, and they are equipped to make the infield their home for three days. They are determined not to miss one minute of racing: the qualifying runs and the practices on Friday, the support race and then more practicing on Saturday, and the featured race on Sunday, with all its festivities. (page 11)

In the 1950’s and 1960’s, writes Hagstrom, the infield was quite a bit like the Wild West. The local sheriff even set up a temporary jail there. In recent decades, however, track owners have substantially improved their infields in order to charge higher prices.

NASCAR has a number of rules:

NASCAR rules are designed to promote close, competitive racing, which the fans want, in a way that maintains parity and does not unduly favor the well-financed teams. The paramount force behind all the rules, however, is the safety of both the drivers and the fans. Everyone in NASCAR is aware of the potential for injury with so many machines running in close quarters at such high speeds, and so the rules and regulations are vigorously enforced.

A NASCAR-sanctioned motorsports event, like the Southern 500, is officiated by NASCAR and conducted in accordance with its rules. These rules cover not only the race, but all periods leading up to and following it, including registration, inspections, time trials, qualifying races, practices, and postrace inspections. (page 13)

Corporate sponsorship is the foundation of the sport, says Hagstrom. There are many different types of businesses–including many Fortune 500 companies–that are NASCAR corporate sponsors. The highest form of advertising in motorsports is to sponsor a team. As with other sports, the greatest leverage NASCAR has had in selling itself to advertisers is based increasingly on its television audience.

As far as levels of competition, the highest level in NASCAR today (2017) is the Monster Energy NASCAR Cup Series. (The highest level used to be the Winston Cup.)

Two neat things about NASCAR racing, historically:

• pay is based on performance
• drivers are humble and grateful

Hagstrom explains:

…It is the sound of humility and gratitude and enthusiasm. It is the sound of athletes who tell you–and who mean it–that they are no bigger than the fans who come out and support them. It is the sound of autographs being signed, of smiling pictures being snapped, and of kids collecting heroes. It is what is best about American sports… (page 18)

 

RULES OF THE ROAD

Hagstrom recounts the history of the sport:

Stock car racing was born in the South, the boisterous legacy of daredevil moonshine drivers who tore up and down the back roads of Appalachia during the 1930s and 1940s.

For years, hardscrabble farmers in the mountains had been making their own whiskey, just as they made their own tools, clothes, and furniture. But it wasn’t until Prohibition in 1919 that mountaineers discovered that the sippin’ whiskey they made for themselves was worth cash money to the folks in town. For many mountain families, bootlegging was their only source of income in the winter months.

…By the 1940s, the government began sending federal revenue agents into the Appalachian Mountains to stop illegal whiskey manufacturing. To avoid the revenuers, the mountaineers hid their stills and began to work only at night–hence the term “moonshiner.” Drivers would begin their delivery runs after midnight and be safely home before daybreak…

In a stepped-up game of cat and mouse, the revenuers searched for tills by day and staked out the roads at night. To stay ahead, moonshine drivers constantly tinkered with their cars, trying to eke out a few extra horsepower and to improve the suspension so the car would handle better. It wasn’t easy barreling over hills and valleys in the middle of the night, dirt kicking up everywhere, and your car loaded down with twenty-five cases of white lightning… (pages 21-22)

Sometime in the mid-1930s “in a cow pasture in the town of Stockbridge, Georgia,” a few moonshiners started arguing about who had the fastest car and who was the better driver. Someone made a quarter-mile dirt track in a farmer’s field.

After a few races, more and more people started to show up to watch. The farmer fenced off the area and started charging admission. The drivers’ pay also increased until it became more profitable to win a race than to run moonshine. Hagstrom:

After driving over 100 miles an hour on the dirt roads of North Carolina in the middle of the night while being chased by revenuers, the moonshiners looked at these smooth, level, quarter-mile racetracks, crossed their arms, rocked back on their heels, and grinned.

The Flock brothers–Tim, Bob, and Fonty–drove for their uncle, Peachtree Williams, who had one of the biggest stills in Georgia. Buddy Shuman also ran whiskey and drove stock cars. But the most famous bootlegger ever to drive stock cars was Junior Johnson. Junior ran whiskey for his daddy, Glenn Johnson, who had the biggest and most profitable moonshine operation in Wilkes Country, North Carolina. (page 23)

For instance, Junior had perfected the power slide, which allowed him to speed up into the turns rather than slow down.

But modern stock car racing owes its success to one man: William Henry Getty France, or “Big Bill” France. Big Bill France raced in the Maryland suburbs of Washington, D.C., and he worked as a mechanic in garages and service stations.

France and Annie B, his wife, went to Florida to live in Miami. But after stopping at Daytona Beach, France decided to settle there. He opened up his own gas station. Before long, his garage was a favorite hangout of mechanics and race car drivers.

Daytona Beach had hard-packed sandy beaches 500 yards wide and 25 miles long. It was already known as the Speed Capital of the world. In 1936 and 1937, writes Hagstrom, the city fathers of Daytona Beach put together races. (This was partlyout of concern forracers who were leaving for the Bonneville Salt Flats of Utah.) But these races were poorly managed. So they sought Bill France to manage the race in 1938.

France was already well-liked by most mechanics and race car drivers in the area. He was also a natural promotor. And because he had been a racer, he knew what worked and what didn’t in putting on a race.

France convinced a local restauranteur, Charlie Reese, to pay for the race as long as Bill France did all the work. They would split the profits. The race was a great success.

Soon thereafter, France heard about an oval dirt track for rent in Charlotte, North Carolina. France decided to sponsor a 100-mile National Championship race there. But local reporters hesitated to cover the race because there was no sanctioning body and no official rules.

France couldn’t convince AAA, so he organized his own sanctioning body, the National Championship Stock Car Circuit (NCSCC). NCSCC would sponsor monthly races at various tracks, and the winners would be determined by a cumulative point system and winners’ fund. France found someone to run his service station in Daytona Beach–and he got his wife Annie B to handle accounting–so that he could focus completely on setting up the system he envisioned.

1947 was the first full year for the National Championship Stock Car Circuit. It was a great success. The points’ fund, at $3,000, was divided among the top finishers. The bootlegger Fonty Flock won first place.

The problem was that stock car racing at the time didn’t have a good reputation. France knew it needed a central authority to govern all drivers, all car owners, and all track owners. So France invited the most influential people from racing to Daytona Beach for a year-end meeting about the future of stock car racing.

France described his vision to his colleagues, including a national point system and winners’ fund. Hagstrom adds:

…The rules, he declared, would have to be consistent, enforceable, and ironclad. Cheating would not be allowed. The regulations would be designed to ensure close competition, for they all knew that close side-by-side racing was what fans cheered for. Finally, he argued, the organizing body should promote a racing division dedicated solely to standard street stock cars, the same cars that could be bought at automobile dealerships. Fans would love these races, France argued, because they could identify with the cars. (page 29)

The group voted to form a national organization of stock car racing. France was elected president. And they decided to incorporate the entity. The National Association for Stock Car Auto Racing (NASCAR) was incorporated on February 15, 1948.

A technical committee set the rules for engine size, safety, and fair competition. Only American-made cars were allowed. NASCAR also decided to guarantee purses for the races it sanctioned. And they established a national point system.

NASCAR today does a very detailed set of inspections. And the rules are still designed to ensure parity and safety. As a part of parity, costs are strictly controlled.

 

IT TAKES MONEY TO RACE

Hagstrom writes:

Sponsorship is a form of marketing in which companies attach their name, brand, or logo to an event for the purpose of achieving future profit. It is not the same as advertising. Both strategies seek the same end result–corporate profit–but go about it in different ways. Advertising is a direct and overt message to consumers. If successful, it stimulates a near-term purchase. Sponsorship, on the other hand, generates a more subtle message that, if successful, creates a lasting bond between consumers and the company. (page 49)

Corporate entertainment can be an effective marketing tool.

If the goal of sponsorship is to increase sales, that can be measured over specific time periods. The same goes for other goals of sponsorship, including increasing worker productivity.

It’s more difficult to measure the impact of stock car racing sponsorship on corporate images over time. But historically, consumers have been extremely positive towards nearly all companies that sponsor stock car racing.

Hagstrom says it is impossible to attend a NASCAR race without feeling a great deal of emotion. The cars are so powerful and quick, and the competition is so close and intense, that you cannot avoid being impressed if you’ve never been to a race before.

In one survey (in the late 90’s), stock car racing fans were able to identify more than 200 different companies or brands connected with stock car racing. Of all the companies mentioned, only 1 percent were incorrectly named by the fans, notes Hagstrom. This is simply incredible.

Drivers know that their teams couldn’t race without corporate sponsors. And fans know that ticket prices would be much higher without corporate sponsors.

 

PRIME TIME

Hagstrom writes:

The reason NASCAR events do well all season long is the same reason the other sports do so well during the playoffs: the thrill of seeing the sport’s best athletes compete in a one-time event. By the time baseball, basketball, and football get to the playoffs, the very best teams are facing each other. Each game in a playoff series takes on an intensity that increases geometrically; as the stakes rise, so does the excitement. So too does the sense of urgency. Fans know that playoffs and championship games will be played only once, and they had better not miss them. (page 83)

Although a variety of camera angles and close-up views allow fans to follow NASCAR races on television better than ever before, there is still nothing like seeing a NASCAR race live.

 

THE MEANEST MILE

Hagstrom:

Although Darlington Raceway is credited with being NASCAR’s first superspeedway, world-famous Daytona is the track most responsible for launching the sport of stock car racing into the modern era. Ask any driver his reaction on seeing Daytona for the first time and you will hear words like “amazing,” “incredible,” and “intimidating.”…

Without question, Daytona was built for speed. It’s 2.5 miles long, with big sweeping turns banked at 31 degrees. Fireball Roberts, another famous 1960s NASCAR driver, was eager. “This is the track where you can step on the accelerator and let it roll. You can flatfoot it all the way.” (pages 107-108)

Hagstrom then describes Talladega (as of the late 90’s):

Talladega stretched the imagination. At 2.66 miles long, it was the longest and soon the fastest speedway. It was here that Bill Elliott drove the fastest lap in NASCAR history–212 mph. Drivers, once they built experience, began racing here at speeds in excess of 200 mph. Because Talladega is wide (one lane wider than Daytona), racing three abreadst became the norm. The intensity of competition racheted up several levels.

At last, racers had found a track that was built for speeds faster than most were comfortable driving. NASCAR had finally answered its own question: Just how fast is fast enough? (pages 108-109)

Track owners have the following sources of revenue:

• General admission and luxury suites
• Television and radio broadcast fees
• Sponsorship fees and advertising
• Concession, program, and mechandise sales
• Hospitality tents and souvenir trailers

Expenses include:

• Sanctioning fee
• Prize money
• Operating costs

Selling tickets has been the key for decades. As of the late 90’s, grandstand seats, suites, and infield parking account for 70 percent of a track’s revenue, notes Hagstrom. Other sources of revenue include concessions, souvenirs, signage, and broadcast rights.

 

TRUE AMERICAN HEROES

From 1973 to 1975, Dale Earnhardt was living hand to mouth and trying to save money to race. Earnhardt finally got a chance to race at the World 600 at the Charlotte Motor Speedway. Still, it takes years before a driver can race at NASCAR’s highest level. Finally, in 1978, Earnhardt came in fourth place at Atlanta International Raceway. Dale Earnhardt earned Rookie of the Year in 1979. And he won the championship in 1980. By the late 90’s, Earnhardt was arguably the greatest stock car racer of all time.

NASCAR race fans are probably the most passionate fans in the world, or at least have been historically. Without fans buying tickets–and souvenirs and other products–stock car racing as it is would not exist. But, unlike most other major sports historically, NASCAR fans can walk up to their favorite athletes and talk with them.

Hagstrom writes:

There is much about NASCAR racing that draws people to it. For one thing, it is easy to identify with the activity. Almost every adult in America knows how to drive a car, and most can remember the teenage thrill of driving fast. Many fans own cars that, except for the paint job, look just like cars on the racetrack. Unlike other sports, you don’t have to be a certain size, weight, or height to be a race driver. So it’s not too much of a stretch for fans to imagine themselves behind the wheel of those powerful cars.

Something in the human psyche is attracted to danger, and that too is part of the appeal. Today’s race cars are many times safer than today’s ordinary passenger vehicles; nonetheless, there is always the sense that something spectacular could happen at any moment. Finally, racing is inherently exciting in a way that many other sports are not. The noise, the vibration, the speed all combine to affect observers in a powerful, almost visceral way.

All those factors, however, would not be enough to explain the loyalty of the NASCAR fans were it not for one other critical ingredient: the intense emotional bond that exists between fans and their drivers. That bond rests on a foundation of courtesy, humility, and respect that runs both ways. The drivers’ attitude toward their fans is the unique factor that sets NASCAR apart and makes its drivers genuine heroes. (pages 152-153)

 

FORTY-TWO TEAMS ON THE SAME FIELD AT THE SAME TIME

To win at the highest level, teams need not only a great driver, but a fast car and an excellent crew. The crew chief is a crucial position.

…In all matters relating to the technical aspects of the car, including building it in the shop and monitoring how it performs at the track, the decisions rest with the crew chief. The crew chief hires the race shop personnel, including a shop foreman, engine builders, fabricators, machinists, engineers, mechanics, gear/transmission specialists, a parts manager, and a transport driver. (page 163)

Note again that Hagstrom was writing in the late 90’s. In 2017, computers are far more powerful and are, accordingly, more essential in car racing generally.

On race day, the race crew is essential. As of the late 90’s, they could change all four tires and refuel the car in twenty seconds.

In qualifying runs, typically there are more teams than available slots. Qualifying often depends on tenths of a second. Fine-tuning the race car requires a high degree of skill and teamwork.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

January 24, 2022

The Lessons of History””first published in 1968″”is the result of a lifetime of work by the outstanding historians, Will and Ariel Durant. Top investor Ray Dalio, founder and leader of Bridgewater Associates, has recommended the book as a succinct set of observations on history.

 

History may not repeat itself, but it does rhyme.  – Mark Twain

 

BIOLOGY AND HISTORY

There are three biological lessons of history:

• Life is competition.
• Life is selection.
• Life must breed.

The Durants write that our acquisitiveness, greediness, and pugnacity are remnants of our evolutionary history as hunters and gatherers.  In those days, we had to eat as much as possible when we managed to get food from a successful hunt.  We also had to hoard food (and others goods) whenever possible.

As for selection, there is virtually an infinite variety of random differences in people.  And the environment itself can often be random and unpredictable.  People who win the Ovarian Lottery””to use Warren Buffett’s term””don’t just have talents; rather, they have talents well-suited to a specific environment.  Here’s Buffett:

As my friend Bill Gates says, if I’ve been born in some different place or some different time I’d have been some animal’s lunch.  I’d have been running real fast, and the animal would have been chasing me and I’d say “I allocate capital” and the animal would say “well, those are the kind that taste the best”.  I’ve been in the right place at the right time, and I’m lucky, I think a fair amount of that luck should be shared with others.

In 2013, someone from a group of students asked Warren Buffett how his understanding of markets affected his political views.  Buffett replied:

I wouldn’t say knowledge of markets has.  My political views were formed by this process.  Just imagine that it is 24 hours before you are born.  A genie comes and says to you in the womb, “You look like an extraordinarily responsible, intelligent, potential human being.  Going to emerge in 24 hours and it is an enormous responsibility I am going to assign to you””determination of the political, economic and social system into which you are going to emerge.  You set the rules, any political system, democracy, parliamentary, anything you wish, can set the economic structure, communistic, capitalistic, set anything in motion and I guarantee you that when you emerge this world will exist for you, your children and grandchildren.  What’s the catch?  One catch””just before you emerge you have to go through a huge bucket with 7 billion slips, one for each human.  Dip your hand in and that is what you get””you could be born intelligent or not intelligent, born healthy or disabled, born black or white, born in the US or in Bangladesh, etc.  You have no idea which slip you will get.  Not knowing which slip you are going to get, how would you design the world?  Do you want men to push around females?  It’s a 50/50 chance you get female.  If you think about the political world, you want a system that gets what people want.  You want more and more output because you’ll have more wealth to share around.  The US is a great system, turns out $50,000 GDP per capital, 6 times the amount when I was born in just one lifetime.  But not knowing what slip you get, you want a system that once it produces output, you don’t want anyone to be left behind.  You want to incentivize the top performers, don’t want equality in results, but do want something that those who get the bad tickets still have a decent life.  You also don’t want fear in people’s minds””fear of lack of money in old age, fear of cost of health care.  I call this the “Ovarian Lottery”.  My sisters didn’t get the same ticket.  Expectations for them were that they would marry well, or if they work, would work as a nurse, teacher, etc.  If you are designing the world knowing 50/50 male or female, you don’t want this type of world for women ““ you could get female.  Design your world this way; this should be your philosophy.  I look at Forbes 400, look at their figures and see how it’s gone up in the last 30 years.  Americans at the bottom are also improving, and that is great, but we don’t want that degree of inequality.  Only governments can correct that.  Right way to look at it is the standpoint of how you would view the world if you didn’t know who you would be.  If you’re not willing to gamble with your slip out of 100 random slips, you are lucky!  The top 1% of 7 billion people.  Everyone is wired differently.  You can’t say you do everything yourself.  We all have teachers, and people before us who led us to where we are.  We can’t let people fall too far behind.  You all definitely got good slips.

Link:  http://blogs.rhsmith.umd.edu/davidkass/uncategorized/warren-buffetts-meeting-with-university-of-maryland-mbams-students-november-15-2013/

As for the third biological lesson of history, that life must breed, Will and Ariel Durant explain:

Nature has no use for organisms, variations, or groups that cannot reproduce abundantly.  She has a passion for quantity as prerequisite to the selection of quality; she likes large litters, and relishes the struggle that picks the surviving few; doubtless she looks on approvingly at the upstream race of a thousand sperms to fertilize one ovum.  She is more interested in the species than in the individual, and makes little difference between civilization and barbarism.  She does not care that a high birth rate has usually accompanied a culturally low civilization, and a low birth rate a civilization culturally high; and she (meaning Nature as the process of birth, variation, competition, selection, and survival) sees to it that a nation with a low birth rate shall be periodically chastened by some more virile and fertile group.  (page 21)

 

RACE AND HISTORY

Will and Ariel Durant sum it up:

“Racial” antipathies have some origin in ethnic origin, but they are also generated, perhaps predominantly, by differences of acquired culture””of language, dress, habits, moral, or religion.  There is no cure for such antipathies except a broadened education.  A knowledge of history may teach us that civilization is a co-operative product, that nearly all people’s have contributed to it; it is our common heritage and debt; and the civilized soul will reveal itself in treating every man or woman, however lowly, as a representative of one of these creative and contributory groups.  (page 31)

 

CHARACTER AND HISTORY

Will and Ariel Durant:

Evolution in man during recorded time has been social rather than biological: it has proceeded not by heritable variations in the species, but mostly by economic, political, intellectual, and moral innovation transmitted to individuals and generations by imitation, custom, or education.  Custom and tradition within a group correspond to type and heredity in the species, and to instincts in the individual; they are ready adjustments to typical and frequently repeated situations.  New situations, however, do arise, requiring novel, unstereotyped responses; hence development, in the higher organisms, requires a capacity for experiment and innovation””the social correlates of variation and mutation.  Social evolution is an interplay of custom with origination.  (page 34)

Occasionally some new challenge or situation has required the new (or sometimes very old) ideas of an innovator””whether scientist, inventor, or leader (business, political, spiritual).

 

MORALS AND HISTORY

Will and Ariel Durant note that what are today considered vices may once have been virtues””i.e., advantages for survival.  They observe that the transition from hunting to agriculture called for new virtues:

We may reasonably assume that the new regime demanded new virtues, and changed some old virtues into vices.  Industriousness became more vital than bravery, regularity and thrift more profitable than violence, peace more victorious than war.  Children were economic assets; birth control was made immoral.  On the farm, the family was the unit of production under the discipline of the father and the seasons, and paternal authority had a firm economic base.  (page 38)

Gradually and then rapidly, write the Durants, the Industrial Revolution changed the economic form and moral superstructure of European and American life.  Many went to work as individuals in factories, and many of them worked with machines in the factories.

The Durants point out that much written history is, as Voltaire said, “a collection of the crimes, follies, and misfortunes” of humankind.  However, this written history typically does not include many good and noble deeds that actually occurred:

We must remind ourselves again that history as usually written (peccavimus) is quite different from history as usually lived: the historian records the exceptional because it is interesting””because it is exceptional.  If all those individuals who had no Boswell had found their numerically proportionate place in the pages of historians we should have a duller but juster view of the past and of man.  Behind the red facade of war and politics, misfortune and poverty, adultery and divorce, murder and suicide, were millions of orderly homes, devoted marriages, men and women kindly and affectionate, troubled and happy with children.  Even in recorded history we find so many instances of goodness, even of nobility, that we can forgive, though not forget, the sins.  The gifts of charity have almost equaled the cruelties of battlefields and jails.  How many times, even in our sketchy narratives, have we seen men helping one another… (page 41)

 

RELIGION AND HISTORY

Religion has helped with educating the young.  And religion has given meaning and dignity to even the lowliest existence, write the Durants.  Religion gives many people hope.  However, religion has stumbled at important times:

The majestic dream broke under the attacks of nationalism, skepticism, and human frailty.  The Church was manned with men, who often proved biased, venal, or extortionate.  France grew in wealth and power, and made the papacy her political tool.  Kings became strong enough to compel a pope to dissolve that Jesuit order which had so devotedly supported the popes.  The Church stooped to fraud, as with pious legends, bogus relics, and dubious miracles… More and more the hierarchy spent its energies in promoting orthodoxy rather than morality, and the Inquisition almost fatally disgraced the Church.  Even while preaching peace the Church fomented religious wars in sixteenth-century France and the Thirty Years’ War in seventeenth-century Germany.  It played only a modest part in the outstanding advance of modern morality””

the abolition of slavery.  It allowed the philosophers to take the lead in the humanitarian movements that have alleviated the evils of our time.  (page 45)

 

ECONOMICS AND HISTORY

Will and Ariel Durant open the chapter:

Unquestionably the economic interpretation illuminates much history.  The money of the Delian Confederacy built the Parthenon; the treasury of Cleopatra’s Egypt revitalized the exhausted Italy of Augustus, gave Virgil an annuity and Horace a farm.  The Crusades, like the wars of Rome with Persia, were attempts of the West to capture trade routes to the East; the discovery of America was a result of the failure of the Crusades.  The banking house of the Medici financed the Florentine Renaissance; the trade and industry of Nuremberg made Durer possible.  The French Revolution came not because Voltaire wrote brilliant satires and Rousseau sentimental romances, but because the middle classes had risen to economic leadership, needed legislative freedom for their enterprise and trade, and itched for social acceptance and political power.  (pages 52-53)

Bankers have often risen to the top of the economic pyramid, since they have been able to direct the flow of capital.

The Durants note the importance of the profit motive in moving the economy forward:

The experience of the past leaves little doubt that every economic system must sooner or later rely upon some form of profit motive to stir individuals and groups to productivity.  Substitutes like slavery, police supervision, or ideological enthusiasm prove too unproductive, too expensive, and too transient.  (page 54)

Wealth tends naturally to concentrate in the hands of the most able.  Periodically it must be redistributed.

…The government of the United States, in 1933-52 and 1960-65, followed Solon’s peaceful methods, and accomplished a moderate and pacifying redistribution; perhaps someone had studied history.  The upper classes in America cursed, complied, and resumed the concentration of wealth.

We conclude that the concentration of wealth is natural and inevitable, and is periodically alleviated by violent or peaceable partial redistribution.  (page 57)

 

SOCIALISM AND HISTORY

Capitalism””especially in America””has unleashed amazing productivity and will continue to do so for a long time:

The struggle of socialism against capitalism is part of the historic rhythm in the concentration and dispersion of wealth.  The capitalist, of course, has fulfilled a creative function in history: he has gathered the savings of the people into productive capital by the promise of dividends or interest; he has financed the mechanization of industry and agriculture, and the rationalization of distribution; and the result has been such a flow of goods from producer to consumer as history has never seen before.  He has put the liberal gospel of liberty to his use by arguing that businessmen left relatively free from transportation tolls and legislative regulation can give the public a greater abundance of food, homes, comfort, and leisure than has ever come from industries managed by politicians, manned by governmental employees, and supposedly immune to the laws of supply and demand.  In free enterprise the spur of competition and the zeal and zest of ownership arouse the productiveness and inventiveness of men; nearly every economic ability sooner or later finds its niche and reward in the shuffle of talents and the natural selection of skills; and a basic democracy rules the process insofar as most of the articles to be produced, and the services to be rendered, are determined by public demand rather than by governmental decree.  Meanwhile competition compels the capitalist to exhaustive labor, and his products to ever-rising excellence.  (pages 58-59)

Throughout most of history, socialist structures or centralized control by government have guided economies.  The Durants offer many examples, including that of Egypt:

In Egypt under the Ptolemies (323 B.C. – 30 B.C.) the state owned the soil and managed agriculture: the peasant was told what land to till, what crops to grow; his harvest was measured and registered by government scribes, was threshed on royal threshing floors, and was conveyed by a living chain of fellaheen into the granaries of the king.  The government owned the mines and appropriated the ore.  It nationalized the production and sale of oil, salt, papyrus, and textiles.  All commerce was controlled and regulated by the state; most retail trade was in the hands of state agents selling state-produced goods.  Banking was a government monopoly, but its operation might be delegated to private firms.  Taxes were laid upon every person, industry, process, product, sale, and legal document.  To keep track of taxable transactions and income, the government maintained a swarm of scribes and a complex system of personal and property registration.  The revenue of this system made the Ptolemaic the richest state of the time.  Great engineering enterprises were completed, agriculture was improved, and a large proportion of the profits went to develop and adorn the country and to finance its cultural life.  About 290 B.C. the famous Museum and Library of Alexandria were founded.  Science and literature flourished; at uncertain dates in this Ptolemaic era some scholars made the “Septuagint” translation of the Pentateuch into Greek.  (pages 59-60)

The Durants then tell the story of Rome under Diocletian:

…Faced with increasing poverty and restlessness among the masses, and with imminent danger of barbarian invasion, he issued in A.D. 301 an Edictum de pretiis, which denounced monopolists for keeping goods from the market to raise prices, and set maximum prices and wages for all important articles and services.  Extensive public works were undertaken to put the unemployed to work, and food was distributed gratis, or at reduced prices, to the poor.  The government””which already owned most mines, quarries, and salt deposits””brought nearly all major industries and guilds under detailed control.  “In every large town,” we are told, “the state became a powerful employer, … standing head and shoulders above the private industrialists, who were in any case crushed by taxation.”  When businessmen predicted ruin, Diocletian explained that the barbarians were at the gate, and that individual liberty had to be shelved until collective liberty could be made secure.  The socialism of Diocletian was a war economy, made possible by fear of foreign attack.  Other factors equal, internal liberty varies inversely as external danger.

The task of controlling men in economic detail proved too much for Diocletian’s expanding, expensive, and corrupt bureaucracy.  To support this officialdom””the army, the court, public works, and the dole””taxation rose to such heights that men lost incentive to work or earn, and an erosive contest began between lawyers finding devices to evade taxes and lawyers formulating laws to prevent evasion.  Thousands of Romans, to escape the taxgatherer, fled over the frontiers to seek refuge among the barbarians.  Seeking to check this elusive mobility, and to facilitate regulation and taxation, the government issued decrees binding the peasant to his field and the worker to his shop until all his debts and taxes had been paid.  In this and other ways medieval serfdorm began.  (pages 60-61)

The Durants then recount several attempts at socialism in China, including under the philosopher-king Wang Mang:

Wang Mang (r. A.D. 9-23) was an accomplished scholar, a patron of literature, a millionaire who scattered his riches among his friends and the poor.  Having seized the throne, he surrounded himself with men trained in letters, science, and philosophy.  He nationalized the land, divided it into equal tracts among the peasants, and put an end to slavery.  Like Wu Ti, he tried to control prices by the accumulation or release of stockpiles.  He made loans at low interest to private enterprise.  The groups whose profits had been clipped by his legislation united to plot his fall; they were helped by drought and flood and foreign invasion.  The rich Liu family put itself at the head of a general rebellion, slew Wang Mang, and repealed his legislation.  Everything was as before.  (page 62)

Later, the Durants tell of the longest-lasting socialist government: the Incas in what is now Peru.  Everyone was an employee of the state.  It seems all were happy, given the promise of security and food.

There was also a Portuguese colony in which 150 Jesuits organized 200,000 Indians in a socialist society (c. 1620 – 1750).  Every able-bodied person was required to work eight hours a day.  The Jesuits served as teachers, physicians, and judges.  The penal system did not include capital punishment.  The Jesuits also provided for recreation, including choral performances.  All were peaceful and happy, write the Durants.  And they defended themselves well when attacked.  The socialist experiment ended when the Spanish in America wanted immediately to occupy the Portuguese colony because it was rumored to contain gold.  The Portuguese government under Pombal””at the time, in disagreement with the Jesuits””ordered the priests and the natives to leave the settlements, say the Durants.

The Durants conclude the chapter:

… [Marx] interpreted the Hegelian dialectic as implying that the struggle between capitalism and socialism would end in the complete victory of socialism; but if the Hegelian formula of thesis, antithesis, and synthesis is applied to the Industrial Revolution as thesis, and to capitalism versus socialism as antithesis, the third condition would be a synthesis of capitalism and socialism; and to this reconciliation the Western world visibly moves.  (page 66)

Note that the Durants were writing in 1968.

 

GOVERNMENT AND HISTORY

Will and Ariel Durant:

Alexander Pope thought that only a fool would dispute over forms of government.  History has a good word to say for all of them, and for government in general.  Since men love freedom, and the freedom of individuals in society requires some regulation of conduct, the first condition of freedom is its own limitation; make it absolute and it dies in chaos.  So the prime task of government is to establish order; organized central force is the sole alternative to incalculable and disruptive forces in private hands.  (page 68)

It’s difficult to say when people were happiest.  Since I believe strongly that the most impactful technological breakthroughs ever””including but not limited to AI and genetics””are going to occur in the next 20-80 years, I would argue that we as humans are a long way away from the happiness we can achieve in the future.  (I also think Steven Pinker is right””in The Better Angels of Our Nature“”that people are becoming less violent, slowly but surely.)

But if you had to pick a historical period, I would defer to the great historians to make this selection.  The Durants:

…”If,” said Gibbon, “a man were called upon to fix the period during which the condition of the human race was most happy and prosperous, he would without hesitation name that which elapsed from the accession of Nerva to the death of Marcus Aurelius.  Their united reigns are possibly the only period of history in which the happiness of a great people was the sole object of government.”  In that brilliant age, when Rome’s subjects complimented themselves on being under her rule, monarchy was adoptive: the emperor transmitted his authority not to his offspring but to the ablest man he could find; he adopted this man as his son, trained him in the functions of government, and gradually surrendered to him the reins of power.  The system worked well, partly because neither Trajan nor Hadrian had a son, and the sons of Antonius Pius died in childhood.  Marcus Aurelius had a son, Commodus, who succeeded him because the philosopher failed to name another heir; soon chaos was king.  (page 69)

The Durants then write that most monarchs overall do not have a great record.

Hence most governments have been oligarchies””ruled by a minority, chosen either by birth, as in aristocracies, or by a religious organization, as in theocracies, or by wealth, as in democracies.  It is unnatural (as even Rousseau saw) for a majority to rule, for a majority can seldom be organized for united and specific action, and a minority can.  If the majority of abilities is contained in a minority of men, minority government is as inevitable as the concentration of wealth; the majority can do no more than periodically throw out one minority and set up another.  The aristocrat holds that political selection by birth is the sanest alternative to selection by money or theology or violence.  Aristocracy withdraws a few men from the exhausting and coarsening strife of economic competition, and trains them from birth, through example, surroundings, and minor office, for the tasks of government; these tasks require a special preparation that no ordinary family or background can provide.  Aristocracy is not only a nursery of statesmanship, it is also a repository and vehicle of culture, manners, standards, and tastes, and serves thereby as a stabilizing barrier to social fads, artistic crazes, or neurotically rapid changes in the moral code… (page 70)

When aristocracies were too selfish and myopic, however, slowing progress, the new rich combined with the poor to overthrow them, say the Durants.

The Durants point out that most revolutions probably would have occurred without violence through gradual economic development.  They mention the rise of America as an example.  They also note that the English aristocracy was gradually replaced by the money-controlling business class in England.  The Durants then generalize:

The only real revolution is in the enlightenment of the mind and the improvement of character, the only real emancipation is individual, and the only real revolutionists are philosophers and saints.  (page 72)

A bit later, the Durants discuss the battles between the poor and the rich in Athenian democracy around the time of Plato’s death (347 B.C.).

…The poor schemed to despoil the rich by legislation, taxation, and revolution; the rich organized themselves for protection against the poor.  The members of some oligarchic organizations, says Aristotle, took a solemn oath: “I will be an adversary of the people” (i.e., the commonalty), “and in the Council I will do it all the evil that I can.”  “The rich have become so unsocial,” wrote Isocrates about 366 B.C., “that those who own property had rather throw their possessions into the sea than lend aid to the needy, while those who are in poorer circumstances would less gladly find a treasure than seize the possessions of the rich.”  (pages 74-75)

Much of this class warfare became violent.  And Greece was divided when Philip of Macedon attacked in 338 B.C.

The Durants continue:

Plato’s reduction of political evolution to a sequence of monarchy, aristocracy, democracy, and dictatorship found another illustration in the history of Rome.  During the third and second centuries before Christ a Roman oligarchy organized a foreign policy and a disciplined army, and conquered and exploited the Mediterranean world.  The wealth so won was absorbed by the patricians, and the commerce so developed raised to luxurious opulence the upper middle class.  Conquered Greeks, Orientals, and Africans were brought to Italy to serve as slaves on the latifundia; the native farmers, displaced from the soil, joined the restless, breeding proletariat in the cities, to enjoy the monthly dole of grain that Caius Gracchus had secured for the poor in 123 B.C.  Generals and proconsuls returned from the provinces loaded with spoils for themselves and the ruling class; millionaires multiplied; mobile money replaced land as the source or instrument of political power; rival factions competed in the wholesale purchase of candidates and votes; in 53 B.C. one group of voters received ten million sesterces for its support.  When money failed, murder was available: citizens who had voted the wrong way were in some instances beaten close to death and their houses were set on fire.  Antiquity had never known so rich, so powerful, and so corrupt a government.  The aristocrats engaged Pompey to maintain their ascendancy; the commoners cast their lot with Caesar; ordeal of battle replaced the auctioning of victory; Caesar won, and established a popular dictatorship.  Aristocrats killed him, but ended by accepting the dictatorship of his grandnephew and stepson Augustus (27 B.C.).  Democracy ended, monarchy was restored; the Platonic wheel had come full turn.  (pages 75-76)

The Durants describe American democracy as the most universal ever seen so far.  But the advance of technology””to the extent that it makes the economy more complex””tends to concentrate power even more:

Every advance in the complexity of the economy puts an added premium upon superior ability, and intensifies the concentration of wealth, responsibility, and political power.  (page 77)

Will and Ariel Durant conclude that democracy has done less harm and more good than any other form of government:

…It gave to human existence a zest and camaraderie that outweighed its pitfalls and defects.  It gave to thought and science and enterprise the freedom essential to their operation and growth.  It broke down the walls of privilege and class, and in each generation its raised up ability from every rank and place.  Under its stimulus Athens and Rome became the most creative cities in history, and America in two centuries has provided abundance for an unprecedently large proportion of its population.  Democracy has now dedicated itself resolutely to the spread and lengthening of education, and to the maintenance of public health.  If equality of educational opportunity can be established, democracy will be real and justified.  For this is the vital truth beneath its catchwords: that though men cannot be equal, their access to education and opportunity can be made more nearly equal.  The rights of man are not rights to office and power, but the rights of entry into every avenue that may nourish and test a man’s fitness for office and power.  A right is not a gift of God or nature but a privilege which it is good that the individual should have.  (pages 78-79)

 

HISTORY AND WAR

As mentioned earlier, I happen to agree with Steven Pinker’s thesis in The Better Angels of Our Nature:  we humans are slowly but surely becoming less violent as economic and technological progress continues.  But it could still take a very long time before wars stop entirely (if ever).

Will and Ariel Durant were writing in 1968, so they didn’t know that the subsequent 50 years would be (arguably) less violent overall.  In any case, they offer interesting insights into war:

The causes of war are the same as the causes of competition among individuals: acquisitiveness, pugnacity, and pride; the desire for food, land, materials, fuels, mastery.  The state has our instincts without our restraints.  The individual submits to restraints laid upon him by morals and laws, and agrees to replace combat with conference, because the state guarantees him basic protection in his life, property, and legal rights.  The state itself acknowledges no substantial restraints, either because it is strong enough to defy any interference with its will or because there is no superstate to offer it basic protection, and no international law or moral code wielding effective force.  (page 81)

The Durants write that, after freeing themselves from papal control, many modern European states””if they foresaw a war””would cause their people to hate the people in the opposing country.  Today, we know from psychology that when people develop extreme hatreds, they nearly always dehumanize and devalue the human beings they hate and minimize their virtues.  Such extreme hatreds, if unchecked, often lead to tragic consequences.  (The Durants note that wars between European states in the sixteenth century still permitted each side to respect the other’s civilization and achievements.)

Again bearing in mind when the Durants were writing (1968), the historical precedent seemed to indicate that the United States should attack emerging communist powers before they became powerful enough to overcome the United States.  The Durants:

…There is something greater than history.  Somewhere, sometime, in the name of humanity, we must challenge a thousand evil precedents, and dare to apply the Golden Rule to nations, as the Buddhist King Ashoka did (262 B.C.), or at least do what Augustus did when he bade Tiberius desist from further invasion of Germany (A.D. 9)… “Magnanimity in politics,” said Edmund Burke, “is not seldom the truest wisdom, and a great empire and little minds go ill together.”  (pages 84-85)

Perhaps the humanist will agree with Pinker (as I do) that eventually, however slowly, we will move towards the cessation of war (at least between humans).  If this happens, it may be due largely to unprecedented progress in technology (including but not limited to AI and genetics):  we will gain control of our own evolution and wealth per capita will advance to unimaginable levels.

At the same time, we shouldn’t assume that aliens are necessarily peace-loving.  Perhaps humanity will have to unite in self-defense, say the Durants.

 

GROWTH AND DECAY

Will and Ariel Durant give again their definition of civilization:

We have defined civilization as “social order promoting cultural creation.”  It is political order secured through custom, morals, and law, and economic order secured through a continuity of production and exchange; it is cultural creation through freedom and facilities for the origination, expression, testing, and fruition of ideas, letters, manners, and arts.  It is an intricate and precarious web of human relationships, laboriously built and readily destroyed.  (page 87)

The Durants later add:

History repeats itself in the large because human nature changes with geological leisureliness, and man is equipped to respond in stereotyped ways to frequently occurring situations and stimuli like hunger, danger, and sex.  But in a developed and complex civilization individuals are more differentiated and unique than in primitive society, and many situations contain novel circumstances requiring modifications of instinctive response; custom recedes, reasoning spreads; the results are less predictable.  There is no certainty that the future will repeat the past.  Every year is an adventure.  (page 88)

Growth happens when people meet challenges.

If we ask what makes a creative individual, we are thrown back from history to psychology and biology””to the influence of the environment and the gamble and secret of the chromosomes.  (page 91)

Decay of the civilization or group happens when the political or intellectual leaders fail to meet the challenges of change.

The challenges may come from a dozen sources… A change in the instruments or routes of trade””as by the conquest of the ocean or the air””may leave old centers of civilization becalmed and decadent, like Pisa or Venice after 1492.  Taxes may mount to the point of discouraging capital investment and productive stimulus.  Foreign markets and materials may be lost to more enterprising competition; excess of imports over exports may drain [wealth and reserves].  The concentration of wealth may disrupt the nation in class or race war.  The concentration of population and poverty in great cities may compel a government to choose between enfeebling the economy with a dole and running the risk of riot and revolution.  (page 92)

All great individuals so far have died.  (Future technology may allow us to fix that, perhaps this century.)  But great civilizations don’t really die, say the Durants:

…Greek civilization is not really dead; only its frame is gone and its habitat has changed and spread; it survives in the memory of the race, and in such abundance that no one life, however full and long, could absorb it all.  Homer has more readers now than in his own day and land.  The Greek poets and philosophers are in every library and college; at this moment Plato is being studied by a hundred thousand discoverers of the “dear delight” of philosophy overspreading life with understanding thought.  This selective survival of creative minds is the most real and beneficent of immortalities.

Nations die.  Old regions grow arid, or suffer other change.  Resilient man picks up his tools and his arts, and moves on, taking his memories with him.  If education has deepened and broadened those memories, civilization migrates with him, and builds somewhere another home.  In the new land he need not begin entirely anew, nor make his way without friendly aid; communication and transport bind him, as in a nourishing placenta, with his mother country.  Rome imported Greek civilization and transmitted it to Western Europe; America profited from European civilization and prepares to pass it on, with a technique of transmission never equaled before.

Civilizations are the generations of the racial soul.  As life overrides death with reproduction, so an aging culture hands its patrimony down to its heirs across the years and the seas.  Even as these lines are being written, commerce and print, wires and waves and invisible Mercuries of the air are binding nations and civilizations together, preserving for all what each has given to the heritage of mankind.  (pages 93-94)

 

IS PROGRESS REAL?

If progress is increasing our control of the environment, then obviously progress continues to be made, primarily because scientists, inventors, entrepreneurs, and other leaders continue to push science, technology, and education forward.  The Durants also point out that people are living much longer than ever before.  (Looking forward today from 2017, the human lifespan may double or triple at a minimum; and we may eventually develop the capacity to live virtually forever.)

Will and Ariel Durant then sum up all they have learned:

History is, above all else, the creation and recording of that heritage; progress is its increasing abundance, preservation, transmission, and use.  To those of us who study history not merely as a warning reminder of man’s follies and crimes, but also as an encouraging remembrance of generative souls, the past ceases to be a depressing chamber of horrors; it becomes a celestial city, a spacious country of the mind, wherein a thousand saints, statesmen, inventors, scientists, poets, artists, musicians, lovers, and philosophers still live and speak, teach and carve and sing.  The historian will not mourn because he can see no meaning in human existence except that which man puts into it; let it be our pride that we ourselves may put meaning into our lives, and sometimes a significance that transcends death.  If a man is fortunate he will, before he dies, gather up as much as he can of his civilized heritage and transmit it to his children.  And to his final breath he will be grateful for this inexhaustible legacy, knowing that it is our nourishing mother and our lasting life.  (page 102)

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

January 17, 2022

Ultra-low-cost index funds tend to be exceptionally good long-term investments. It’s not just that, on an annual basis, index funds typically do better than 60-80% of all funds. It’s that index funds very consistently do better. Consistently outperforming 60-80% of all funds annually virtually guarantees that index funds will beat at least 90-95% of all funds over the course of several decades or more. It’s just a matter of simple arithmetic, as Bogle has noted. Moreover, the past several decades illustrate this result (see Brute Facts below).

If you’re a long-term investor, then by investing in index funds, you are likely to beat at least 90-95% of all investors, net of costs, over time. Investing in index funds is the best long-term investment for the vast majority of investors, as Warren Buffett–one of the greatest investors ever–has often noted. See: https://boolefund.com/warren-buffett-jack-bogle/

Jack Bogle’sDoun’t Count on It!(Wiley, 2011) is a collection of his writings on a variety of topics including capitalism, entrepreneurship, indexing, idealism, and heroes. It’s a long book (586 pages), but well worth reading. Below is my brief summary of Chapter 18 (pages 369-392).

 

THE INTELLECTUAL BASIS FOR INDEXING

The main reason that index funds generally beat at least 90-95% of all investors over time is ultra-low costs. Bogle:

…we don’t need to accept the EMH [Efficient Market Hypothesis] to be index believers. For there is a second reason for the triumph of indexing, and it is not only more compelling but unarguably universal. I call it the CMH–the Cost Matters Hypothesis–and not only is it all that is needed to explain why indexing must and does work, but it in fact enables us to quantify with some precision how well it works. Whether or not the markets are efficient, the explanatory power of the CMH holds. (page 371)

Bogle further explains:

…The mathematical expectation of the speculator is not zero; it is a loss equal to the amount of transaction costs incurred.

So, too, the mathematical expectation of the long-term investor also is a shortfall to whatever returns our financial markets are generous enough to provide. Indeed the shortfall can be described as precisely equal to the costs of our system of financial intermediation–the sum total of all those advisory fees, marketing expenditures, sales loads, brokerage commissions, transaction costs, custody and legal fees, and securities processing expenses. Intermediation costs in the U.S. equity market may well total as much as $250 billion a year or more. If today’s $13 trillion stock market were to provide, say, a 7 percent annual return ($910 billion), costs would consume more than a quarter of it, leaving less than three-quarters of the return for the investors–those who put up 100 percent of the capital. We don’t need the EMH to explain the dire odds that investors face in their quest to beat the stock market. We need only the CMH. Whether markets are efficient or inefficient, investors as a group must fall short of the market return by the amount of the costs they incur. (page 372)

 

ORIGIN OF VANGUARD

Bogle recounts:

Our introduction of First Index Investment Trust was greeted by the investment community with derision. It was dubbed ‘Bogle’s Folly,’ and described as un-American, inspiring a widely circulated poster showing Uncle Sam calling on the world to ‘Help Stamp Out Index Funds’… Fidelity Chairman Edward C. Johnson led the skeptics, assuring the world that Fidelity had no intention of following Vanguard’s lead: ‘I can’t believe that the great mass of investors are going to be satisfied with just receiving average returns. The name of the game is to be the best.’ (Fidelity now runs some $38 billion in indexed assets.) (pages 375-376)

Of course, all investors would like to get the best returns if possible. Yet, by definition, investors on the whole will get average results. But that is before costs.

After costs, the average investor will get lessthan the market returns. And the amount of the shortfall will precisely equal the costs.

 

BRUTE FACTS

Bogle examines the long-term performance of mutual funds:

…In 1970, there were 355 equity mutual funds, and we have now had more than three decades over which to measure their success. We’re first confronted with an astonishing–and important–revelation: Only 147 funds survived the period. Fully 208 of those funds vanished from the scene, an astonishing 60 percent failure rate…

Now let’s look at the records of the survivors–doubtless the superior funds of the initial group. Yet fully 104 of them fell short of the 11.3 percent average annual return achieved by the unmanaged S&P 500 Index. Just 43 funds exceeded the index return. If, reasonably enough, we describe a return that comes within plus or minus a single percentage point of the market as statistical noise, 52 of the surviving funds provided a return roughly equivalent to that of the market. A total of 72 funds, then, were clear losers (i.e., by more than a percentage point), with only 23 clear winners above that threshold.

If we widen the ‘noise’ threshold to plus or minus two percentage points, we find that 43 of the 50 funds outside that range were inferior and only 7 superior–a tiny 2 percent of the 355 funds that began the period…

But I believe the evidence actually overrates the long-term achievement of the seven putatively successful funds. Is the obvious credibility of those superior records in fact credible? I’m not so sure. Those winning funds have much in common. First, each was relatively unknown (and relatively unowned by investors) at the start of the period. Their assets were tiny, with the smallest at $1.9 million, the median at $9.8 million, and the largest at $59 million. Second, their best returns were achieved during their first decade, and resulted in enormous asset growth, typically from those little widows’ mites at the start of the period to $5 billion or so at the peak, before performance started to deteriorate. (One fund actually peaked at $105 billion!) Third, despite their glowing early records, most have lagged the market fairly consistently during the past decade, sometimes by a substantial amount… The pattern for five of the seven funds is remarkably consistent: a peak in relative return in the early 1990s, followed by annual returns of the next decade that lagged the market’s return by about three percentage points per year–roughly, S&P 500 +12 percent, mutual fund +9 percent.

In the field of fund management it seems apparent that ‘nothing fails like success’… For the vicious circle of investing–good past performance draws large dollars of inflow, and having large dollars to manage crimps the very ingredients that were largely responsible for the good performance–is almost inevitable in any winning field. So even if an investor was smart enough or lucky enough to have selected one of the few winning funds at the outset, selecting such funds by hindsight–after their early success–was also largely a loser’s game. Whatever the case, the brute evidence of the past three decades makes a powerful case against the quest to find the needle in the haystack. Investors would be better served by simply owning, through an index fund, the market haystack itself. (pages 378-380)

Bogle continues:

In the field of investment management, relying on past performance simply has not worked. The past has not been prologue, for there is little persistence in fund performance. A recent study of equity mutual fund risk-adjusted returns during 1983-2003 reflected a randomness in performance that is virtually perfect. A comparison of fund returns in the first half to the second half of the first decade, in the first half to the second half of the second decade, and in the first full decade to the second full decade makes the point clear. Averaging the three periods shows that 25 percent of the top-quartile funds in the first period found themselves in the top quartile in the second–precisely what chance would dictate. Almost the same number of top-quartile funds–23 percent–tumbled to the bottom quartile, again a close-to-random outcome. In the bottom quartile, 28 percent of the funds mired there during the first half remained there in the second, while slightly more–29 percent–had actually jumped to the top quartile.

…Simply picking the top-performing funds of the past fails to be a winning strategy. What is more, even when funds succeed in outpacing their peers, they still have a way to go to match the return of the stock market index itself. (pages 381-382)

 

ARGUMENT FOR ACTIVE MANAGEMENT

Bogle writes:

…What do the proponents of active management point to? Themselves! ‘We can do it better.’ ‘We have done it better.’ ‘Just buy the (inevitably superior performing) funds that we advertise.’ It turns out, then, that the big idea that defines active management is that there is no big idea. Its proponents offer only a few good anecdotes of the past and promises for the future.

Also, it turns out that there is in fact one big idea that can be generalized without contradiction. Cost is the single statistical construct that is highly correlated with future investment success. The higher the cost, the lower the return. Equity fund expense ratios have a negative correlation coefficient of -0.61 with equity fund returns. In the fund business, you get what you don’t pay for. You get what you don’t pay for!

If we simply aggregate funds by quartile, this correlation jumps right out at us. During the decade ended November 30, 2003, the lowest-cost quartile of funds provided an average annual return of 10.7 percent; the second-lowest, 9.8 percent; the second-highest, 9.5 percent; and the highest quartile, 7.7 percent–the difference of fully three percentage points per year between the high and low quartiles, equal to a 30 percent increase in annual return! The same pattern holds irrespective of manager style or market capitalization. But of course, with index funds carrying by far the lowest costs in the industry, there are few, if any, promotions by active managers of the undeniable relationship between cost and value. (pages 385-386)

 

REASONS FOR SUCCESS OF INDEX FUNDS

Bogle explains why index funds have succeeded in beating nearly all other funds over the course of several decades or more:

The reasons for that success are the essence of simplicity: (1) the broadest possible diversification, often subsuming the entire U.S. stock market; (2) a focus on the long-term, with minimal, indeed nominal, portfolio turnover (say, 3% to 5% annually); and (3) rock-bottom cost, with neither advisory fees nor sales loads, and minimal operating expenses….

…While fund costs essentially represent the difference between success and failure for investors who seek to accumulate assets, they have gone up as index fees have come down. The initial expense ratio of our 500 Index Fund was 0.43 percent, compared to 1.40 percent for the average equity fund. Today, it is 0.18 percent or less, while the ratio for the average equity fund has risen to 1.58 percent. Add in turnover costs and sales commissions and the all-in cost of the average fund is at least 2.5 percent, suggesting a future annual index fund advantage of at least 2.3 percent per year. (page 387)

 

CONCLUSION

Bogle concludes:

Now think of this in personal terms. What difference would an index fund make in your own retirement plan over, say, 40 years? Well, let’s postulate a future long-term annual return of 8 percent on stocks. If we assume that mutual fund costs continue at their present level of at least 2.5 percent a year, an average mutual fund might return 5.5 percent. Extending this tax-deferred compounding out in time on your investment of $3,000 each year over 40 years, an investment in the stock market itself would grow to $840,000, with the market index fund not far behind. Your actively managed mutual fund would produce $430,000–only a little more than one-half as much.

Looked at from a different perspective, your retirement plan has earned a value of $840,000 before costs, and donated $410,000 of that total to the mutual fund industry. You have kept the remainder – $430,000. The financial system has consumed 48 percent of the return, and you have achieved but 52 percent of your earning potential. Yet it was you who provided 100 percent of the initial capital; the industry provided none. Confronted by the issue in this way, would an intelligent investor consider this split to represent a fair shake? Merely to ask the question is to answer it: ‘No.’ (pages 391-392)

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

 

January 10, 2022

A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market (Random House, 2017) is the autobiography of Edward O. Thorp, a remarkable person.  Here’s the beginning:

Join me in my odyssey through the worlds of science, gambling, and the securities markets.  You will see how I overcame risks and reaped rewards in Las Vegas, Wall Street, and life.  On the way, you will meet interesting people from blackjack card counters to investment experts, from movie starts to Nobel Prize winners.  And you’ll learn about options and other derivatives, hedge funds, and why a simple investment approach beats most investors in the long run, including experts.

The simple approach to which Thorp refers is investing in ultra-low-cost index funds.  Thorp’s view here is similar to Warren Buffett’s:  https://boolefund.com/warren-buffett-jack-bogle/

 

LOVING TO LEARN

Even as a young child, Thorp loved learning.  And he especially loved testing ideas by doing experiments:

A trait that showed up about this time was my tendency not to accept anything I was told until I had checked it out for myself.

From the beginning, I loved learning through experimentation and exploration how my world worked.

Thorp also demonstrated awesome powers of concentration:

When I was reading or just thinking, my concentration was so complete that I lose all awareness of my surroundings.

Thorp was influenced by a few great teachers, including Jack Chasson:

Jack was twenty-seven then, with wavy brown hair and the classic good looks of a Greek god.  He had a ready, warm smile and a way of saying something that boosted the self-esteem of everyone he met… my first great teacher…

 

SCIENCE IS MY PLAYGROUND

Thorp became fascinated by radio and electronics.  The ability to hear voices from the air amazed him:

The mechanical world of wheels, pulleys, pendulums, and gears was ordinary.  I could see, touch, and watch it in action.  But this new world was one of invisible waves that traveled through space.  You had to figure out through experiments that it was actually there and then use logic to grasp how it worked.

Eventually this led Thorp to think things through for himself and also to design experiments:

I was learning to work things out for myself, not limited to prompting from teachers, parents, or the school curriculum.  I relished the power of pure thought, combined with the logic and predictability from science.  I loved visualizing an idea and then making it happen.

Learning, thinking, and experimenting were all great fun for Thorp, leading him to contemplate becoming a scientist at a university:

An academic life was becoming my dream.  I liked all the science experiments I was doing and the knowledge they led to.  If I could have a career continuing this kind of playing, I would be very happy.  And the way to have that kind of life was by joining the academic world where they had the laboratories, the kinds of experiments and projects I enjoyed, and maybe the chance to work with other people like me.

In the summer of 1948, Thorp read through a list of 60 great novels, mostly American literature but also including some foreign authors like Dostoyevski and Stendhal.  Thorp’s teacher Jack Chasson had given him the list and then lent him the books from his personal library.

 

PHYSICS AND MATHEMATICS

Thorp was the number one student in his chemistry class, but lost that position when he was cheated.  When the mistake was not corrected, Thorp changed his major to physics:

This rash decision, which led me to change my school and my major subject, would change my whole path in life.  In hindsight, it turned out for the best, as my interests and my future were in physics and mathematics.

In graduate school, after transferring from Berkeley to UCLA, Thorp completed all the course work for the PhD and was halfway through his thesis on the structure of atomic nuclei.  All he had to do was finish the thesis work and pass a final oral exam.  But he would have to learn much more mathematics in order to finish the complex quantum mechanical calculations.  Thorp realized that he could earn a PhD in mathematics much sooner than he would likely be able to earn a PhD in physics.  So he got the PhD in mathematics.

While in graduate school, Thorp had become re-acquainted with Vivian Sinetar.  Thorp says he was lucky she was still single, despite family pressure to marry.  Also, Vivian, whose parents were immigrant Hungarian Jews, would be the first to marry outside the Jewish faith.  Fortunately, Vivian’s parents liked Thorp even though he was an academic rather than a doctor or a lawyer.

 

LAS VEGAS

Ed Thorp and his wife Vivian spent one Christmas vacation in Las Vegas because the city had turned itself into a bargain vacation spot (to attract gamblers).  The city was different at that time:

Back then the long, straight, uncrowded highway had a dozen or so one-story hotel-casino complexes scattered on either side with hundreds of yards of sand and tumbleweeds separating them.

Just before this trip to Vegas, Thorp had learned from a colleague what is now called basic strategy for blackjack.  This strategy gave the player the smallest statistical disadvantage – 0.62 percent – of any casino game.  Thorp thought he would have fun by risking a few dollars trying out basic strategy.

Before this trip, Thorp had already realized that roulette could be beaten.  Why not blackjack?

The belief that casinos must come out ahead in the long run was supported by conventional wisdom, which argued that if blackjack could be beaten, the casinos would have to either change the rules or drop the game.  Neither had happened.  But, confident from my experiments that I could predict roulette, I wasn’t willing to accept these claims about blackjack.  I decided to check for myself if the player could systematically win.

 

CONQUERING BLACKJACK

Thorp explains:

It wasn’t the money that drew me to blackjack.  Though we could certainly use extra dollars, Vivian and I expected to lead the usual low-budget academic life.  What intrigued me was the possibility that merely by sitting in a room and thinking, I could figure out how to win.

Back from vacation, Thorp went to the section of the UCLA library where mathematical and statistical research articles were.

I started with the fact that the strategy I had used in the casino assumed that every card had the same chance of being dealt as any other during play.  This cut the casino’s edge to just 0.62 percent, the best odds of any game being offered.  But I realized that the odds as the game progressed actually depended on which cards were still left in the deck and that the edge would shift as play continued, sometimes favoring the casino and sometimes the player.  The player who kept track could vary his bets accordingly. 

The player would keep his bets small when the casino had the advantage, which was most of the time.  But the player would bet much more when the odds were in his favor.  Over a large number of hands, the casino would win most of the small bets, but the player would win most of the big bets.  As long as the deal was fair””otherwise the player should learn to quit right away””Thorp’s strategy would be profitable over time.

Thorp began to do calculations to see how the player’s advantage changed based on which cards had already been played.  Thorp figured out that what mattered was the proportion of each type of card left as a percentage of the total number of cards left.

When Thorp started teaching mathematics at MIT, he had access to an IBM 704 computer, which he used to test his blackjack approximations.  Next he used the computer to figure out how the odds changed when all four of a specific card were missing from the remaining deck.  The math also showed that if removing a specific group of cards shifted the odds in one direction, adding an equal number of the same cards would move the odds the other way by the same amount.

Eventually Thorp was able to calculate the player advantage based on the specific cards that had been played.  He decided to publish his results in Proceedings of the National Academy of Sciences.  But he needed a member of the academy to approve and forward his work.  The only mathematics member of the academy at MIT was Claude Shannon.  Shannon was famous for the invention of information theory, the foundation of modern computing.

To Thorp’s surprise, Shannon was fascinated by Thorp’s ideas.  A few minutes became an hour and a half of animated dialogue.  Shannon said Thorp had likely made a theoretical breakthrough.  But he suggested the paper be titled “A Favorable Strategy for Twenty-One” instead of “A Winning Strategy for Blackjack.”

Then Shannon asked Thorp if he was working on anything else involving games of chance.  Thorp told him about his idea that roulette was predictable.  This led to several more hours of excited conversation.  Shannon and Thorp decided to work together to build a small, wearable computer that could be used to beat roulette.

 

TESTING THORP’S SYSTEM

Thorp had decided to present his blackjack system at the annual meeting of the American Mathematical Society in Washington, DC.  Thorp made this decision because previous mathematicians (centuries earlier in some cases) seemed to have proven that no casino game could be beat.  Dick Stewart of The Boston Globe had heard about Thorp’s upcoming talk.  Stewart called Thorp to ask about it.  The newspaper also sent a photographer to take Thorp’s picture.  The next morning Stewart’s article and Thorp’s picture were on the front page.

When the day of the meeting arrived, instead of the usual scholarly audience of forty or fifty, there were hundreds of curious people, including many with sunglasses, pinkie rings, or cigars.  Thorp writes:

In the abstract, life is a mixture of chance and choice.  Chance can be thought of as the cards you are dealt in life.  Choice is how you play them.  I chose to investigate blackjack.  As a result, chance offered me a new set of unexpected opportunities.

Thorp was deluged by offers to back a casino test, ranging from a few thousand dollars to $100,000.  Many were curious about whether Thorp’s system would really work.  Thorp felt he owed his readers proof.

The most promising offer came from two New York multimillionaires.  Thorp called them Mr. X and Mr. Y.  Initially, Thorp was concerned about the dangers of a bankroll provided by strangers.  But Mr. X kept calling, so Thorp finally decided to meet him.

Emmanual “Manny” Kimmel (Mr. X) arrived at Thorp’s residence in a Cadillac with two good-looking young blondes.  Kimmel introduced the two women as his “nieces.”  Kimmel dealt blackjack to Thorp for a couple of hours while asking him about his research.  Then they agreed to plan a trip to Nevada.  When Manny was leaving, he grabbed several pearl necklaces from his pocket and offered a strand to Vivian.  The pearls stayed in Thorp’s family and are now warn by his daughter.

Kimmel and his friend (Mr. Y) gave Thorp a bankroll of $100,000.  (This is the equivalent of $800,000 in 2017 dollars.)  But Thorp insisted on starting with only $10,000 in order to prove first that his system worked.

This plan, of betting only at a level at which I was emotionally comfortable and not advancing until I was ready, enabled me to play my system with a calm and disciplined accuracy.  This lesson from the blackjack tables would prove invaluable throughout my investment lifetime as the stakes grew ever larger.

Thorp’s system worked.  But the blackjack player had to understand randomness and odds over a very long series of bets.  Most small bets the casino would win.  And there would also be times when the player was unlucky on bigger bets, despite favorable odds.  But eventually, over time, Thorp’s system worked.

…the Ten-Count System had shown moderately heavy losses mixed with ‘lucky’ streaks of the most dazzling brilliance.  I learned later that this was a characteristic of a random series of favorable bets.  And I would see it again and again in real life in both the gambling and the investment worlds.

Note:  Thorp’s system worked as long as the deal was fair most of the time.  But the player had to learn to spot signs of cheating.  The player also had to quit games where losses were happening fast.  (Fast losses usually meant cheating.)

Cheating was so relentless during those days in Las Vegas that I spent as much time learning about the many ways it was being done as I did playing.  Everywhere we went, we reached a point where we were cheated, barred from play,  or the dealer reshuffled the cards after every hand.

 

PROFESSOR OR GAMBLER?

Thorp and Shannon created a wearable computer that would allow the player to win at roulette.

Thorp was now in a position to win a good deal of money””compared to his salary as a mathematics professor””by playing blackjack and roulette.  But introspection revealed to him that he would enjoy life more as an academic than as a gambler:

I was at a point in life where I could choose between two very different futures.  I could roam the world as a professional gambler winning millions per year.  Switching between blackjack and roulette, I could spend some of the winnings as perfect camouflage by also betting on other games offering a small casino edge, like craps or baccarat.

My other choice was to continue my academic life.  The path I would take was determined by my character, namely, What makes me tick?  As the Greek philosopher Heraclitus said, ‘Character is destiny.’

 

WALL STREET: THE GREATEST CASINO ON EARTH

Thorp writes:

Gambling is investing simplified.  The striking similarities between the two suggested to me that, just as some gambling games could be beaten, it might also be possible to do better than the market averages.  Both can be analyzed using mathematics, statistics, and computers.  Each requires money management, choosing the proper balance between risk and return.  Betting too much, even though each individual bet is in your favor, can be ruinous… On the other hand, playing safe and betting too little means you leave money on the table.  The psychological makeup to succeed at investing also has similarities to that for gambling.  Great investors are often good at both.

Thorp made several mistakes when he started investing.  The first stock he bought dropped 50%.  Thorp decided to wait until he could get even.  This happened after four years.  One thing Thorp learned from this experience is to avoid anchoring.

Learn about the anchoring effect here:  https://boolefund.com/why-simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Thorp’s second mistake was investing based on momentum.  It didn’t work.  Thorp learned not to expect momentum to continue unless you have good reasons to think it will.

Thorp’s third mistake was to buy silver on margin.  Initially silver rose and Thorp used the profits to buy even more silver on margin.  Then the silver price dropped, which wiped Thorp out because he was on margin. After that, silver started going up again, but Thorp had already lost his whole investment due to his use of margin.  This experience taught Thorp about proper risk management.

Thorp learned how to invest in undervalued warrants while hedging the position:

To form a hedge, take two securities whose prices tend to move together, such as a warrant and the common stock it can be used to purchase, but which are comparatively mispriced.  Buy the relatively underpriced security and sell short the relatively overpriced security.  If the proportions in the position are chosen well, then even though prices fluctuate, the gains and losses on the two sides will approximately offset or hedge each other.  If the relative mispricing between the two securities disappears as expected, close the position in both and collect a profit.

Thorp figured out a formula for pricing warrants and options.  An option to buy a stock is like a warrant except that usually the company issues warrants.  Thorp began investing portfolios for friends and acquaintances.

 

BRIDGE WITH BUFFETT

Ralph Waldo Gerard was an early investor with Thorp.  Previously, Gerard had invested in the Buffett Partnership.  Gerard was related to the father of value investing, Benjamin Graham (Buffett’s teacher and mentor).

Gerard invited Thorp and his wife to his home for dinner with Susie and Warren Buffett.  Buffett is arguably the most successful investor of all time.  But Thorp learned that Buffett had to work extremely hard in order to find a few excellent long-term investments.

By contrast, Thorp’s quantitative, statistical investment strategy seemed much easier than analyzing in detail thousands of companies.  Thorp’s approach would give him more free time to enjoy family and to pursue his academic career.

Later, Buffett invited Gerard and Thorp to his home in Emerald Bay, California for an afternoon of bridge.  Thorp:

Bridge is what mathematicians call a game of imperfect information.  The bidding, which precedes the play of the cards, gives some information about the four concealed hands held by two pairs of players who are opposing each other.  As the cards are played, players use the bidding and the cards they have seen so far to make inferences about who has the remaining unplayed cards.  The stock market also is a game of imperfect information and even resembles bridge in that both have their deceptions.  As in bridge, you do better in the market if you get more information sooner and put it to better use.  It’s no surprise that Buffett, arguably the greatest investor in history, is a bridge addict.

Thorp was impressed by Buffett and made a prediction:

Impressed by Warren’s mind and his methods, as well as his record as an investor, I told Vivian that I believed he would eventually become the richest man in America.  Buffett was an extraordinarily smart evaluator of underpriced companies, so he could compound money much faster than the average investor.  He also could continue to rely mainly on his own talent even as his capital grew to an enormous amount.  Warren furthermore understood the power of compound interest and, clearly, planned to apply it over a long time.

Thorp partnered with a New York stockbroker, Jay Regan, who had studied philosophy at Dartmouth.  Together, they launched Convertible Hedge Associates””later renamed Princeton Newport Partners.  They aimed to raise $5 million, but only reached $1.4 million.  They went ahead anyways.

 

GOING INTO PARTNERSHIP

Princeton Newport Partners (PNP) specialized in the hedging of convertible securities””warrants, options, convertible bonds and preferreds, and other types of derivative securities.  PNP not only hedged each individual position.  But it also hedged the portfolio against changes in interest rates and changes in the overall market level.  PNP’s near total reliance on quantitative methods””using mathematical formulas, economic models, and computers””made them the earliest “quants.”

Thorp was motivated to reduce risk:

Influenced by having been born during the Great Depression and by my early investment experiences, I made reducing risk a central feature of my investing approach.

The hedges protected us against losses but at the expense of giving up some of the gains in the big up-markets.

In 1973-1974, each $1,000 invested in the S&P 500 would have shrunk to $618, whereas each $1,000 invested in PNP grew to $1,160.

Thorp’s wife, Vivian, not only raised their three children.  She was also active in local politics, helping reelect a decent congressman.  And Vivian organized and ran a large phone bank that helped elect the first black man to a California statewide office.  Moreover, she influenced many people one on one.

One time, a woman complained to Vivian about “those Jews.”  Vivian was Jewish and had lost several relatives in Nazi World War II prison camps.  Ed Thorp:

When she told us about meeting the woman, we expected to hear how she tore her to shreds.  Explaining why she did not, Vivian pointed out that the woman would have learned nothing and simply would have become an enemy.  Vivian patiently educated this basically good person and they became friends for the rest of their lives.

Thorp’s PhD thesis had been in pure mathematics and this continued to be his focus for fifteen years.  Although Thorp loved teaching and research, eventually he resigned his full professorship at the University of California, Irvine.  He felt a sense of loss, but it turned out to be for the best.  Thorp continued his friendships and research collaborations.  He continued to present his work at meetings and publish it in the mathematical, financial, and gambling literature.

 

FRONT-RUNNING THE QUANTITATIVE REVOLUTION

Thorp and his colleagues continued to solve problems for valuing derivatives before academics did.  This gave PNP a large edge from 1967 to 1988, when PNP closed.

Hedging with derivatives was a key source of profits for PNP during its entire nineteen years.  Such hedging also became a core strategy for many later hedge funds like Citadel, Stark, and Elliott, which each went on to manage billions.

Some risks cannot be hedged:

There is another kind of risk on Wall Street from which computers and formulas can’t protect you.  That’s the danger of being swindled or defrauded.  Being cheated at cards in the casinos in the 1960s was valuable preparation for the far greater scale of dishonesty I would encounter in the investment world.  The financial press reveals new skulduggery on a daily basis.

 

RISE…

PNP’s dream for the 1980s was to expand their expertise into new areas.

Of the scores of indicators we systematically analyzed, several correlated strongly with past performance.  Among them were earnings yield (annual earnings divided by price), dividend yield, book value divided by price, momentum, short interest…, earnings surprise…, purchases and sales by company officers, directors, and large shareholders, and the ratio of total company sales to the market price of the company.  We studied each of these separately, then worked out how to combine them.  When the historical patterns persisted as prices unfolded into the future, we created a trading system called MIDAS (multiple indicator diversified asset system) and used it to run a separate long/short hedge fund (long the “good” stocks, short the “bad” ones).  The power of MIDAS was that it applied to the entire multitrillion-dollar stock market, with the possibility of investing very large sums.

From November 1, 1979 through January 1, 1988, PNP’s capital expanded from $28.6 million to $273 million.  The partnership earned 22.8 percent per year before fees, which meant 18.2 percent per year for limited partners.

Furthermore, PNP invented excellent new products that could allow the fund to manage billions.  They included:

• State-of-the-art convertible, warrant, and option computerized analytic models and trading systems
• Statistical arbitrage
• Expert investments based on interest rates
• MIDAS
• OSM Partners, a “fund of hedge funds”

 

…AND FALL

In the 1970s, less established companies had to scramble for funding.  A young financial innovator named Michael Milken had an idea:

Milken’s group underwrote issues of low-rated, high-yielding bonds””the so-called junk bonds””some of which were convertible or came with warrants to purchase stock… Filling a gaping need and hungry demand in the business community, Milken’s group became the greatest financing engine in Wall Street history.

Such innovation outraged the old line establishment of corporate America, who were initially transfixed like deer in the headlights as a horde of entrepreneurs, funded with seemingly unlimited Drexel-generated cash, began a wave of unfriendly takeovers.  Many old firms were vulnerable because the officers and directors had done a poor job of investing the shareholders’ equity.  With subpar returns on capital, the stocks were cheap…

The officers and directors of America’s big corporations were happy with the way things had been.  They enjoyed their hunting lodges and private jets, made charitable donations for their personal aggrandizement and objectives, and granted themselves generous salaries, retirement plans, bonuses of cash, stock, and stock options, and golden parachutes.  All these things were designed by and for themselves and paid for with corporate dollars, the expenses routinely ratified by a scattered and fragmented shareholder base.  Economists call this conflict of interest between management, or agents, and the shareholders, who are the real owners, the agency problem.  It continues today, one example being the massive continuing grants of stock options by management to itself…

Rudolph Giuliani, U.S. Attorney for the Southern District of New York, was on a campaign to prosecute real and alleged Wall Street criminals.  As a part of his effort to prosecute Michael Milken at Drexel Burnham and Robert Freeman at Goldman Sachs, Giuliani went after Thorp’s partner Jay Regan, who knew both Milken and Freeman well.

Giuliani went after the Princeton office of PNP.  The Newport office, where Thorp and forty others worked, did not have any knowledge of the alleged acts in the Princeton office.  No one at the Newport office was implicated in, or charged with, any wrongdoing in this (or any other) matter.

To apply more pressure, the U.S. Attorney began contacted the limited partners of PNP.  They subpoened them to come to New York and testify before the grand jury.  Thorp explains that the limited partners were passive participants in PNP.  The subpoenas thus had no real value for Giuliani’s case.  It seems Giuliani wanted to disturb and upset these limited partners so that they might withdraw from PNP.

In the end, convictions for racketeering and tax fraud against a few PNP defendants were thrown out by the Second Court of Appeals.  Thorp writes:

In January 1992, having achieved their real goal, which was to convict Milken and Freeman, the prosecutors dropped the remaining charges against four of the five PNP defendants and a relate charge against the Drexel trader.  Princeton’s head trader and the Drexel defendant were still facing fines and three-month prison terms for their remaining counts.  In September 1992, a federal judge vacated these sentences as well.

Thorp later explains:

The old establishment financiers were lucky in that prosecutors would find numerous violations of securities laws within the Milken group and among its allies, associates, and clients.  However, it is difficult to judge how relatively bad these were, compared with the incessant violations that have always been, and continue to be, endemic in business and finance, because only a few of the many violators  are caught, and when they are prosecuted it may be for only a tiny fraction of their offenses.  This contrasts with the case of Drexel, where the searchlight of government was focused to reveal as many violations as possible.  It’s like the case of the man who was cited three times in a single year for driving while intoxicated.  His neighbor would also drink and drive, but was never pulled over.  Who is the greater criminal?  Now suppose I tell you that the caught man did it only three times and was apprehended every time, whereas his neighbor did it a hundred times and was never caught.  How could this happen?  What if I tell you that the two men are bitter business rivals and that the traffic cop’s boss, the police chief, gets large campaign contributions from the man who got no traffic citations.  Now who is the greater criminal?

Thorp considered launching a partnership that would be similar to PNP.  But he loved the quantitative analysis part of the business, not operations and marketing.  So he decided to wind down the Newport office.

 

PERIOD OF ADJUSTMENT

Although the closing of PNP erased billions in future wealth for Thorp and his colleagues, Thorp and his wife had more than enough money to be free to spend their time exactly as they wanted.

Around this time, Thorp discovered the greatest financial fraud.  He had been hired to examine some hedge fund investments.  Thorp approved them with one exception: Bernard Madoff Investment.

Madoff claimed to use a split-strike price strategy:  He would buy a stock, sell a call option at a higher price, and use the proceeds to pay for a put option at a lower price.

I explained that, according to financial theory, the long-run impact on portfolio returns from many properly priced options with zero net proceeds should also be zero.  So we expect, over time, that the client’s portfolio return should be roughly the same as the return on equities.  The returns Madoff reported were too large to be believed.  Moreover, in months when stocks are down, the strategy should produce a loss””but Madoff wasn’t reporting any losses.  After checking the client’s account statements I found that losing months for the strategy were magically converted to winners by short sales of S&P Index futures.  In the same way, months that should have produced very large wins were ‘smoothed out.’

Thorp:

…At my suggestion, the client then hired my firm to conduct a detailed analysis of their individual transactions to prove or disprove my suspicions that they were fake.  After analyzing about 160 individual option trades, we found that for half of them no trades occurred on the exchange where Madoff said that they supposedly took place.  For many of the remaining half that did trade, the quantity reported by Madoff just for my client’s two accounts exceeded the entire volume reported for everyone.  To check the minority of remaining trades, those that did not conflict with the prices and volumes reported by the exchanges, I asked an official at Bear Stearns to find out in confidence who all the buyers and sellers of the options were.  We could not connect any of them to Madoff’s firm.

Thorp had proved Madoff’s investment operation was a fraud.  Madoff was running a Ponzi scheme.

In 1991, Thorp was seeking a partner to whom to sell their statistical arbitrage software.  This led him to meet with Bruce Kovner, a successful commodities trader.

About this time he realized large oil tankers were in such oversupply that the older ones were selling for little more than scrap value.  Kovner formed a partnership to buy one.  I was one of the limited partners.  Here was an interesting option.  We were largely protected against loss because we could always sell the tanker for scrap, recovering most of our investment;  but we had a substantial upside:  Historically, the demand for tankers had fluctuated widely and so had their price.  Within a few years, our refurbished 475,000-ton monster, the Empress Des Mers, was profitably plying the world’s sea-lanes stuffed with oil.  I liked to think of my ownership as a twenty-foot section just forward of the bridge… The Empress Des Mers operated profitably into the twenty-first century, when the saga finally ended.  Having generated a return on investment of 30 percent annualized, she was sold for scrap in 2004, fetching almost $23 million, far more than her purchase price of $6 million.

Thorp discusses traders who always try to save a tiny amount on each trade.  The problem is that the trader may do this successfully twenty times in a row, but then miss a trade that goes up so much that it wipes out the savings on the previous twenty trades.

What the hagglers and the traders do reminds me of the behavioral psychology distinction between two extremes on a continuum of types:  satisficers and maximizers.  When a maximizer goes shopping, looks for a handyman, buys gas, or plans a trip, he searches for the best (maximum) possible deal.  Time and effort don’t matter much.  Missing the very best deal leads to regret and stress.  On the other hand, the satisficer, so-called because he is satisfied with a result that is close to the best, factors in the costs of searching and decision making, as well as the risk of losing a near-optimal opportunity and perhaps never finding anything as good again.

This is reminiscent of the so-called secretary or marriage problem in mathematics.  Assume that you will interview a series of people, from which you will choose one.  Further, you must consider them one at a time, and having once rejected someone, you cannot reconsider.  The optimal strategy is to wait until you have seen about 37 percent of the prospects, then choose the next one you see who is better than anybody among this first 37 percent that you passed over.  If no one is better you are stuck with the last person on the list.

 

SWINDLES AND HAZARDS

Thorp:

…Some exchanges, such as NASDAQ, let HF [High Frequency] traders peek at customer orders ahead of everyone else for thirty milliseconds before the order goes to the exchange.  Seeing an order to buy, for instance, the HF traders can buy first, pushing the stock price up, then resell to the customer at a profit.  Seeing someone’s order to sell, the HF trader sells first, causing the stock to fall, and then buys it back at the lower price.  How is this different from the crime of front-running, described in Wikipedia as ‘the illegal practice of a stock broker executing orders on a security for its own account while taking advantage of advance knowledge of pending orders from its customers’?

Some securities industry spokesmen argue that harvesting this wealth from investors somehow makes the markets more efficient and that ‘markets need liquidity.’  Nobel Prize-winning economist Paul Krugman disagrees sharply, arguing that high-frequency trading is simply a way of taking wealth from ordinary investors, serves no useful purpose, and wastes national wealth because the resources consumed create no social good.

Since the more the rest of us trade the more we as a group lose to the computers, here’s one more reason to buy and hold rather than trade, unless you have a big enough edge.

 

BUYING LOW, SELLING HIGH

Thorp discusses a statistical arbitrage investment project:

The idea of the project was to study how the historical returns of securities were related to various characteristics, or indicators.  Among the scores of fundamental and technical measures we considered were the ratio of earnings per share to price per share, known as the earnings yield, the liquidation or “book” value of the company compared with its market price, and the total market value of the company (its “size”).  Today our approach is well known and widely explored but back in 1979 it was denounced by massed legions of academics who believed market prices already had fully adjusted to such information.  Many practitioners disagreed.  The time was right for our project because the necessary high-quality databases and the powerful new computers with which to explore them were just becoming affordable.

The idea for statistical arbitrage was based on the discovery (by one of Thorp’s researchers) that the stocks that had gone up the most over the previous two weeks did the worst as a group over the ensuing few weeks, while the stocks that had gone down the most over the previous two weeks did the best.

In 1994, Thorp launched a new investment partnership, Ridgeline Partners.  Limited partners gained 18 percent per year over eight and a quarter years.

We charged Ridgeline Partners 1 percent per year plus 20 percent of net new profits.  We voluntarily reduced fees during a period when we felt disappointed in our performance.  We gave back more than $1 million to the limited partners.  Some of today’s greedy hedge fund managers might say our return of fees was economically irrational, but our investors were happy and we nearly always had a waiting list.  Ridgeline was closed a large part of the time to new investors, and current partners were often restricted from adding capital.  To maintain higher returns, we sometimes even reduced our size by returning capital to partners.

Instead of charging more fees, Thorp says he sought to treat limited partners as he would wish to be treated if he were in their place.  Thorp closed the fund down in the fall of 2002 because returns had declined due to more hedge funds using statistical arbitrage programs.  More importantly, Ed and Vivian wanted time to travel, read, and learn, and to be with their family.

 

HEDGING YOUR BETS

Thorp:

The consensus of industry studies of hedge fund returns to investors seems to be that, considering the level of risk, hedge funds on average once gave their investors extra return, but this has faded as the industry expanded.  Later analyses say average results are worse than portrayed.  Funds voluntarily report their results to the industry databases.  Winners tend to participate much more than losers.  One study showed that this doubled the reported average annual return for funds as a group from an actual 6.3 percent during 1996-2014 to a supposed 12.6 percent.

The study goes one to point out that if returns over the years are given weights that correspond to the dollars invested, then the returns are ‘only marginally higher than risk-free [U.S. Treasury Bonds] rates of return.’  Another reason that reports by the industry look better than what investors experienced is that they combined higher-percentage returns from the earlier years, when the total invested in hedge funds was smaller, with the lower-percentage returns later, when they managed much more money.

It’s difficult to get an edge picking stocks.  Hedge funds are little businesses just like companies that trade on the exchanges.  Should one be any better at picking hedge funds than we are at picking stocks?

Thorp points out that you will rarely find an investment that is better than an ultra-low-cost index fund over time.  Also, some hedge funds and mutual funds create spectacular records early on but mediocre results when assets under management have grown:

One method that leads to this has also been used to launch new mutual funds.  Fund managers sometimes start a new fund with a small amount of capital.  They then stuff it with hot IPOs (initial public offerings) that brokers give them as a reward for the large volume of business they have been doing through their established funds.  During this process of ‘salting the mine,’ the fund is closed to the public.  When it establishes a stellar track record, the public rushes in, giving the fund managers a huge capital base from which they reap large fees.  The brokers who supplied the hot IPOs are rewarded by a flood of additional business from the triumphant managers of the new fund.  The available volume of hot IPOs is too small to help returns much once the fund gets big, so the track record declines to mediocrity.  However, the fund promoters can use more hot IPOs to incubate yet another spectacularly performing new fund;  and so it goes on.

Like Buffett, Thorp predicts the gradual disappearance of any excess returns produced by hedge funds as a group.  Here is Buffett’s view:  https://boolefund.com/warren-buffett-jack-bogle/

 

BEAT MOST INVESTORS BY INDEXING

Thorp:

Call any investment that mimics the whole market of listed U.S. securities ‘passive’ and notice that since each of these passive investments acts just like the market, so does a pool of all of them.  If these passive investors together own, say, 15 percent of every stock, then ‘everybody else’ owns 85 percent and, taken as a group, their investments also are like one giant index fund.  But ‘everybody else’ means all the active investors, each of whom has his own recipe for how much to own of each stock and none of whom has indexed.  As Nobel Prize winner Bill Sharpe says, it follows from the laws of arithmetic that the combined holdings of all the active investors also replicates the index.

Reducing risk through diversification is a reason to own an index fund.  An even more important reason to own an index fund is to reduce your costs.  Ultra-low costs are why index funds outperform, necessarily, the vast majority of investors, especially over the course of several decades.  Thorp explains:

Investors who don’t index pay on average an extra 1 percent a year in trading costs and another 1 percent to what Warren Buffett calls ‘helpers'””the money managers, salespeople, advisers, and fiduciaries that permeate all areas of investing.  As a result of these costs, active investors as a group trail the index by 2 percent or so, whereas the passive investor who selects a no-load (no sales fee), low-expense-ratio (low overhead and low management fee) index fund can pay less than 0.25 percent in fees and trading costs.  From the gambling perspective, the return to an active investor is that of a passive investor plus the extra gain or loss from paying (on average) 2 percent a year to toss a fair coin in some (imaginary) casino.  Taxable active investors do even worse, because a high portfolio turnover means short-term capital gains, which currently are taxed at a higher rate than gains from securities, the sales of which have been deferred for a year.

Furthermore, notes Thorp, one way an investor could mimic an index fund is simply to buy a portfolio of at least twenty stocks.  If the choices are randomized, then the returns from this portfolio should track the index over time.  Consider, for instance, that the Dow Jones Industrial Average””comprised of thirty stocks””has closely tracked the S&P 500 Index over time.

Moreover, the portfolio of twenty stocks could be even lower cost than an ultra-low-cost index fund because the 20-stock portfolio likely would not require any trading at all, whereas a broad market ultra-low-cost index fund would have to make minor adjustments over time in order to keep tracking the index.

 

CAN YOU BEAT THE MARKET?  SHOULD YOU EVEN TRY?

Thorp observes that for a perfectly efficient market, one you can’t beat, we expect:

• All information to be instantly available to many participants.
• Many participants to be financially rational.
• Many participants to be able instantly to evaluate all available relevant information and determine the current fair price of every security.
• New information to cause prices immediately to jump to the new fair price, preventing anyone from gaining an excess market return by trading at intermediate prices during the transition.

Supporters of the EMH (Efficient Market Hypothesis) typically argue that these conditions hold as an approximation.

In the real world of investing, Thorp writes that the market is somewhat inefficient.  In particular:

• Some information is instantly available to the minority that happen to be listening at the right time and place.  Much information starts out known only to a limited number of people, then spreads to a wider group in stages.  This spreading could take from minutes to months, depending on the situation.  The first people to act on the information capture the gains.  The others get nothing or lose.  (Note:  The use of early information by insiders can be either legal or illegal, depending on the type of information, how it is obtained, and how it’s used.)
• Each of us is financially rational only in a limited way.  We vary from those who are almost totally irrational to some who strive to be financially rational in nearly all their actions.  In real markets the rationality of the participants is limited.
• Participants typically have only some of the relevant information for determining the fair price of a security.  For each situation, both the time to process the information and the ability to analyze it generally very widely.
• The buy and sell orders that come in response to an item of information sometimes arrive in a flood within a few seconds, causing the price to gap or nearly gap to the new level.  More often, however, the reaction to news is spread out over minutes, hours, days, or months, as the academic literature documents.

These realities tell us how to beat the market, says Thorp:

• Get good information early.  How do you know if your information is good enough or early enough?  If you are not sure, then it probably isn’t.
• Be a disciplined rational investor.  Follow logic and analysis rather than sales pitches, whims, or emotion.  Assume you may have an edge only when you can make a rational affirmative case that withstands your attempts to tear it down.  Don’t gamble unless you are highly confident you have the edge.  As Buffett says, ‘Only swing at the fat pitches.’
• Find a superior method of analysis.  Ones that you have seen pay off for me include statistical arbitrage, convertible hedging, the Black-Scholes formula, and card counting at blackjack.  Other winning strategies include superior security analysis by the gifted few and the methods of the better hedge funds.
• When securities are known to be mispriced and people take advantage of this, their trading tends to eliminate the mispricing.  This means the earliest traders gain the most and their continued trading tends to reduce or eliminate the mispricing.  When you have identified an opportunity, invest ahead of the crowd.

Thorp sums it up:

Note that market inefficiency depends on the observer’s knowledge.  Most market participants have no demonstrable advantage.  For them, just as the cards in blackjack or the numbers at roulette seem to appear at random, the market appears to be completely efficient.

To beat the market, focus on investments well within your knowledge and ability to evaluate, your ‘circle of competence.’  Be sure your information is current, accurate, and essentially complete.  Be aware that information flows down a ‘food chain,’ with those who get it first ‘eating’ and those who get it late being eaten.  Finally, don’t bet on an investment unless you can demonstrate by logic, and if appropriate by track record, that you have an edge.

 

THOUGHTS

Thorp wraps up his book by sharing some of what he learned on his odyssey through science, mathematics, gambling, hedge funds, finance, and investing:

Education has made all the difference for me.  Mathematics taught me to reason logically and to understand numbers, tables, charts, and calculations as second nature.  Physics, chemistry, astronomy, and biology revealed wonders of the world, and showed me how to build models and theories to describe and to predict.  This paid off for me in both gambling and investing.

Education builds software for your brain.  When you’re born, think of yourself as a computer with a basic operating system and not much else.  Learning is like adding programs, big and small, to this computer, from drawing a face to riding a bicycle to reading to mastering calculus.  You will use these programs to make your way in the world.  Much of what I’ve learned came from schools and teachers.  Even more valuable, I learned at an early age to teach myself.  This paid off later on because there weren’t any courses in how to beat blackjack, build a computer for roulette, or launch a market-neutral hedge fund.

I found that most people don’t understand the probability calculations needed to figure out gambling games or to solve problems in everyday life.  We didn’t need that skill to survive as a species in the forests and jungles.  When a lion roared, you instinctively climbed the nearest tree and thought later about what to do next.  Today we often have the time to think, calculate, and plan ahead, and here’s where math can help us make decisions…

Thorp later writes that economists have found one factor that explains a nation’s future economic growth more than any other:  its output of scientists and engineers.  Therefore it’s crucial to have the best education system we can.  It’s essential that we strive to keep talented American-born scientists and engineers in the United States, and that we also seek to keep gifted foreign-born scientists and engineers after they have received advanced degrees in the United States.  Thorp:

To starve education is to eat our seed corn.  No tax today, no technology tomorrow.

Thorp concludes:

Life is like reading a novel or running a marathon.  It’s not so much about reaching a goal but rather about the journey itself and the experiences along the way.  As Benjamin Franklin famously said, ‘Time is the stuff life is made of,’ and how you spend it makes all the difference.

…Whatever you do, enjoy your life and the people who share it with you, and leave something good of yourself for the generations to follow.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

January 2, 2022

Verde AgriTech (AMHPF) is an agri-tech company founded in 2005 by Cristiano Veloso.  Veloso comes from a family of farmers.  He is highly educated and intellectually curious.

Verde owns the first Brazilian potash mine in 33 years.  Brazil imports 94% of its potash.  Verde is the lowest cost potash producer“”its cost is $170 per ton of potash””for two mains reasons:

• • Because Verde produces its potash in Brazil, that makes it lower cost than most potash producers who exist outside of Brazil and have to pay additional shipping costs;
  • Verde’s production process does not use any water or chemicals, and no tailings dams or waste generation is needed.  The company’s deposit is at the surface, while most potash mines are deep.

Because Verde is the lowest cost producer, it has a sustainable competitive advantage that should allow it to earn high margins””and high return on invested capital””for many years.

Furthermore, traditional potash has high salinity and is therefore bad for the biodiversity of the soil.  The world uses 61.5 million tons of potassium chloride each year, which is equivalent to 460 billion liters of bleach.

Verde’s product””K Forte””is a salinity and chloride free potassium specialty fertilizer.  Verde produces K Forte by using the naturally occurring glauconite in the Alto Paranaiba region in the state of Minas Gerais in Brazil.  Whereas traditional potash contains potassium and chlorine, K Forte contains potassium, silicon, magnesium, and manganese. As a result, K Forte is better for the soil and leads to healthier plants and ultimately healthier food.  K Forte also improves the carbon capture capacity of the soil.  The company’s mission: “Our purpose is to improve the health of people and the planet.”

Verde currently has only Plant 1 running.  It is maxed out at 400,000 tons per year (tpy).  The company has already maxed out Plant 1’s production.  Plant 2 is expected to start producing in Q3 2022 and it will be able to produce 800,000 tpy.  Plant 3 will be even larger.

Potash prices could remain high for much of 2022.  See: https://www.dtnpf.com/agriculture/web/ag/crops/article/2021/12/17/sanctions-affect-world-potash-supply

More importantly, potash prices could be high for much of the next decade if it turns out to be a commodities bull market.  See: https://www.bridgewater.com/its-mostly-a-demand-shock-not-a-supply-shock-and-its-everywhere

The current market cap of AMHPF is $110.57 million, while the stock price is $2.20.  The enterprise value (EV) is $114.47 million.  Assuming that production is maxed out for Plants 1 and 2, that would be 1,200,000 tpy.  Assuming a potash price of $400, that would be $66.67 per ton ($400 for 6 tons of K Forte).  Revenue would be $80.00 million.

Operating cost per ton is $28.33 ($170 for 6 tons of K Forte).  So operating costs would be $34.00 million.  That means operating profit would be $46.00 million.  Net profit would be $41.00 million.  EBITDA would be $47.00 million.  (That puts the EBITDA margin at 58.8%.  If potash prices are higher, the EBITDA margin can reach 70-80%.)   Operating cash flow can be estimated at $47.00 million.

Using these figures, we get the following multiples:

• • EV/EBITDA = 2.44
  • P/E = 2.70
  • P/B = 1.76
  • P/CF = 2.35
  • P/S = 1.38

Also note that the NAV of the company””based on its glauconite resources and on the fact that the total Brazilian market for potash is 20 million tpy””is $49.77 per share in Canadian dollars or $38.82 per share in U.S. dollars.

The CEO Cristiana Veloso owns 20% of the shares.  Also, he has been adamant about not using shares to fund growth.  For the construction of its Plant 2, the company is using $3.75 million in debt and $1.41 million in internally generated cash flows.

On the most recent quarterly call, Veloso commented on the triple digit growth: “Blitzscaling is never an easy endeavour, but I’m confident we have built the right team to continue succeeding at this challenge.  It is still day 1 at Verde.”

The company board includes people like Alysson Paolinelli, a professor, former minister of agriculture, and a World Food Prize winner.

Verde AgriTech has a Piotroski F_Score of 7, which is good.

Net debt is low:  Cash is $2.4 million.  Debt is $2.8 million.  TL/TA is 30%, which is good.

Intrinsic value scenarios:

• • Low case: Assume $400 per ton for potash prices””the current price is over $700″”and that plant 2 is delayed.  In this scenario, revenue could be $27 million and net profit $5 million.  With a P/E of 15, the stock would trade at $1.49, which is 32% below today’s $2.20.
  • Mid case: Assume $400 per ton for potash prices””the current price is over $700.  With plant 1 and plant 2 producing, production would be 1,200,000 tpy.  That translates into $80 million in revenue and $41 million in net profit.  The company should have at least a P/E of 15.  That would put the intrinsic value of the stock at $12.24, which is over 450% higher than today’s $2.20.
  • High case: Assume $400 per ton for potash prices””the current price is over $700.  With plant 1 and plant 2 producing, production would be 1,200,000 tpy.  That translates into $80 million in revenue and $41 million in net profit.  The company should have at least a P/E of 25 because of its significant growth potential.  That would put the intrinsic value of the stock at $20.39, which is over 825% higher than today’s $2.20.
  • Very high case:  NAV per share””based on the company’s glauconite resources and on the size of the Brazilian market””is $38.82.  That is over 1,660% higher than today’s $2.20.  In other words, a 16-bagger.  (Note that the company has 777.28 million tons of glauconite reserves, enough to supply most of the Brazilian market for decades.)

Risks

The biggest risks are market adoption risk, currency risk, political risk, and commodity price risk.

So far, farmers are adopting Verde’s products, as evidenced by the sold out production.

The Brazilian real seems to be fairly stable at $0.18 per U.S. dollar.  But if there is a flight to safety during a bear market or recession, the Brazilian real would decline versus the U.S. dollar.

One political risk is that their application for the license needed to start Plant 2 in Q3 2022 won’t be approved in time.  Another political risk is government corruption and the lack of growth-oriented reforms.

There is a risk that the price of potash could fall significantly.  This seems unlikely, given double digit inflation in many parts of the world.  It seems to be a commodity bull market.  It’s even possible that potash prices will remain at $600-800 per ton, which would mean huge profits for Verde Agritech as long as it can fully increase its production as planned.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.