Buffett’s Best: Microcap Cigar Butts

(Image:  Zen Buddha Silence by Marilyn Barbone)

October 8, 2017

Warren Buffett, the world’s greatest investor, earned the highest returns of his career from microcap cigar butts.  Buffett wrote in the 2014 Berkshire Letter:

My cigar-butt strategy worked very well while I was managing small sums.  Indeed, the many dozens of free puffs I obtained in the 1950’s made the decade by far the best of my life for both relative and absolute performance.

Even then, however, I made a few exceptions to cigar butts, the most important being GEICO.  Thanks to a 1951 conversation I had with Lorimer Davidson, a wonderful man who later became CEO of the company, I learned that GEICO was a terrific business and promptly put 65% of my $9,800 net worth into its shares.  Most of my gains in those early years, though, came from investments in mediocre companies that traded at bargain prices.  Ben Graham had taught me that technique, and it worked.

But a major weakness in this approach gradually became apparent:  Cigar-butt investing was scalable only to a point.  With large sums, it would never work well…

Before Buffett led Berkshire Hathaway, he managed an investment partnership from 1957 to 1970 called Buffett Partnership Ltd. (BPL).  While running BPL, Buffett wrote letters to limited partners filled with insights (and humor) about investing and business.  Jeremy C. Miller has written a great book— Warren Buffett’s Ground Rules (Harper, 2016)—summarizing the lessons from Buffett’s partnership letters.

This blog post considers a few topics related to microcap cigar butts:

  • Net Nets
  • Dempster: The Asset Conversion Play
  • Liquidation Value or Earnings Power?
  • Mean Reversion for Cigar Butts
  • Focused vs. Statistical
  • The Rewards of Psychological Discomfort
  • Conclusion



Here Miller quotes the November 1966 letter, in which Buffett writes about valuing the partnership’s controlling ownership position in a cigar-butt stock:

…Wide changes in the market valuations accorded stocks at some point obviously find reflection in the valuation of businesses, although this factor is of much less importance when asset factors (particularly when current assets are significant) overshadow earnings power considerations in the valuation process…

Ben Graham’s primary cigar-butt method was net nets.  Take net current asset value minus ALL liabilities, and then only buy the stock at 2/3 (or less) of that level.  If you buy a basket (at least 20-30) of such stocks, then given enough time (at least a few years), you’re virtually certain to get good investment results, predominantly far in excess of the broad market.

A typical net-net stock might have $30 million in cash, with no debt, but have a market capitalization of $20 million.  Assume there are 10 million shares outstanding.  That means the company has $3/share in net cash, with no debt.  But you can buy part ownership of this business by paying only $2/share.  That’s ridiculously cheap.  If the price remained near those levels, you could in theory buy $1 million in cash for $667,000—and repeat the exercise many times.

Of course, a company that cheap almost certainly has problems and may be losing money.  But every business on the planet, at any given time, is in either one of two states:  it is having problems, or it will be having problems.  When problems come—whether company-specific, industry-driven, or macro-related—that often causes a stock to get very cheap.

The key question is whether the problems are temporary or permanent.  Statistically speaking, many of the problems are temporary when viewed over the subsequent 3 to 5 years.  The typical net-net stock is so extremely cheap relative to net tangible assets that usually something changes for the better—whether it’s a change by management, or a change from the outside (or both).  Most net nets are not liquidated, and even those that are still bring a profit in many cases.

The net-net approach is one of the highest-returning investment strategies ever devised.  That’s not a surprise because net nets, by definition, are absurdly cheap on the whole, often trading below net cash—cash in the bank minus ALL liabilities.

Buffett called Graham’s net-net method the cigar butt approach:

…I call it the cigar butt approach to investing.  You walk down the street and you look around for a cigar butt someplace.  Finally you see one and it is soggy and kind of repulsive, but there is one puff left in it.  So you pick it up and the puff is free – it is a cigar butt stock.  You get one free puff on it and then you throw it away and try another one.  It is not elegant.  But it works.  Those are low return businesses.

Link: http://intelligentinvestorclub.com/downloads/Warren-Buffett-Florida-Speech.pdf

(Photo by Sky Sirasitwattana)

When running BPL, Buffett would go through thousands of pages of Moody’s Manuals (and other such sources) to locate just one or a handful of microcap stocks trading at less than liquidation value.  Other leading value investors have also used this technique.  This includes Charlie Munger (early in his career), Walter Schloss, John Neff, Peter Cundill, and Marty Whitman, to name a few.

The cigar butt approach is also called deep value investing.  This normally means finding a stock that is available below liquidation value, or at least below net tangible book value.

When applying the cigar butt method, you can either do it as a statistical group approach, or you can do it in a focused manner.  Walter Schloss achieved one of the best long-term track records of all time—near 21% annually (gross) for 47 years—using a statistical group approach to cigar butts.  Schloss typically had a hundred stocks in his portfolio, most of which were trading below tangible book value.

At the other extreme, Warren Buffett—when running BPL—used a focused approach to cigar butts.  Dempster is a good example, which Miller explores in detail in his book.



Dempster was a tiny micro cap, a family-owned company in Beatrice, Nebraska, that manufactured windmills and farm equipment.  Buffett slowly bought shares in the company over the course of five years.

(Photo by Digikhmer)

Dempster had a market cap of $1.6 million, about $13.3 million in today’s dollars, says Miller.

  • Note:  A market cap of $13.3 million is in the $10 to $25 million range—among the tiniest micro caps—which is avoided by nearly all investors, including professional microcap investors.

Buffett’s average price paid for Dempster was $28/share.  Buffett’s estimate of liquidation value early on was near $35/share, which is intentionally conservative.  Miller quotes one of Buffett’s letters:

The estimated value should not be what we hope it would be worth, or what it might be worth to an eager buyer, etc., but what I would estimate our interest would bring if sold under current conditions in a reasonably short period of time.

To estimate liquidation value, Buffett followed Graham’s method, as Miller explains:

  • cash, being liquid, doesn’t need a haircut
  • accounts receivable are valued at 85 cents on the dollar
  • inventory, carried on the books at cost, is marked down to 65 cents on the dollar
  • prepaid expenses and “other” are valued at 25 cents on the dollar
  • long-term assets, generally less liquid, are valued using estimated auction values

Buffett’s conservative estimate of liquidation value for Dempster was $35/share, or $2.2 million for the whole company.  Recall that Buffett paid an average price of $28/share—quite a cheap price.

Even though the assets were clearly there, Dempster had problems.  Stocks generally don’t get that cheap unless there are major problems.  In Dempster’s case, inventories were far too high and rising fast.  Buffett tried to get existing management to make needed improvements.  But eventually Buffett had to throw them out.  Then the company’s bank was threatening to seize the collateral on the loan.  Fortunately, Charlie Munger—who later became Buffett’s business partner—recommended a turnaround specialist, Harry Bottle.  Miller:

Harry did such an outstanding job whipping the company into shape that Buffett, in the next year’s letter, named him “man of the year.”  Not only did he reduce inventories from $4 million to $1 million, alleviating the concerns of the bank (whose loan was quickly repaid), he also cut administrative and selling expenses in half and closed five unprofitable branches.  With the help of Buffett and Munger, Dempster also raised prices on their used equipment up to 500% with little impact to sales volume or resistance from customers, all of which worked in combination to restore a healthy economic return in the business.

Miller explains that Buffett rationally focused on maximizing the return on capital:

Buffett was wired differently, and he achieves better results in part because he invests using an absolute scale.  With Dempster he wasn’t at all bogged down with all the emotional baggage of being a veteran of the windmill business.  He was in it to produce the highest rate of return on the capital he had tied up in the assets of the business.  This absolute scale allowed him to see that the fix for Dempster would come by not reinvesting back into windmills.  He immediately stopped the company from putting more capital in and started taking the capital out.

With profits and proceeds raised from converting inventory and other assets to cash, Buffett started buying stocks he liked.  In essence, he was converting capital that was previously utilized in a bad (low-return) business, windmills, to capital that could be utilized in a good (high-return) business, securities.

Bottle, Buffett, and Munger maximized the value of Dempster’s assets.  Buffett took the further step of not reinvesting cash in a low-return business, but instead investing in high-return stocks.  In the end, on its investment of $28/share, BPL realized a net gain of $45 per share.  This is a gain of a bit more than 160% on what was a very large position for BPL—one-fifth of the portfolio.  Had the company been shut down by the bank, or simply burned through its assets, the return after paying $28/share could have been nothing or even negative.

Miller nicely summarizes the lessons of Buffett’s asset conversion play:

Buffett teaches investors to think of stocks as a conduit through which they can own their share of the assets that make up a business.  The value of that business will be determined by one of two methods: (1) what the assets are worth if sold, or (2) the level of profits in relation to the value of assets required in producing them.  This is true for each and every business and they are interrelated…

Operationally, a business can be improved in only three ways: (1) increase the level of sales; (2) reduce costs as a percent of sales; (3) reduce assets as a percentage of sales.  The other factors, (4) increase leverage or (5) lower the tax rate, are the financial drivers of business value.  These are the only ways a business can make itself more valuable.

Buffett “pulled all the levers” at Dempster…



For most of the cigar butts that Buffett bought for BPL, he used Graham’s net-net method of buying at a discount to liquidation value, conservatively estimated.  However, you can find deep value stocks—cigar butts—on the basis of other low “price-to-a-fundamental” ratio’s, such as low P/E or low EV/EBITDA.  Even Buffett, when he was managing BPL, used a low P/E in some cases to identify cigar butts.  (See an example below: Western Insurance Securities.)

Tobias Carlisle and Wes Gray tested various measures of cheapness from 1964 to 2011.  Quantitative Value (Wiley, 2012)—an excellent book—summarizes their results.  James P. O’Shaugnessy has conducted one of the broadest arrays of statistical backtests.  See his results in What Works on Wall Street (McGraw-Hill, 4th edition, 2012), a terrific book.

(Illustration by Maxim Popov)

  • Carlisle and Gray found that low EV/EBIT was the best-performing measure of cheapness from 1964 to 2011. It even outperformed composite measures.
  • O’Shaugnessy learned that low EV/EBITDA was the best-performing individual measure of cheapness from 1964 to 2009.
  • But O’Shaugnessy also discovered that a composite measure—combining low P/B, P/E, P/S, P/CF, and EV/EBITDA—outperformed low EV/EBITDA.

Assuming relatively similar levels of performance, a composite measure is arguably better because it tends to be more consistent over time.  There are periods when a given individual metric might not work well.  The composite measure will tend to smooth over such periods.  Besides, O’Shaugnessy found that a composite measure led to the best performance from 1964 to 2009.

Carlisle and Gray, as well as O’Shaugnessy, didn’t include Graham’s net-net method in their reported results.  Carlisle wrote another book, Deep Value (Wiley, 2014)—which is fascinating—in which he summarizes several tests of net nets:

  • Henry Oppenheimer found that net nets returned 29.4% per year versus 11.5% per year for the market from 1970 to 1983.
  • Carlisle—with Jeffrey Oxman and Sunil Mohanty—tested net nets from 1983 to 2008. They discovered that the annual returns for net nets averaged 35.3% versus 12.9% for the market and 18.4% for a Small Firm Index.
  • A study of the Japanese market from 1975 to 1988 uncovered that net nets outperformed the market by about 13% per year.
  • An examination of the London Stock Exchange from 1981 to 2005 established that net nets outperformed the market by 19.7% per year.
  • Finally, James Montier analyzed all developed markets globally from 1985 to 2007. He learned that net nets averaged 35% per year versus 17% for the developed markets on the whole.

Given these outstanding returns, why didn’t Carlisle and Gray, as well as O’Shaugnessy, consider net nets?  Primarily because many net nets are especially tiny microcap stocks.  For example, in his study, Montier found that the median market capitalization for net nets was $21 million.  Even the majority of professionally managed microcap funds do not consider stocks this tiny.

  • Recall that Dempster had a market cap of $1.6 million, or about $13.3 million in today’s dollars.
  • Unlike the majority of microcap funds, the Boole Microcap Fund does consider microcap stocks in the $10 to $25 million market cap range.

In 1999, Buffett commented that he could get 50% per year by investing in microcap cigar butts.  He was later asked about this comment in 2005, and he replied:

Yes, I would still say the same thing today.  In fact, we are still earning those types of returns on some of our smaller investments.  The best decade was the 1950s;  I was earning 50% plus returns with small amounts of capital.  I would do the same thing today with smaller amounts.  It would perhaps even be easier to make that much money in today’s environment because information is easier to access.  You have to turn over a lot of rocks to find those little anomalies.  You have to find the companies that are off the map—way off the map.  You may find local companies that have nothing wrong with them at all.  A company that I found, Western Insurance Securities, was trading for $3/share when it was earning $20/share!!  I tried to buy up as much of it as possible.  No one will tell you about these businesses.  You have to find them.

Although the majority of microcap cigar butts Buffett invested in were cheap relative to liquidation value—cheap on the basis of net tangible assets—Buffett clearly found some cigar butts on the basis of a low P/E.  Western Insurance Securities is a good example.



Warren Buffett commented on high quality companies versus statistically cheap companies in his October 1967 letter to partners:

The evaluation of securities and businesses for investment purposes has always involved a mixture of qualitative and quantitative factors.  At the one extreme, the analyst exclusively oriented to qualitative factors would say, “Buy the right company (with the right prospects, inherent industry conditions, management, etc.) and the price will take care of itself.”  On the other hand, the quantitative spokesman would say, “Buy at the right price and the company (and stock) will take care of itself.”  As is so often the pleasant result in the securities world, money can be made with either approach.  And, of course, any analyst combines the two to some extent—his classification in either school would depend on the relative weight he assigns to the various factors and not to his consideration of one group of factors to the exclusion of the other group.

Interestingly enough, although I consider myself to be primarily in the quantitative school… the really sensational ideas I have had over the years have been heavily weighted toward the qualitative side where I have had a “high-probability insight”.  This is what causes the cash register to really sing.  However, it is an infrequent occurrence, as insights usually are, and, of course, no insight is required on the quantitative side—the figures should hit you over the head with a baseball bat.  So the really big money tends to be made by investors who are right on qualitative decisions but, at least in my opinion, the more sure money tends to be made on the obvious quantitative decisions.

Buffett and Munger acquired See’s Candies for Berkshire Hathaway in 1972.  See’s Candies is the quintessential high quality company because of its sustainably high ROIC (return on invested capital) of over 100%.

Truly high quality companies—like See’s—are very rare and difficult to find.  Cigar butts—including net nets—are much easier to find by comparison.

Furthermore, it’s important to understand that Buffett got around 50% annual returns from cigar butts because he took a focused approach, like BPL’s 20% position in Dempster.

The vast majority of investors, if using a cigar butt approach like net nets, should implement a group—or statistical—approach, and regularly buy and hold a basket of cigar butts (at least 20-30).  This typically won’t produce 50% annual returns.  But net nets, as a group, clearly have produced very high returns, often 30%+ annually.  To do this today, you’d have to look globally.

As an alternative to net nets, you could implement a group approach using one of O’Shaugnessy’s composite measures—such as low P/B, P/E, P/S, P/CF, EV/EBITDA.  Applying this to micro caps can produce 15-20% annual returns.  Generally not as good as net nets, but much easier to apply consistently.

You may think that you can find some high quality companies.  But that’s not enough.  You have to find a high quality company that can maintain its competitive position and high ROIC.  And it has to be available at a reasonable price.

Most high quality companies are trading at very high prices, to the extent that you can’t do better than the market by investing in them.  In fact, often the prices are so high that you’ll probably do worse than the market.

Consider this comment by Charlie Munger:

The model I like to sort of simplify the notion of what goes o­n in a market for common stocks is the pari-mutuel system at the racetrack.  If you stop to think about it, a pari-mutuel system is a market.  Everybody goes there and bets and the odds change based o­n what’s bet.  That’s what happens in the stock market.

Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so o­n and so on.  But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2.  Then it’s not clear which is statistically the best bet using the mathematics of Fermat and Pascal.  The prices have changed in such a way that it’s very hard to beat the system.

(Illustration by Nadoelopisat)

A horse with a great record (etc.) is much more likely to win than a horse with a terrible record.  But—whether betting on horses or betting on stocks—you don’t get paid for identifying winners.  You get paid for identifying mispricings.

The statistical evidence is overwhelming that if you systematically buy stocks at low multiples—P/B, P/E, P/S, P/CF, EV/EBITDA, etc.—you’ll almost certainly do better than the market over the long haul.

A deep value—or cigar butt—approach has always worked, given enough time.  Betting on “the losers” has always worked eventually, whereas betting on “the winners” hardly ever works.

Classic academic studies showing “the losers” doing far better than “the winners” over subsequent 3- to 5-year periods:

That’s not to say deep value investing is easy.  When you put together a basket of statistically cheap companies, you’re buying stocks that are widely hated or neglected.  You have to endure loneliness and looking foolish.  Some people can do it, but it’s important to know yourself before using a deep value strategy.

In general, we extrapolate the poor performance of cheap stocks and the good performance of expensive stocks too far into the future.  This is the mistake of ignoring mean reversion.

When you find a group of companies that have been doing poorly for at least several years, those conditions typically do not persist.  Instead, there tends to be mean reversion, or a return to “more normal” levels of revenues, earnings, or cash flows.

Similarly for a group of companies that have been doing exceedingly well.  Those conditions also do not continue in general.  There tends to be mean reversion, but in this case the mean—the average or “normal” conditions—is below recent activity levels.

Here’s Ben Graham explaining mean reversion:

It is natural to assume that industries which have fared worse than the average are “unfavorably situated” and therefore to be avoided.  The converse would be assumed, of course, for those with superior records.  But this conclusion may often prove quite erroneous.  Abnormally good or abnormally bad conditions do not last forever.  This is true of general business but of particular industries as well.  Corrective forces are usually set in motion which tend to restore profits where they have disappeared or to reduce them where they are excessive in relation to capital.

With his taste for literature, Graham put the following quote from Horace’s Ars Poetica at the beginning of Security Analysis—the bible for value investors:

Many shall be restored that now are fallen and many shall fall than now are in honor.

Tobias Carlisle, while discussing mean reversion in Deep Value, smartly (and humorously) included this image of Albrecht Durer’s Wheel of Fortune:

(Albrecht Durer’s Wheel of Fortune from Sebastien Brant’s Ship of Fools (1494) via Wikimedia Commons)



We’ve already seen that there are two basic ways to do cigar-butt investing: focused vs. statistical (group).

Ben Graham usually preferred the statistical—or group—approach.  Near the beginning of the Great Depression, Graham’s managed accounts lost more than 80 percent.  Furthermore, the economy and the stock market took a long time to recover.  As a result, Graham had a strong tendency towards conservatism in investing.  This is likely part of why he preferred the statistical approach to net nets.  By buying a basket of net nets (at least 20-30), the investor is virtually certain to get the statistical results of the group over time, which are broadly excellent.

Graham also was a polymath of sorts.  He had wide-ranging intellectual interests.  Because he knew net nets as a group would do quite well over the long term, he wasn’t inclined to spend much time analyzing individual net nets.  Instead, he spent time on his other interests.

Warren Buffett was Graham’s best student.  Buffett was the only student ever to be awarded an A+ in Graham’s class at Columbia University.  Unlike Graham, Buffett has always had an extraordinary focus on business and investing.  After spending many years learning everything about virtually every public company, Buffett took a focused approach to net nets.  He found the ones that were the cheapest and that seemed the surest.

Buffett has asserted that returns can be improved—and risk lowered—if you focus your investments only on those companies that are within your circle of competence—those companies that you can truly understand.  Buffett also maintains, however, that the vast majority of investors should simply invest in index funds: http://boolefund.com/warren-buffett-jack-bogle/

Regarding individual net nets, Graham admitted a danger:

Corporate gold dollars are now available in quantity at 50 cents and less—but they do have strings attached.  Although they belong to the stockholder, he doesn’t control them.  He may have to sit back and watch them dwindle and disappear as operating losses take their toll.  For that reason the public refuses to accept even the cash holdings of corporations at their face value.

Graham explained that net nets are cheap because they “almost always have an unsatisfactory trend in earnings.”  Graham:

If the profits had been increasing steadily it is obvious that the shares would not sell at so low a price.  The objection to buying these issues lies in the probability, or at least the possibility, that earnings will decline or losses continue, and that the resources will be dissipated and the intrinsic value ultimately become less than the price paid.

(Image by Preecha Israphiwat)

Value investor Seth Klarman warns:

As long as working capital is not overstated and operations are not rapidly consuming cash, a company could liquidate its assets, extinguish all liabilities, and still distribute proceeds in excess of the market price to investors.  Ongoing business losses can, however, quickly erode net-net working capital.  Investors must therefore always consider the state of a company’s current operations before buying.

Even Buffett—nearly two decades after closing BPL—wrote the following in his 1989 letter to Berkshire shareholders:

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.  For example, if you buy a business for $8 million that can be sold or liquidated for $10 million and promptly take either course, you can realize a high return.  But the investment will disappoint if the business is sold for $10 million in ten years and in the interim has annually earned and distributed only a few percent on cost…

Based on these objections, you might think that Buffett’s focused approach is better than the statistical (group) method.  That way, the investor can figure out which net nets are more likely to recover rather than burn through their assets and leave the investor with a low or negative return.

However, Graham’s response was that the statistical or group approach to net nets is highly profitable over time.  There is a wide range of potential outcomes for net nets, and many of those scenarios are good for the investor.  Therefore, while there are always some individual net nets that don’t work out, a group or basket of net nets is nearly certain to work well eventually.

Indeed, Graham’s application of a statistical net-net approach produced 20% annual returns over many decades.  Most backtests of net nets have tended to show annual returns of close to 30%.  In practice, while around 5 percent of net nets may suffer a terminal decline in stock price, a statistical group of net nets has done far better than the market and has experienced fewer down years.  Moreover, as Carlisle notes in Deep Value, very few net nets are actually liquidated or merged.  In the vast majority of cases, there is a change by management, a change from the outside, or both, in order to restore earnings to a level more in line with net asset value.  Mean reversion.



We noted earlier that it’s far more difficult to find a company like See’s Candies, at a reasonable price, than it is to find statistically cheap stocks.  Moreover, if you buy a basket of statistically cheap stocks, you don’t have to possess an ability to analyze individual businesses in great depth.

That said, in order to use a deep value strategy, you do have to be able to handle the psychological discomfort of being lonely and looking foolish.

(Illustration by Sangoiri)

John Mihaljevic, author of The Manual of Ideas (Wiley, 2013), writes:

Comfort can be expensive in investing.  Put differently, acceptance of discomfort can be rewarding, as equities that cause their owners discomfort frequently trade at exceptionally low valuations….

…Misery loves company, so it makes sense that rewards may await those willing to be miserable in solitude…

Mihaljevic explains:

If we owned nothing but a portfolio of Ben Graham-style bargain equities, we may become quite uncomfortable at times, especially if the market value of the portfolio declined precipitously.  We might look at the portfolio and conclude that every investment could be worth zero.  After all, we could have a mediocre business run by mediocre management, with assets that could be squandered.  Investing in deep value equities therefore requires faith in the law of large numbers—that historical experience of market-beating returns in deep value stocks and the fact that we own a diversified portfolio will combine to yield a satisfactory result over time.  This conceptually sound view becomes seriously challenged in times of distress…

Playing into the psychological discomfort of Graham-style equities is the tendency of such investments to exhibit strong asset value but inferior earnings or cash flows.  In a stressed situation, investors may doubt their investment theses to such an extent that they disregard the objectively appraised asset values.  After all—the reasoning of a scared investor might go—what is an asset really worth if it produces no cash flow?

Deep value investors often find some of the best investments in cyclical areas.  A company at a cyclical low may have multi-bagger potential—the prospect of returning 300-500% (or more) to the investor.

A good current example is Ensco plc (NYSE: ESV), an offshore oil driller.  Having just completed its acquisition of Atwood Oceanics (NYSE: ATW), Ensco is now a leading offshore driller with a high-specification, globally diverse fleet.  The company also has one of the lowest cost structures, and relatively low debt levels (with the majority of debt due in 2024 or later).  Ensco—like Atwood—has a long history of operational excellence and safety.  Ensco has been rated #1 for seven consecutive years in the leading independent customer satisfaction survey.

  • At $5.60 recently, Ensco is trading near 20% of tangible book value.  (It purchased Atwood at about the same discount to tangible book.)  If oil prices revert to a mean of $60-70 per barrel (or more), Ensco will probably be worth at least tangible book value.
  • That implies a 400% return (or more)—over the next 3 to 5 years—for an investor who owns shares today.

However, it’s possible oil will never return to $60-70.  It’s possible the seemingly cyclical decline for offshore oil drillers is actually more permanent in nature.  Mihaljevic observes:

The question of whether a company has entered permanent decline is anything but easy to answer, as virtually all companies appear to be in permanent decline when they hit a rock-bottom market quotation.  Even if a business has been cyclical in the past, analysts generally adopt a “this time is different” attitude.  As a pessimistic stock price inevitably influences the appraisal objectivity of most investors, it becomes exceedingly difficult to form a view strongly opposed to the prevailing consensus.

Consider the following industries that have been pronounced permanently impaired in the past, only to rebound strongly in subsequent years:  Following the financial crisis of 2008-2009, many analysts argued that the banking industry would be permanently negatively affected, as higher capital requirements and regulatory oversight would compress returns on equity.  The credit rating agencies were seen as impaired because the regulators would surely alter the business model of the industry for the worse following the failings of the rating agencies during the subprime mortgage bubble.  The homebuilding industry would fail to rebound as strongly as in the past, as overcapacity became chronic and home prices remained tethered to building costs.  The refining industry would suffer permanently lower margins, as those businesses were capital-intensive and driven by volatile commodity prices.

Are offshore oil drillers in a cyclical or a secular decline?  It’s likely that oil will return to $60-70, at least in the next 5-10 years.  But no one knows for sure.

Ongoing improvements in technology allow oil producers to get more oil—more cheaply—out of existing fields.  Also, growth in transport demand for oil will slow significantly at some point, due to ongoing improvements in fuel efficiency (and possibly also due to a more widespread adoption of electric vehicles).  See: https://www.spe.org/en/jpt/jpt-article-detail/?art=3286

Transport demand is responsible for over 50% of daily oil consumption, and it’s inelastic—typically people have to get where they’re going, so they’re not very sensitive to fuel price increases.

But even if oil never returns to $60+, oil will be needed for many decades.  At least some offshore drilling will still be needed, and Ensco will be a survivor.

Full Disclosure:

  • The Boole Fund had an investment in Atwood Oceanics. With the acquisition of Atwood by Ensco now completed, the Boole Fund currently owns shares in Ensco plc.
  • The Boole Fund holds positions for 3 to 5 years. The fund doesn’t sell an investment that is still cheap, even if the stock in question is no longer a micro cap.



Buffett has made it clear, including in his 2014 letter to shareholders, that the best returns of his career came from investing in microcap cigar butts.  Most of these were mediocre businesses (or worse).  But they were ridiculously cheap.  And, in some cases like Dempster, Buffett was able to bring about needed improvements when required.

When Buffett wrote about buying wonderful businesses in his 1989 letter, that’s chiefly because investable assets at Berkshire Hathaway had grown far too large for microcap cigar butts.

Even in recent years, Buffett invested part of his personal portfolio in a group of cigar butts he found in South Korea.  So he’s never changed his view that an investor can get the highest returns from microcap cigar butts, either by using a statistical group approach or by using a more focused method.



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

The Art of Value Investing

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 17, 2017

The Art of Value Investing (Wiley, 2013) is an excellent book by John Heins and Whitney Tilson.  Heins and Tilson have been running the monthly newsletter, Value Investor Insight, for a decade now.  Over that time, they have interviewed many of the best value investors in the world.  The Art of Value Investing is a collection of quotations carefully culled from those interviews.

I’ve selected and discussed the best quotes from the following areas:

  • Margin of Safety
  • Humility, Flexibility, and Patience
  • Courage
  • Cigar-Butt’s
  • Opportunities in Micro Caps
  • Predictable Human Irrationality
  • Long-Term Time Horizon
  • Screening and Quantitative Models



(Ben Graham, by Equim43)

Ben Graham, the father of value investing, stressed having a margin of safety by buying well below the probable intrinsic value of a stock.  This is essential because the future is uncertain.  Also, mistakes are inevitable.  (Good value investors tend to be right 60 percent of the time and wrong 40 percent of the time.)  Jean-Marie Eveillard:

Whenever Ben Graham was asked what he thought would happen to the economy or to company X’s or Y’s profits, he always used to deadpan, ‘The future is uncertain.’  That’s precisely why there’s a need for a margin of safety in investing, which is more relevant today than ever.

Value investing legend Seth Klarman:

People should be highly skeptical of anyone’s, including their own, ability to predict the future, and instead pursue strategies that can survive whatever may occur.  

The central idea in value investing is to figure out what a business is worth (approximately), and then pay a lot less to acquire part ownership of that business via stock.  Howard Marks:

If I had to identify a single key to consistently successful investing, I’d say it’s ‘cheapness.’  Buying at low prices relative to intrinsic value (rigorously and conservatively derived) holds the key to earning dependably high returns, limiting risk and minimizing losses.  It’s not the only thing that matters – obviously – but it’s something for which there is no substitute.



(Image by Wilma64)

Successful value investing, to a large extent, is about having the right mindset.  Matthew McLennan identifies humility, flexibility, and patience as key traits:

Starting with the first recorded and reliable history that we can find – a history of the Peloponnesian war by a Greek author named Thucydides – and following through a broad array of key historical global crises, you see recurring aspects of human nature that have gotten people into trouble:  hubris, dogma, and haste.  The keys to our investing approach are the symmetrical opposite of that:  humility, flexibility, and patience.

On the humility side, one of the things that Jean-Marie Eveillard firmly ingrained in the culture here is that the future is uncertain.  That results in investing with not only a price margin of safety, but in companies with conservative balance sheets and prudent and proven management teams….

In terms of flexibility, we’ve been willing to be out of the biggest sectors of the market…

The third thing in terms of temperament we think we value more than most other investors is patience.  We have a five-year average holding period….We like to plant seeds and then watch the trees grow, and our portfolio is often kind of a portrait of inactivity.

It’s hard to overstate the importance of humility in investing.  Many of the biggest investing mistakes have occurred when intelligent investors who have succeeded in the past have developed high conviction in an idea that happens to be wrong.  Kyle Bass explains this point clearly:

You obviously need to develop strong opinions and to have the conviction to stick with them when you believe you’re right, even when everybody else may think you’re an idiot.  But where I’ve seen ego get in the way is by not always being open to questions and to input that could change your mind.  If you can’t ever admit you’re wrong, you’re more likely to hang on to your losers and sell your winners, which is not a recipe for success.

It often happens in investing that ideas that seem obvious or even irrefutable turn out to be wrong.  The very best investors – such as Warren Buffett, Charlie Munger, Seth Klarman, Howard Marks, Jeremy Grantham, George Soros, and Ray Dalio – have developed enough humility to admit when they’re wrong, even when all the evidence seems to indicate that they’re right.

Here are two great examples of how seemingly irrefutable ideas can turn out to be wrong:

  • shorting the U.S. stock market;
  • shorting the Japanese yen.

(Illustration by Eti Swinford)

Professor Russell Napier is the author of Anatomy of the Bear (Harriman House, 4th edition, 2016).  Napier was a top-rated analyst for many years and has been studying and writing about global macro strategy for institutional investors since 1995.

Napier has maintained (at least since 2012) that the U.S. stock market is significantly overvalued based on the Q-ratio and also the CAPE (cyclically adjusted P/E).  Moreover, Napier points out that every major U.S. secular bear market bottom in the last 100 years or so has seen the CAPE approach single digits.  The catalyst for the major drop has always been either inflation or deflation, states Napier.

Napier continues to argue (mid-2017) that U.S. stocks are overvalued and that deflation will cause the U.S. stock market to drop significantly, similar to previous secular bear markets.

Many highly intelligent value investors – at least since 2012 or 2013 – have maintained high cash balances and/or short positions because they essentially agree with Napier’s argument.

However, Napier is probably wrong.  Here’s why:  U.S. interest rates are quite low, while profit margins are high compared to history.  And these conditions are likely to continue.

Low interest rates cause stocks to be much higher than otherwise.  At the extreme, as Buffett has noted, if rates stayed low enough for long enough, the stock market could have a P/E of 50 or more.

Also, U.S. profit margins are considerably higher than they have been in the last 100 years.  This situation will probably persist because software and related technology keep becoming more important in the U.S. and global economy.  The five largest U.S. companies are Google, Apple, Microsoft, Facebook, and Amazon, all technology companies.

One of the most astute value investors who tracks fair value of the S&P 500 Index is Jeremy Grantham of GMO.  Grantham used to think, back in 2012-2013, that the U.S. secular bear market was not over.  Then he partially revised his view and predicted that the S&P 500 Index was likely to exceed 2250-2300.  This level would have made the S&P 500’s value two standard deviations above the historical mean, indicating that it was back in bubble territory according to GMO’s definition.

Recently, in the GMO Quarterly Letter (Q2 2017), Grantham has revised his view again.  See: https://www.gmo.com/docs/default-source/public-commentary/gmo-quarterly-letter.pdf

Grantham now says that without a crash in profit margins, or without a dramatic sustained rise in inflation, there’s no reason to expect a market crash.  Furthermore, Grantham believes it’s unlikely for either of those things to happen, especially in the near term.  The fact that Grantham has been able to take in new information and noticeably revise his strongest convictions illustrates why he is a top value investor.

(Image by joshandandreaphotography)

As John Maynard Keynes is (probably incorrectly) reported to have said:

When the information changes, I alter my conclusions.  What do you do, sir?

There are some very smart value investors – such as Frank Martin and John Hussman – who still basically agree with Russell Napier’s views.  They may eventually be right.

But no one has ever been able to predict the stock market.  Ben Graham – with a 200 IQ – was as smart or smarter than any value investor who’s ever lived.  And here’s what Graham said near the end of his career:

If I have noticed anything over these sixty years on Wall Street, it is that people do not succeed in forecasting what’s going to happen to the stock market.

In 1963, Graham gave a lecture, “Securities in an Insecure World.”  Link: https://www8.gsb.columbia.edu/rtfiles/Heilbrunn/Schloss%20Archives%20for%20Value%20Investing/Articles%20by%20Benjamin%20Graham/DOC005.PDF

In the lecture, Graham admits that the Graham P/E – based on ten-year average earnings of the Dow components – was much too conservative.  Graham:

The action of the stock market since then would appear to demonstrate that these methods of valuations are ultra-conservative and much too low, although they did work out extremely well through the stock market fluctuations from 1871 to about 1954, which is an exceptionally long period of time for a test.  Unfortunately in this kind of work, where you are trying to determine relationships based upon past behavior, the almost invariable experience is that by the time you have had a long enough period to give you sufficient confidence in your form of measurement just then new conditions supersede and the measurement is no longer dependable for the future.

Jeremy Grantham, in the GMO Q2 2017 Letter mentioned earlier, actually quotes these two sentences (among others).  But I first discovered Graham’s 1963 lecture several years ago.

Graham goes on to note that, in the 1962 edition of Security Analysis, Graham and Dodd addressed this issue.  Because of the U.S. government’s more aggressive policy with respect to preventing a depression, Graham and Dodd concluded that the U.S. stock market should have a fair value 50 percent higher.

Similar logic can be applied to the S&P 500 Index today – which exceeds 2500.  If interest rates remain relatively low for many years – in part based on a more aggressive Fed policy (designed to avoid deflation and create inflation) – and if profit margins are at a permanently higher level, then fair value for the S&P 500 has arguably increased significantly.  Whereas the CAPE (cyclically adjusted P/E) – the modern form of the original Graham P/E – put fair value of the S&P 500 Index at around 1100-1200 back in 2011-2013, that’s way too low if interest rates remain low and if profit margins are permanently higher.

In brief, previous methods – very well-established based on nearly a century – put fair value for the S&P 500 Index around 1100-1200.  But actual fair value could easily be closer to 1800 or more.  And fair value grows each year as the economy grows.  The U.S. economy is still growing steadily.  So 2500 for the S&P 500 may be quite far from “bubble” territory.  In fact, the market may be fairly valued – if not now, then in 5-10 years.

Furthermore, always bear in mind that no one can predict the stock market.  This has not only been observed by Graham.  But it’s also been pointed out by Peter Lynch, Seth Klarman, Henry Singleton, and Warren Buffett.  Peter Lynch is one of the best investors.  Klarman is even better.  Buffett is arguably the best.  And Singleton was even smarter than Buffett.

In a word, history strongly demonstrates that no one has ever been able to predict the stock market with any sort of reliability.

(Illustration by Maxim Popov)

Peter Lynch:

Nobody can predict interest rates, the future direction of the economy, or the stock market.  Dismiss all such forecasts and concentrate on what’s actually happening to the companies in which you’ve invested.

Seth Klarman:

In reality, no one knows what the market will do; trying to predict it is a waste of time, and investing based upon that prediction is a speculative undertaking.

Now, every year there are “pundits” who make predictions about the stock market.  Therefore, as a matter of pure chance, there will always be people in any given year who are “right.”  But there’s zero evidence that any of those who were “right” at some point in the past have been correct with any sort of reliability.

Howard Marks has asked: of those who correctly predicted the bear market in 2008, how many of them predicted the recovery in 2009 and since then?  The answer: very few.  Marks points out that most of those who got 2008 right were already disposed to bearish views in general.  So when a bear market finally came, they were “right,” but the vast majority missed the recovery starting in 2009.

There are always naysayers making bearish predictions.  But anyone who owned an S&P 500 index fund from 2007 to present (Sept. 2017) would have done dramatically better than most of those who listened to naysayers.  Buffett:

Ever-present naysayers may prosper by marketing their gloomy forecasts.  But heaven help them if they act on the nonsense they peddle.

Buffett himself made a 10-year wager against a group of talented hedge fund (and fund of hedge fund) managers.  With only a few months left until the conclusion of the bet, Buffett’s investment in a Vanguard S&P 500 index fund has roughly quadrupled the performance of the hedge funds: http://boolefund.com/warren-buffett-jack-bogle/

Some very able investors have stayed largely in cash since 2011-2012.  The S&P 500 Index has more than doubled since then.  Moreover, many have tried to short the U.S. stock market since 2011-2012.  Some are down 50 percent, while the S&P 500 Index has more than doubled.  The net result of that combination is to be at only 20-25% of the S&P 500’s current value.

Henry Singleton, a business genius (100 points from being a chess grandmaster) who was easily one of the best capital allocators in American business history, never relied on financial forecasts – despite operating in a secular bear market from 1968 to 1982:

I don’t believe all this nonsense about market timing. Just buy very good value and when the market is ready that value will be recognized.

Warren Buffett puts it best:

  • Charlie and I never have an opinion on the market because it wouldn’t be any good and it might interfere with the opinions we have that are good.
  • We will continue to ignore political and economic forecasts, which are an expensive distraction for many investors and businessmen.
  • Market forecasters will fill your ear but never fill your wallet.
  • Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.
  • Stop trying to predict the direction of the stock market, the economy, interest rates, or elections.
  • [On economic forecasts:] Why spend time talking about something you don’t know anything about?  People do it all the time, but why do it?
  • I don’t invest a dime based on macro forecasts.

Another good example of a “can’t lose” investment idea that has turned out not to be right:  shorting the Japanese yen.  Many macro experts have been quite certain that the Japanese yen versus the U.S. dollar would eventually exceed 200.  They thought this would have happened years ago.  Some called it the “trade of the decade.”  But the yen versus U.S. dollar is still around 110.  A simple S&P 500 index fund appears to be doing far better than the “trade of the decade.”

(Illustration by Shalom3)

Some have tried to short Japanese government bonds (JGB’s), rather than shorting the yen currency.  But that hasn’t worked for decades.  In fact, shorting JGB’s has become known as the widowmaker trade.

Seth Klarman on humility:

In investing, certainty can be a serious problem, because it causes one not to reassess flawed conclusions.  Nobody can know all the facts.  Instead, one must rely on shreds of evidence, kernels of truth, and what one suspects to be true but cannot prove.

Klarman on the vital importance of doubt:

It is much harder psychologically to be unsure than to be sure;  certainty builds confidence, and confidence reinforces certainty.  Yet being overly certain in an uncertain, protean, and ultimately unknowable world is hazardous for investors.  To be sure, uncertainty breeds doubt, which can be paralyzing.  But uncertainty also motivates diligence, as one pursues the unattainable goal of eliminating all doubt.  Unlike premature or false certainty, which induces flawed analysis and failed judgments, a healthy uncertainty drives the quest for justifiable conviction.

My own painful experiences:  shorting the U.S. stock market and shorting the Japanese yen.  In each case, I believed that the evidence was overwhelming.  By far the biggest mistake I’ve ever made was shorting the U.S. stock market in 2011-2013.  At the time, I agreed with Russell Napier’s arguments.  I was completely wrong.

After that, I shorted the Japanese yen because I was convinced the argument was virtually irrefutable.  Wrong.  Perhaps the yen will collapse some day, but if it’s 10-20 years in the future – or even later – then an index fund or a quantitative value fund would be a far better and safer investment.

Spencer Davidson:

Over a long career you learn a certain humility and are quicker to attribute success to luck rather than your own brilliance.  I think that makes you a better investor, because you’re less apt to make the big mistake and you’re probably quicker to capitalize on good fortune when it shines upon you.

Jeffrey Bronchick:

It’s important not to get carried away with yourself when times are good, and to be able to admit your mistakes and move on when they’re not so good.  If you are intellectually honest – and not afraid to be visibly and sometimes painfully judged by your peers – investing is not work, it’s fun.

Patiently waiting for pessimism or temporary bad news to create low stock prices (some place), and then buying stocks well below probable intrinsic value, does not require genius in general.  But it does require the humility to focus only on areas where you can do well.  As Warren Buffett has remarked:

What counts for most people in investing is not how much they know, but rather how realistically they define what they don’t know.



(Courage concept by Travelling-light)

Humility is essential for success in investing.  But you also need the courage to think and act independently.  You have to be able to develop an investment thesis based on the facts and good reasoning without worrying if many others disagree.  Most of the best value investments are contrarian, meaning that your view differs from the consensus.  Ben Graham:

In the world of securities, courage becomes the supreme virtue after adequate knowledge and a tested judgment are at hand.

Graham again:

You’re neither right nor wrong because the crowd disagrees with you.  You’re right because your data and reasoning are right.

Or as Carlo Cannell says:

Going against the grain is clearly not for everyone – and it doesn’t tend to help you in your social life – but to make the really large money in investing, you have to have the guts to make the bets that everyone else is afraid to make.

Joel Greenblatt identifies two chief reasons why contrarian value investing is hard:

Value investing strategies have worked for years and everyone’s known about them.  They continue to work because it’s hard for people to do, for two main reasons.  First, the companies that show up on the screens can be scary and not doing so well, so people find them difficult to buy.  Second, there can be one-, two- or three-year periods when a strategy like this doesn’t work.  Most people aren’t capable of sticking it out through that.

Contrarian value investing requires buying what is out-of-favor, neglected, or hated.  It also requires the ability to endure multi-year periods of trailing the market, which most investors just can’t do.  Furthermore, while you’re buying what everyone hates and while you’re trailing the market, you also have to put up with people calling you an idiot.  In a word, you must have the ability to suffer.  Eveillard:

If you are a value investor, you’re a long-term investor.  If you are a long-term investor, you’re not trying to keep up with a benchmark on a short-term basis.  To do that, you accept in advance that every now and then you will lag behind, which is another way of saying you will suffer.  That’s very hard to accept in advance because, the truth is, human nature shrinks from pain.  That’s why not so many people invest this way.  But if you believe as strongly as I do that value investing not only makes sense, but that it works, there’s really no credible alternative.



(Photo by Leung Cho Pan)

Warren Buffett has remarked that buying baskets of statistically cheap cigar-butt’s – 50-cent dollars – is a more dependable way to generate good returns than buying high-quality businesses.  Rich Pzena perhaps expressed it best:

When I talk about the companies I invest in, you’ll be able to rattle off hundreds of bad things about them – but that’s why they’re cheap!  The most common comment I get is ‘Don’t you read the paper?’  Because if you read the paper, there’s no way you’d buy these stocks.

They’re priced where they are for good reason, but I invest when I believe the conditions that are causing them to be priced that way are probably not permanent.  By nature, you can’t be short-term oriented with this investment philosophy.  If you’re going to worry about short-term volatility, you’re just not going to be able to buy the cheapest stocks.  With the cheapest stocks, the outlooks are uncertain.

Many investors incorrectly assume that high growth in the past will continue into the future, or that a high-quality company is automatically a good investment.  Behavioral finance expert and value investor James Montier:

There’s a great chapter [in Dan Ariely’s Predictably Irrational] about the ways in which we tend to misjudge price and use it as an indicator of something or other.  That links back to my whole thesis that the most common error we as investors make is overpaying for the hope of growth.  Dan did an experiment involving wine, in which he told people, ‘Here’s a $10 bottle of wine and here’s a $90 bottle of wine.  Please rate them and tell me which tastes better.’  Not surprisingly, nearly everyone thought the $90 wine tasted much better than the $10 wine.  The only snag was that the $90 wine and the $10 wine were actually the same $10 wine.



(Illustration by Mopic)

Micro-cap stocks are the most inefficiently priced.  That’s because, for most professional investors, assets under management are too large.  These investors cannot even consider micro caps.  The Boole Microcap Fund is designed to take advantage of this inefficiency: http://boolefund.com/best-performers-microcap-stocks/

James Vanasek on the opportunity in micro caps:

We’ll invest in companies with up to $1 billion or so in market cap, but have been most successful in ideas that start out in the $50 million to $300 million range.  Fewer people are looking at them and the industries the companies are in can be quite stable.  Given that, if you find a company doing well, it’s more likely it can sustain that advantage over time.

Because very few professional investors can even contemplate investing in micro caps, there’s far less competition.  Carlo Cannell:

My basic premise is that the efficient markets hypothesis breaks down when there is inconsistent, imperfect dissemination of information.  Therefore it makes sense to direct our attention to the 14,000 or so publicly traded companies in the U.S. for which there is little or no investment sponsorship by Wall Street, meaning three or fewer sell-side analysts who publish research…

You’d be amazed how little competition we have in this neglected universe.  It is just not in the best interest of the vast majority of the investing ecosphere to spend 10 minutes on the companies we spend our lives looking at.

Robert Robotti adds:

We focus on smaller-cap companies that are largely ignored by Wall Street and face some sort of distress, of their own making or due to an industry cycle.  These companies are more likely to be inefficiently priced and if you have conviction and a long-term view they can produce not 20 to 30 percent returns, but multiples of that.



Value investors recognize that the stock market is not always efficient, largely because humans are often less than fully rational.  As Seth Klarman explains:

Markets are inefficient because of human nature – innate, deep-rooted, permanent.  People don’t consciously choose to invest with emotion – they simply can’t help it.

Quantitative value investor James O’Shaugnessy:

Because of all the foibles of human nature that are well documented by behavioral research – people are always going to overshoot and undershoot when pricing securities.  A review of financial markets all the way back to the South Sea Company nearly 300 years ago proves this out.

Bryan Jacoboski:

The very reason price and value diverge in predictable and exploitable ways is because people are emotional beings.  That’s why the distinguishing attribute among successful investors is temperament rather than brainpower, experience, or classroom training.  They have the ability to be rational when others are not.

Overconfidence is extremely deep-rooted in human psychology.  When asked, the vast majority of us rate ourselves as above average across a wide variety of dimensions such as looks, smarts, driving skill, academic ability, future well-being, and even luck (!).

In a field such as investing, it’s vital to become aware of our natural overconfidence.  Charlie Munger likes this quote from Demosthenes:

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

But becoming aware of our overconfidence is usually not enough.  We also have to develop systems – such as checklists – that can automatically reduce both the frequency and the severity of mistakes.

(Image by Aleksey Vanin)

Charlie Munger reminds value investors not only to develop and use a checklist, but also to follow the advice of mathematician Carl Jacobi:

Invert, always invert.

In other words, instead of thinking about how to succeed, Munger advises value investors to figure out all the ways you can fail.  This is a powerful concept in a field like investing, where overconfidence frequently causes failure.  Munger:

It is occasionally possible for a tortoise, content to assimilate proven insights of his best predecessors, to outrun hares which seek originality or don’t wish to be left out of some crowd folly which ignores the best work of the past.  This happens as the tortoise stumbles on some particularly effective way to apply the best previous work, or simply avoids the standard calamities.  We try more to profit by always remembering the obvious than from grasping the esoteric.  It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.

When it comes to checklists, it’s helpful to have a list of cognitive biases.  Here’s my list: http://boolefund.com/cognitive-biases/

Munger’s list is more comprehensive: http://boolefund.com/the-psychology-of-misjudgment/

Recency bias is one of the most important biases to be aware of as an investor.  Jed Nussdorf:

It is very hard to avoid recency bias, when what just happened inordinately informs your expectation of what will happen next.  One of the best things I’ve read on that is The Icarus Syndrome, by Peter Beinart.  It’s not about investing, but describes American hubris in foreign policy, in many cases resulting from doing what seemed to work in the previous 10 years even if the setting was materially different or conditions had changed.  One big problem is that all the people who succeed in the recent past become the ones in charge going forward, and they think they have it all figured out based on what they did before.  It’s all quite natural, but can result in some really bad decisions if you don’t constantly challenge your core beliefs.

Availability bias is closely related to recency bias and vividness bias.  You’re at least 15-20 times more likely to be hit by lightning in the United States than to be bitten by shark.  But often people don’t realize this because shark attacks tend to be much more vivid in people’s minds.  Similarly, your odds of dying in a car accident are 1 in 5,000, while your odds of dying in a plane crash are 1 in 11 million.  Nonetheless, many people view flying as more dangerous.

John Dorfman on investors overreacting to recent news:

Investors overreact to the latest news, which has always been the case, but I think it’s especially true today with the Internet.  Information spreads so quickly that decisions get made without particularly deep knowledge about the companies involved.  People also overemphasize dramatic events, often without checking the facts.



(Illustration by Marek)

Because so many investors worry and think about the shorter term, value investors continue to gain a large advantage by focusing on the longer term (especially three to five years).  In a year or less, a given stock can do almost anything.  But over a five-year period, a stock tracks intrinsic business value to a large extent.  Jeffrey Ubben:

It’s still true that the biggest players in the public markets – particularly mutual funds and hedge funds – are not good at taking short-term pain for long-term gain.  The money’s very quick to move if performance falls off over short periods of time.  We don’t worry about headline risk – once we believe in an asset, we’re buying more on any dips because we’re focused on the end game three or four years out.

Mario Cibelli:

One of the last great arbitrages left is to be long-term-oriented when there is a large class of shareholders who have no tolerance for short-term setbacks.  So it’s interesting when stocks get beaten-up because a company misses earnings or the market reacts to a short-term business development.  It’s crazy to me when someone says something is cheap but doesn’t buy it because they think it won’t go anywhere for the next 6 to 12 months.  We have a pretty high tolerance for taking that pain if we see glory longer term.

Whitney Tilson wrote about a great story that value investor Bill Miller told.  Miller recalled that, early in his career, he was visiting an institutional money manager, to whom he was pitching R.J. Reynolds, then trading at four times earnings.  Miller:

“When I finished, the chief investment officer said: ‘That’s a really compelling case but we can’t own that.  You didn’t tell me why it’s going to outperform the market in the next nine months.’  I said I didn’t know if it was going to do that or not but that there was a very high probability it would do well over the next three to five years.

“He said: ‘How long have you been in this business?  There’s a lot of performance pressure, and performing three to five years down the road doesn’t cut it.  You won’t be in business then.  Clients expect you to perform right now.’

“So I said: ‘Let me ask you, how’s your performance?’

“He said: ‘It’s terrible, that’s why we’re under a lot of performance pressure.’

“I said: ‘If you bought stocks like this three years ago, your performance would be good right now and you’d be buying RJR to help your performance over the next three years.’”

Link: http://www.tilsonfunds.com/Patience%20can%20find%20a%20virtue%20in%20market%20inefficiency-FT-6-9-06.pdf

Many investors are so focused on shorter periods of time (a year or less).  They forget that the value of any business is ALL of its (discounted) future free cash flow, which often means 10-20 years or more.  David Herro:

I would assert the biggest reason quality companies sell at discounts to intrinsic value is time horizon.  Without short-term visibility, most investors don’t have the conviction or courage to hold a stock that’s facing some sort of challenge, either internally or externally generated.  It seems kind of ridiculous, but what most people in the market miss is that intrinsic value is the sum of ALL future cash flows discounted back to the present.  It’s not just the next six months’ earnings or the next year’s earnings.  To truly invest for the long term, you have to be able to withstand underperformance in the short term, and the fact of the matter is that most people can’t.

As Mason Hawkins observes, a company may be lagging now precisely because it’s making longer-term investments that will probably increase business value in the future:

Classic opportunities for us get back to time horizon.  A company reports a bad quarter, which disappoints Wall Street with its 90-day focus, but that might be for explainable temporary reasons or even because the company is making very positive long-term investments in the business.  Many times that investment increases the likely value of the company five years from now, but disappoints people who want the stock up tomorrow.

Whitney George:

We evaluate businesses over a full business cycle and probably our biggest advantage is an ability to buy things when most people can’t because the short-term outlook is lousy or very hard to judge.  It’s a good deal easier to know what’s likely to happen than to know precisely when it’s going to happen.

In general, humans are impatient and often discount multi-year investment gains far too much.  John Maynard Keynes: 

Human nature desires quick results, there is a particular zest in making money quickly, and remoter gains are discounted by the average man at a very high rate.



(Word cloud by Arloofs)

Automating of the investment process, including screening, is often more straightforward now than it has been, thanks to enormous advances in computing in the past two decades.

Will Browne:

We often start with screens on all aspects of valuation.  There are characteristics that have been proven over long periods to be associated with above-average rates of return:  low P/Es, discounts to book value, low debt/equity ratios, stocks with recent significant price declines, companies with patterns of insider buying and – something we’re paying a lot more attention to – stocks with high dividend yields.

Stephen Goddard:

Our basic screening process weights three factors equally:  return on tangible capital, the multiple of EBIT to enterprise value, and free cash flow yield.  We rank the universe we’ve defined on each factor individually from most attractive to least, and then combine the rankings and focus on the top 10%.

Carlo Cannell:

[We] basically spend our time trying to uncover the assorted investment misfits in the market’s underbrush that are largely neglected by the investment community.  One of the key metrics we assign to our companies is an analyst ratio, which is simply the number of analysts who follow the company.  The lower the better – as of the end of last year, about 65 percent of the companies in our portfolio had virtually no analyst coverage.

For some time now, it has been clear that simple quant models outperform experts in a wide variety of areas: http://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Quantitative value investor James O’Shaugnessy:

Models beat human forecasters because they reliably and consistently apply the same criteria time after time.  Models never vary.  They are never moody, never fight with their spouse, are never hung over from a night on the town, and never get bored.  They don’t favor vivid, interesting stories over reams of statistical data.  They never take anything personally.  They don’t have egos.  They’re not out to prove anything.  If they were people, they’d be the death of any party.

People on the other hand, are far more interesting.  It’s far more natural to react emotionally or to personalize a problem than it is to dispassionately review broad statistical occurrences – and so much more fun!  It’s much more natural for us to look at the limited set of our personal experiences and then generalize from this small sample to create a rule-of-thumb heuristic.  We are a bundle of inconsistencies, and although this tends to make us interesting, it plays havoc with our ability to successfully invest.

Buffett maintains (correctly) that the vast majority of investors, large or small, should invest in low-cost broad market index funds: http://boolefund.com/quantitative-microcap-value/

If you invest in a quantitative value fund focused on cheap micro caps with improving fundamentals, then you can reasonably expect to do about 7% (+/- 3%) better than the S&P 500 Index over time: http://boolefund.com/best-performers-microcap-stocks/

Will Browne:

When you have a model you believe in, that you’ve used for a long time and which is more empirical than intuitive, sticking with it takes the emotion away when markets are good or bad.  That’s been a central element of our success.  It’s the emotional dimension that drives people to make lousy, irrational decisions.



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

Quantitative Microcap Value

(Image:  Zen Buddha Silence by Marilyn Barbone.)

September 10, 2017

Warren Buffett:

Investing is simple but not easy.

(Photo by USA International Trade Administration)

There are four simple but important facts that virtually every investor should bear in mind when choosing an investment strategy:

  • A low-cost S&P 500 index fund is likely to outperform at least 90-95% of all investors, net of costs, over 4-5 decades.  For the vast majority of investors, an index fund is the best option.  That’s why Warren Buffett consistently suggests index funds not only to small investors, but also to mega-rich individuals, institutions, and pension funds.
  • You can do a bit better than an index fund over time if you adopt a quantitative value approach.  Properly implemented, this is like a value index and should do at least 1-2% better per year, net of costs, than an S&P 500 index fund.  However, quantitative value sometimes trails the market for years in a row.  If you can’t stick with it during such a period, then it’s better to invest in an S&P 500 index fund.
  • If you’re pondering quantitative value investing, you should also consider quantitative microcap value.  That’s what the Boole Microcap Fund does.  By screening for cheap micro caps with improving fundamentals, you can reasonably expect to outperform the S&P 500 by roughly 7% (+/- 3%) per year on average.  (Compared to the S&P 500, this microcap strategy could do 4% better per year, 10% better, or anything in-between.  There’s a high degree of randomness in investing.  The important thing is to stick with it for at least 5-10 years.)
  • Determining which strategy, or mix of strategies, is best for you requires humility.  The trouble is that, generally, we’re overconfident.  If asked, most of us believe we’re above average across a variety of dimensions such as looks, smarts, driving skill, academic ability, future well-being, and even luck.  (Men suffer from overconfidence more than women, perhaps in part because overconfidence was useful for hunting.)  We also suffer from other cognitive biases, all of which are the result of evolution.

On the topic of overconfidence, Buffett’s partner Charlie Munger likes this quote from Demosthenes:

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

(Charlie Munger at the 2010 Berkshire Hathaway shareholders meeting.  Photo by Nick Webb)

Let’s consider each point in a bit more detail.



Would you like to do better than approximately 90-95% of all investors, net of costs, over the next 4-5 decades?  It is surprisingly simple to achieve this result:  invest in a low-cost broad market index fund.  That’s why Warren Buffett, arguably the best investor ever, consistently recommends such an index fund to small investors and also to mega-rich individuals, institutions, and pension funds.

If your investment time horizon is measured in decades, a low-cost index fund is the obvious choice.  Passive investors on the whole will match the market.  Therefore, active investors will also match the market, before costs.  After costs, active investors (on the whole) will trail the market by 2-3% per year.  (John Bogle has done a terrific job telling this simple truth for a long time.)

  • 2-3% per year really adds up over the course of decades.  For example, if the average active approach returns 6.5% per year (net of costs) over the next 30 years, then $1 million will become $6.61 million.  If an S&P 500 index fund returns 9% per year (net) over the next 30 years, the same $1 million will become $13.27 million, twice as much.  (Moreover, the index fund is well-diversified across 500 American businesses.)

Even over the course of one decade, a low-cost broad market index fund can produce excellent results.  Warren Buffett’s 10-year bet against Protégé Partners demonstrates clearly that a simple index fund can beat the vast majority of all investors: http://boolefund.com/warren-buffett-jack-bogle/

After nine years, a group of a few hundred hedge funds – managed by intelligent, honest people who are highly incentivized to maximize their performance – is up a bit over 22%, net of costs.  Buffett’s investment in a Vanguard S&P 500 index fund is up 85.4%, net of costs.  That’s 7.1% per year for the index fund versus 2.2% per year for highly intelligent hedge fund (and fund of hedge fund) managers.

This illustrates how investing is simple but not easy.  Even if you restrict your examination to the most intelligent 10% of all investors, the long-term results are the same:  the vast majority of these investors will trail an S&P 500 index fund, net of costs, over time.

In the 2016 Berkshire Hathaway Letter to Shareholders, Buffett writes that, in his own lifetime, he identified – early on – ten investors he thought would beat the market over the long term.  Buffett was right about these ten.  But that’s only ten out of hundreds, or even thousands, of similarly intelligent investors.  Buffett:

There are no doubt many hundreds of people – perhaps thousands – whom I have never met and whose abilities would equal those of the people I’ve identified.  The job, after all, is not impossible.  The problem simply is that the great majority of managers who attempt to over-perform will fail. 

See: http://berkshirehathaway.com/letters/2016ltr.pdf

In a nutshell, you can do better than about 90-95% of all investors over 4-5 decades by investing in an S&P 500 index fund.  This is purely a function of costs, which average 2-3% per year for active approaches.  Therefore, for the vast majority of investors, whether large or small, you should follow Warren Buffett’s advice:  simply invest in American business by investing in a low-cost broad market index fund.

(BNSF, owned by Berkshire Hathaway.  Photo by Winnie Chao.)



A seminal paper on quantitative deep value investing is by Josef Lakonishok, Andrei Shleifer, and Robert Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

LSV (Lakonishok, Schleifer, and Vishny) were so convinced by their research that they launched LSV Asset Management, which currently manages $105 billion.  LSV’s quantitative deep value strategies have beaten their respective benchmark indices by at least 1-2% per year over time.



(Illustration by Madmaxer.)

Check out this summary of the CRSP Decile-Based Size and Return Data from 1927 to 2015:

Decile Market Cap-Weighted Returns Equal Weighted Returns Number of Firms (year-end 2015) Mean Firm Size (in millions)
1 9.29% 9.20% 173 84,864
2 10.46% 10.42% 178 16,806
3 11.08% 10.87% 180 8,661
4 11.32% 11.10% 221 4,969
5 12.00% 11.92% 205 3,151
6 11.58% 11.40% 224 2,176
7 11.92% 11.87% 300 1,427
8 12.00% 12.27% 367 868
9 11.40% 12.39% 464 429
10 12.50% 17.48% 1,298 107
9+10 11.85% 16.14% 1,762 192

(You can find the data for various deciles here:  http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)

The smallest two deciles – 9+10 – comprise microcap stocks, which typically are stocks with market caps below $500 million.  What stands out is the equal weighted returns of the 9th and 10th size deciles from 1927 to 2015:

Microcap equal weighted returns = 16.14% per year

Large-cap equal weighted returns = ~11% per year

In practice, microcap annual returns will be 1-2% lower because of the difficulty (due to illiquidity) of entering and exiting positions.  So we should say that an equal weighted microcap approach has returned 14% per year from 1927 to 2015.  Still, 3% more per year than large caps really adds up over the course of decades.

  • Most professional investors ignore micro caps as too small for their portfolios.  This causes many micro caps to get very cheap.  And that’s why an equal weighted strategy – applied to micro caps – tends to work well.

Value Screen: +2-3%

By adding a value screen – e.g., low EV/EBIT or low P/E – to a microcap strategy, it is possible to add 2-3% per year.  To maximize the odds of achieving this additional margin of outperformance, you should adopt a quantitative approach.

Improving Fundamentals: +2-3%

You can further boost performance by screening for improving fundamentals.  One excellent way to do this is using the Piotroski F_Score, which works best for cheap micro caps.  See:  http://boolefund.com/joseph-piotroski-value-investing/

Bottom Line

In sum, over the course of several decades, a quantitative value strategy – applied to cheap microcap stocks with improving fundamentals – has high odds of returning at least 7% (+/- 3%) more per year than a low-cost S&P 500 index fund.



(Illustration by Alain Lacroix.)

Human intuition often works remarkably well.  But when a good decision requires careful reasoning – using logic, math, or statistics – our intuition causes systematic errors.  I wrote about cognitive biases here: http://boolefund.com/cognitive-biases/

Munger’s treatment of misjudgment is more comprehensive: http://boolefund.com/the-psychology-of-misjudgment/



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

Deep Value: Profiting from Mean Reversion

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 27, 2017

The essence of deep value investing is systematically buying stocks at low multiples in order to profit from future mean reversion.  Sometimes it seems that there are misconceptions about deep value investing.

  • First, deep value stocks have on occasion been called cheap relative to future growth.  But it’s often more accurate to say that deep value stocks are cheap relative to normalized earnings or cash flows.
  • Second, the cheapness of deep value stocks has often been said to be relative to “net tangible assets.”  However, in many cases, even including stocks at a discount to tangible assets, mean reversion relates to the future normalized earnings or cash flows that the assets can produce.
  • Third, typically more than half of deep value stocks underperform the market.  And deep value stocks are more likely to be distressed than average stocks.  Do these facts imply that a deep value investment strategy is riskier than average?  No…

Have you noticed these misconceptions?  I’m curious to hear your take.  Please let me know.

Here are the sections in this blog post:

  • Introduction
  • Mean Reversion as “Return to Normal” instead of “Growth”
  • Revenues, Earnings, Cash Flows, NOT Asset Values
  • Is Deep Value Riskier?
  • A Long Series of Favorable Bets
  • “Cigar Butt’s” vs. See’s Candies
  • Microcap Cigar Butt’s



Deep value stocks tend to fit two criteria:

  • Deep value stocks trade at depressed multiples.
  • Deep value stocks have depressed fundamentals – they have generally been doing terribly in terms of revenues, earnings, or cash flows, and often the entire industry is doing poorly.

The essence of deep value investing is systematically buying stocks at low multiples in order to profit from future mean reversion.

  • Low multiples include low P/E (price-to-earnings), low P/B (price-to-book), low P/CF (price-to-cash flow), and low EV/EBIT (enterprise value-to-earnings before interest and taxes).
  • Mean reversion implies that, in general, deep value stocks are underperforming their economic potential.  On the whole, deep value stocks will experience better future economic performance than is implied by their current stock prices.

If you look at deep value stocks as a group, it’s a statistical fact that many will experience better revenues, earnings, or cash flows in the future than what is implied by their stock prices.  This is due largely to mean reversion.  The future economic performance of these deep value stocks will be closer to normal levels than their current economic performance.

Moreover, the stock price increases of the good future performers will outweigh the languishing stock prices of the poor future performers.  This causes deep value stocks, as a group, to outperform the market over time.

Two important notes:

  1. Generally, for deep value stocks, mean reversion implies a return to more normal levels of revenues, earnings, or cash flows.  It does not often imply growth above and beyond normal levels.
  2. For most deep value stocks, mean reversion relates to future economic performance and not to tangible asset value per se.

(1) Mean Reversion as Return to More Normal Levels

One of the best papers on deep value investing is by Josef Lakonishok, Andrei Shleifer, and Robert Vishny (1994), “Contrarian Investment, Extrapolation, and Risk.”  Link: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

LSV (Lakonishok, Schleifer, and Vishny) correctly point out that deep value stocks are better identified by using more than one multiple.  LSV Asset Management currently manages $105 billion using deep value strategies that rely simultaneously on several metrics for cheapness, including low P/E and low P/CF.

  • In Quantitative Value (Wiley, 2012), Tobias Carlisle and Wesley Gray find that low EV/EBIT outperformed every other measure of cheapness, including composite measures.
  • However, James O’Shaughnessy, in What Works on Wall Street (McGraw-Hill, 2011), demonstrates – with great thoroughness – that, since the mid-1920’s, composite approaches (low P/S, P/E, P/B, EV/EBITDA, P/FCF) have been the best performers.
  • Any single metric may be more easily arbitraged away by a powerful computerized approach.  Walter Schloss once commented that low P/B was working less well because many more investors were using it.  (In recent years, low P/B hasn’t worked.)

LSV explain why mean reversion is the essence of deep value investing.  Investors, on average, are overly pessimistic about stocks at low multiples.  Investors understimate the mean reversion in future economic performance for these out-of-favor stocks.

However, in my view, the paper would be clearer if it used (in some but not all places) “return to more normal levels of economic performance” in place of “growth.”  Often it’s a return to more normal levels of economic performance – rather than growth above and beyond normal levels – that defines mean reversion for deep value stocks.

(2) Revenues, Earnings, Cash Flows NOT Net Asset Values

Buying at a low price relative to tangible asset value is one way to implement a deep value investing strategy.  Many value investors have successfully used this approach.  Examples include Ben Graham, Walter Schloss, Peter Cundill, John Neff, and Marty Whitman.

Warren Buffett used this approach in the early part of his career.  Buffett learned this method from his teacher and mentor, Ben Graham.  Graham called this the “net-net” approach.  You take net working capital minus ALL liabilities.  If the stock price is below that level, and if you buy a basket of such “net-net’s,” you can’t help but do well over time.  These are extremely cheap stocks, on average.  (The only catch is that there must be enough net-net’s in existence to form a basket, which is not always the case.)

Buffett on “cigar butts”:

…I call it the cigar butt approach to investing.  You walk down the street and you look around for a cigar butt someplace.  Finally you see one and it is soggy and kind of repulsive, but there is one puff left in it.  So you pick it up and the puff is free – it is a cigar butt stock.  You get one free puff on it and then you throw it away and try another one.  It is not elegant.  But it works.  Those are low return businesses.

Link: http://intelligentinvestorclub.com/downloads/Warren-Buffett-Florida-Speech.pdf

But most net-net’s are NOT liquidated.  Rather, there is mean reversion in their future economic performance – whether revenues, earnings, or cash flows.  That’s not to say there aren’t some bad businesses in this group.  For net-net’s, when economic performance returns to more normal levels, typically you sell the stock.  You don’t (usually) buy and hold net-net’s.

Sometimes net-net’s are acquired.  But in many of these cases, the acquirer is focused mainly on the earnings potential of the assets.  (Non-essential assets may be sold, though.)

In sum, the specific deep value method of buying at a discount to net tangible assets has worked well in general ever since Graham started doing it.  And net tangible assets do offer additional safety.  That said, when these particular cheap stocks experience mean reversion, often it’s because revenues, earnings, or cash flows return to “more normal” levels.  Actual liquidation is rare.



According to a study done by Joseph Piotroski from 1976 to 1996 – discussed below – although a basket of deep value stocks clearly beats the market over time, only 43% of deep value stocks outperform the market, while 57% underperform.  By comparison, an average stock has a 50% chance of outperforming the market and a 50% chance of underperforming.

Let’s assume that the average deep value stock has a 57% chance of underperforming the market, while an average stock has only a 50% chance of underperforming.  This is a realistic assumption not only because of Piotroski’s findings, but also because the average deep value stock is more likely to be distressed (or to have problems) than the average stock.

Does it follow that the reason deep value investing does better than the market over time is that deep value stocks are riskier than average stocks?

It is widely accepted that deep value investing does better than the market over time.  But there is still disagreement about how risky deep value investing is.  Strict believers in the EMH (Efficient Markets Hypothesis) – such as Eugene Fama and Kenneth French – argue that value investing must be unambiguously riskier than simply buying an S&P 500 Index fund.  On this view, the only way to do better than the market over time is by taking more risk.

Now, it is generally true that the average deep value stock is more likely to underperform the market than the average stock.  And the average deep value stock is more likely to be distressed than the average stock.

But LSV show that a deep value portfolio does better than an average portfolio, especially during down markets.  This means that a basket of deep value stocks is less risky than a basket of average stocks.

  • A “portfolio” or “basket” of stocks refers to a group of stocks.  Statistically speaking, there must be at least 30 stocks in the group.  In the case of LSV’s study – like most academic studies of value investing – there are hundreds of stocks in the deep value portfolio.  (The results are similar over time whether you have 30 stocks or hundreds.)

Moreover, a deep value portfolio only has slightly more volatility than an average portfolio, not nearly enough to explain the significant outperformance.  In fact, when looked at more closely, deep value stocks as a group have slightly more volatility mainly because of upside volatility – relative to the broad market – rather than because of downside volatility.  This is captured not only by the clear outperformance of deep value stocks as a group over time, but also by the fact that deep value stocks do much better than average stocks in down markets.

Deep value stocks, as a group, not only outperform the market, but are less risky.  Ben Graham, Warren Buffett, and other value investors have been saying this for a long time.  After all, the lower the stock price relative to the value of the business, the less risky the purchase, on average.  Less downside implies more upside.



Let’s continue to assume that the average deep value stock has a 57% chance of underperforming the market.  And the average deep value stock has a greater chance of being distressed than the average stock.  Does that mean that the average individual deep value stock is riskier than the average stock?

No, because the expected return on the average deep value stock is higher than the expected return on the average stock.  In other words, on average, a deep value stock has more upside than downside.

Put very crudely, in terms of expected value:

[(43% x upside) – (57% x downside)] > [avg. return]

43% times the upside, minus 57% times the downside, is greater than the return from the average stock (or from the S&P 500 Index).

The crucial issue relates to making a long series of favorable bets.  Since we’re talking about a long series of bets, let’s again consider a portfolio of stocks.

  • Recall that a “portfolio” or “basket” of stocks refers to a group of at least 30 stocks.

A portfolio of average stocks will simply match the market over time.  That’s an excellent result for most investors, which is why most investors should just invest in index funds: http://boolefund.com/warren-buffett-jack-bogle/

A portfolio of deep value stocks will, over time, do noticeably better than the market.  Year in and year out, approximately 57% of the deep value stocks will underperform the market, while 43% will outperform.  But the overall outperformance of the 43% will outweigh the underperformance of the 57%, especially over longer periods of time.  (57% and 43% are used for illustrative purposes here.  The actual percentages vary.)

Say that you have an opportunity to make the same bet 1,000 times in a row, and that the bet is as follows:  You bet $1.  You have a 60% chance of losing $1, and a 40% chance of winning $2.  This is a favorable bet because the expected value is positive: 40% x $2 = $0.80, while 60% x $1 = $0.60.  If you made this bet repeatedly over time, you would average $0.20 profit on each bet, since $0.80 – $0.60 = $0.20.

If you make this bet 1,000 times in a row, then roughly speaking, you will lose 60% of them (600 bets) and win 40% of them (400 bets).  But your profit will be about $200.  That’s because 400 x $2 = $800, while 600 x $1 = $600.  $800 – $600 = $200.

Systematically investing in deep value stocks is similar to the bet just described.  You may lose 57% of the bets and win 43% of the bets.  But over time, you will almost certainly profit because the average upside is greater than the average downside.  Your expected return is also higher than the market return over the long term.



In his 1989 Letter to Shareholders, Buffett writes about his “Mistakes of the First Twenty-Five Years,” including a discussion of “cigar butt” (deep value) investing:

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. 

Link: http://www.berkshirehathaway.com/letters/1989.html

Buffett has made it clear that cigar butt (deep value) investing does work.  In fact, fairly recently, Buffett bought at basket of cigar butts in South Korea.  The results were excellent.  But he did this in his personal portfolio.

This highlights a major reason why Buffett evolved from investing in cigar butts to investing in higher quality businesses:  size of investable assets.  When Buffett was managing a few hundred million dollars or less, which includes when he managed an investment partnership, Buffett achieved outstanding results in part by investing in cigar butts.  But when investable assets swelled into the billions of dollars at Berkshire Hathaway, Buffett began investing in higher quality companies.

  • Cigar butt investing works best for micro caps.  But micro caps won’t move the needle if you’re investing many billions of dollars.

The idea of investing in higher quality companies is simple:  If you can find a business with a sustainably high ROE – based on a sustainable competitive advantage – and if you can hold that stock for a long time, then your returns as an investor will approximate the ROE (return on equity).  This assumes that the company can continue to reinvest all of its earnings at the same ROE, which is extremely rare when you look at multi-decade periods.

  • The quintessential high-quality business that Buffett and Munger purchased for Berkshire Hathaway is See’s Candies.  They paid $25 million for $8 million in tangible assets in 1972.  Since then, See’s Candies has produced over $2 billion in (pre-tax) earnings, while only requiring a bit over $40 million in reinvestment.
  • See’s turns out more than $80 million in profits each year.  That’s over 100% ROE (return on equity), which is extraordinary.  But that’s based mostly on assets in place.  The company has not been able to reinvest most of its earnings.  Instead, Buffett and Munger have invested the massive excess cash flows in other good opportunities – averaging over 20% annual returns on these other investments (for most of the period from 1972 to present).

Furthermore, buying and holding stock in a high-quality business brings enormous tax advantages over time because you never have to pay taxes until you sell.  Thus, as a high-quality business – with sustainably high ROE – compounds value over many years, a shareholder who never sells receives the maximum benefit of this compounding.

Yet it’s extraordinarily difficult to find a business that can sustain ROE at over 20% – including reinvested earnings – for decades.  Buffett has argued that cigar butt (deep value) investing produces more dependable results than investing exclusively in high-quality businesses.  Very often investors buy what they think is a higher-quality business, only to find out later that they overpaid because the future performance does not match the high expectations that were implicit in the purchase price.  Indeed, this is what LSV show in their famous paper (discussed above) in the case of “glamour” (or “growth”) stocks.



Buffett has said that you can do quite well as an investor, if you’re investing smaller amounts, by focusing on cheap micro caps.  In fact, Buffett has maintained that he could get 50% per year if he could invest only in cheap micro caps.

Investing systematically in cheap micro caps can often lead to higher long-term results than the majority of approaches that invest in high-quality stocks.

First, micro caps, as a group, far outperform every other category.  See the historical performance here: http://boolefund.com/best-performers-microcap-stocks/

Second, cheap micro caps do even better.  Systematically buying at low multiples works over the course of time, as clearly shown by LSV and many others.

Finally, if you apply the Piotroski F-Score to screen cheap micro caps for improving fundamentals, performance is further boosted:  The biggest improvements in performance are concentrated in cheap micro caps with no analyst coverage.  See: http://boolefund.com/joseph-piotroski-value-investing/



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

Cognitive Biases

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 20, 2017

In the great book Thinking, Fast and Slow (2011), Daniel Kahneman explains in detail two different ways of thinking that human beings use.  Kahneman refers to them as System 1 and System 2, which he defines as follows:

System 1:   Operates automatically and quickly, with little or no effort or sense of voluntary control.  Makes instinctual or intuitive decisions – typically based on heuristics.

System 2:   Allocates attention to the effortful mental activities that demand it, including complex computations involving logic, math, or statistics.  The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.

Heuristics are simple rules we use – via System 1 – to form judgments or make decisions.  Heuristics are mental shortcuts whereby we simplify a complex situation in order to jump to a quick conclusion.

Most of the time, heuristics work well.  We can immediately notice a shadow in the grass, alerting us to the possible presence of a lion.  And we can automatically read people’s faces, drive a car on an empty road, do easy math, or understand simple language.  (For more on System 1, see the last section of this blog post.)

However, if we face a situation that requires the use of logic, math, or statistics to reach a good judgment or decision, heuristics lead to systematic errors.  These errors are cognitive biases.

Let’s examine some of the main cognitive biases:

  • anchoring effect
  • availability bias, vividness bias, recency bias
  • confirmation bias
  • hindsight bias
  • overconfidence
  • narrative fallacy
  • information and overconfidence
  • self-attribution bias



anchoring effect:   people tend to use any random number as a baseline for estimating an unknown quantity, despite the fact that the unknown quantity is totally unrelated to the random number.

Daniel Kahneman and Amos Tversky did one experiment where they spun a wheel of fortune, but they had secretly programmed the wheel so that it would stop on 10 or 65.   After the wheel stopped, participants were asked to estimate the percentage of African countries in the UN.   Participants who saw “10” on the wheel guessed 25% on average, while participants who saw “65” on the wheel guessed 45% on average, a huge difference.

Behavioral finance expert James Montier has run his own experiment on anchoring.   People are asked to write down the last four digits of their phone number.   Then they are asked whether the number of doctors in their capital city is higher or lower than the last four digits of their phone number.   Results:  Those whose last four digits were greater than 7000 on average report 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 doctors.  (James Montier, Behavioural Investing, Wiley 2007, page 120)

Those are just two experiments out of many.  The anchoring effect is “one of the most reliable and robust results of experimental psychology” (page 119, Kahneman).  Furthermore, Montier observes that the anchoring effect is one reason why people cling to financial forecasts, despite the fact that most financial forecasts are either wrong, useless, or impossible to time.

When faced with the unknown, people will grasp onto almost anything.  So it is little wonder that an investor will cling to forecasts, despite their uselessness.  (Montier, page 120)



availability bias:   people tend to overweight evidence that comes easily to mind.

Related to the availability bias are vividness bias and recency bias.  People typically overweight facts that are vivid (e.g., plane crashes or shark attacks).   People also overweight facts that are recent (partly because they are more vivid).

Note:  It’s also natural for people to assume that hard-won evidence or insight must be worth more.  But often that’s not true, either.



confirmation bias:   people tend to search for, remember, and interpret information in a way that confirms their pre-existing beliefs or hypotheses.

Confirmation bias makes it quite difficult for many people to improve upon or supplant their existing beliefs or hypotheses.   This bias also tends to make people overconfident about existing beliefs or hypotheses, since all they can see are supporting data.

We know that our System 1 (intuition) often errors when it comes to forming and testing hypotheses. First of all, System 1 always forms a coherent story (including causality), irrespective of whether there are truly any logical connections at all among various things in experience.  Furthermore, when System 1 is facing a hypothesis, it automatically looks for confirming evidence.

But even System 2 – the logical and mathematical system that humans possess and can develop – by nature uses a positive test strategy:

A deliberate search for confirming evidence, known as positive test strategy, is also how System 2 tests a hypothesis.  Contrary to the rules of philosophers of science, who advise testing hypotheses by trying to refute them, people (and scientists, quite often) seek data that are likely to be compatible with the beliefs they currently hold.  (page 81, Kahneman)

Thus, the habit of always looking for disconfirming evidence of our hypotheses – especially our “best-loved hypotheses” – is arguably the most important intellectual habit we could develop in the never-ending search for wisdom and knowledge.

Charles Darwin is a wonderful model for people in this regard.  Darwin was far from being a genius in terms of IQ.  Yet Darwin trained himself always to search for facts and evidence that would contradict his hypotheses.  Charlie Munger explains in “The Psychology of Human Misjudgment” (see Poor Charlie’s Alamanack: The Wit and Wisdom of Charles T.  Munger, expanded 3rd edition):

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. 



Hindsight bias:   the tendency, after an event has occurred, to see the event as having been predictable, despite there having been little or no objective basis for predicting the event prior to its occurrence.

Hindsight bias is also called the “knew-it-all-along effect” or “creeping determinism.”  (See: http://en.wikipedia.org/wiki/Hindsight_bias)

Kahneman writes about hindsight bias as follows:

Your inability to reconstruct past beliefs will inevitably cause you to underestimate the extent to which you were surprised by past events.   Baruch Fischhoff first demonstrated this ‘I-knew-it-all-along’ effect, or hindsight bias, when he was a student in Jerusalem.  Together with Ruth Beyth (another of our students), Fischhoff conducted a survey before President Richard Nixon visited China and Russia in 1972.   The respondents assigned probabilities to fifteen possible outcomes of Nixon’s diplomatic initiatives.   Would Mao Zedong agree to meet with Nixon?   Might the United States grant diplomatic recognition to China?   After decades of enmity, could the United States and the Soviet Union agree on anything significant?

After Nixon’s return from his travels, Fischhoff and Beyth asked the same people to recall the probability that they had originally assigned to each of the fifteen possible outcomes.  The results were clear.  If an event had actually occurred, people exaggerated the probability that they had assigned to it earlier.  If the possible event had not come to pass, the participants erroneously recalled that they had always considered it unlikely.   Further experiments showed that people were driven to overstate the accuracy not only of their original predictions but also of those made by others.   Similar results have been found for other events that gripped public attention, such as the O.J. Simpson murder trial and the impeachment of President Bill Clinton.   The tendency to revise the history of one’s beliefs in light of what actually happened produces a robust cognitive illusion.  (pages 202-3, my emphasis)

Concludes Kahneman:

The sense-making machinery of System 1 makes us see the world as more tidy, simple, predictable, and coherent that it really is.  The illusion that one has understood the past feeds the further illusion that one can predict and control the future.  These illusions are comforting.   They reduce the anxiety we would experience if we allowed ourselves to fully acknowledge the uncertainties of existence.  (page 204-5, my emphasis)



Overconfidence is such as widespread cognitive bias among people that Kahneman devotes Part 3 of his book entirely to this topic.  Kahneman says in his introduction:

The difficulties of statistical thinking contribute to the main theme of Part 3, which describes a puzzling limitation of our mind:  our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in.   We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events.   Overconfidence is fed by the illusory certainty of hindsight.   My views on this topic have been influenced by Nassim Taleb, the author of The Black Swan.  (pages 14-5)

Several studies have shown that roughly 90% of drivers rate themselves as above average.  For more on overconfidence, see: https://en.wikipedia.org/wiki/Overconfidence_effect



In The Black Swan, Nassim Taleb writes the following about the narrative fallacy:

The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship, upon them.  Explanations bind facts together.  They make them all the more easily remembered;  they help them make more sense.  Where this propensity can go wrong is when it increases our impression of understanding.  (page 63-4)

The narrative fallacy is central to many of the biases and misjudgments mentioned by Daniel Kahneman and Charlie Munger.  The human brain, whether using System 1 (intuition) or System 2 (logic), always looks for or creates logical coherence among random data.  Often System 1 is right when it assumes causality; thus, System 1 is generally helpful, thanks to evolution.  Furthermore, System 2, by searching for underlying causes or coherence, has, through careful application of the scientific method over centuries, developed a highly useful set of scientific laws by which to explain and predict various phenomena.

The trouble comes when the data or phenomena in question are “highly random” – or inherently unpredictable (at least for the time being).  In these areas, System 1 makes predictions that are often very wrong.  And even System 2 assumes necessary logical connections when there may not be any – at least, none that can be discovered for some time.

Note:  The eighteenth century Scottish philosopher (and psychologist) David Hume was one of the first to clearly recognize the human brain’s insistence on always assuming necessary logical connections in any set of data or phenomena.



In Behavioural Investing, James Montier explains a study done by Paul Slovic (1973).  Eight experienced bookmakers were shown a list of 88 variables found on a typical past performance chart on a horse.  Each bookmaker was asked to rank the piece of information by importance.

Then the bookmakers were given data for 40 past races and asked to rank the top five horses in each race.  Montier:

Each bookmaker was given the past data in increments of the 5, 10, 20, and 40 variables he had selected as most important.  Hence each bookmaker predicted the outcome of each race four times – once for each of the information sets.  For each prediction the bookmakers were asked to give a degree of confidence ranking in their forecast.  (page 136)


Accuracy was virtually unchanged, regardless of the number of pieces of information the bookmaker was given (5, 10, 20, then 40).

But confidence skyrocketed as the number of pieces of information increased (5, 10, 20, then 40).

This same result has been found in a variety of areas.  As people get more information, the accuracy of their judgments or forecasts typically does not change at all, while their confidence in the accuracy of their judgments or forecasts tends to increase dramatically.



self-attribution bias:   people tend to attribute good outcomes to their own skill, while blaming bad outcomes on bad luck.

This ego-protective bias prevents people from recognizing and learning from their mistakes.  This bias also contributes to overconfidence.



When we are thinking of who we are, we use System 2 to define ourselves.  But, writes Kahneman, System 1 effortlessly originates impressions and feelings that are the main source of the explicit beliefs and deliberate choices of System 2.

Kahneman lists, “in rough order of complexity,” examples of the automatic activities of System 1:

  • Detect that one object is more distant than another.
  • Orient to the source of a sudden sound.
  • Complete the phrase “Bread and…”
  • Make a “disgust face” when shown a horrible picture.
  • Detect hostility in a voice.
  • Answer 2 + 2 = ?
  • Read words on large billboards.
  • Drive a car on an empty road.
  • Find a strong move in chess (if you are a chess master).
  • Understand simple sentences.
  • Recognize that “a meek and tidy soul with a passion for detail” resembles an occupational stereotype.

Kahneman writes that System 1 and System 2 work quite well generally:

The division of labor between System 1 and System 2 is highly efficient:  it minimizes effort and optimizes performance.   The arrangement works well most of the time because System 1 is generally very good at what it does:  its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate.

“Thinking fast” usually works fine.  System 1 is remarkably good at what it does, thanks to evolution.  Kahneman:

System 1 is designed to jump to conclusions from little evidence.

However, when we face situations that are unavoidably complex, System 1 systematically jumps to the wrong conclusions.  In these situations, we have to train ourselves to “think slow” and reason our way to a good decision.

For the curious, here’s the most comprehensive list of cognitive biases I’ve seen: https://en.wikipedia.org/wiki/List_of_cognitive_biases



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

The Psychology of Misjudgment

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 13, 2017

In order to reach our potential as human beings, we have to study our mistakes, including what causes or leads to those mistakes.

Psychologists have identified cognitive biases we all have (from evolution) that regularly lead to mistakes.  Here’s a short list: http://boolefund.com/cognitive-biases/

Below is a longer, more comprehensive list of twenty-four psychological tendencies described by Charlie Munger in his talk, “The Psychology of Human Misjudgment.”  See: https://cogly.org/wp-content/uploads/2017/01/psychology-misjudgement-munger.pdf

Bear in mind this comment by Munger:

Psychological tendencies tend to be both numerous and inseparably intertwined, now and forever, as they interplay in life.

Here are the twenty-four psychological tendencies Munger discusses:

  1.  Reward and Punishment Superresponse Tendency
  2.  Liking/Loving Tendency
  3.  Disliking/Hating Tendency
  4.  Doubt-Avoidance Tendency
  5.  Inconsistency-Avoidance Tendency
  6.  Curiosity Tendency
  7.  Kantian Fairness Tendency
  8.  Envy/Jealousy Tendency
  9.  Reciprocation Tendency
  10.  Influence-from-Mere Association Tendency
  11.  Simple, Pain-Avoiding Psychological Denial
  12.  Excessive Self-Regard Tendency
  13.  Overoptimism Tendency
  14.  Deprival Superreaction Tendency
  15.  Social-Proof Tendency
  16.  Contrast-Misreaction Tendency
  17.  Stress-Influence Tendency
  18.  Availability-Misweighing Tendency
  19.  Use-It-or-Lose-It Tendency
  20.  Drug-Misinfluence Tendency
  21.  Senescence-Misinfluence Tendency
  22.  Authority-Misinfluence Tendency
  23.  Twaddle-Tendency
  24.  Reason-Respecting Tendency

(At the end, Munger gives his answers to a couple of excellent questions, plus a list of good examples to remember.)



Munger introduces his discussion:

Some psychology professors like to demonstrate the inadequacy of contrast-based perception by having students put one hand in a bucket of hot water and one hand in a bucket of cold water.  They are then suddenly asked to remove both hands and place them in a single bucket of room-temperature water.  Now, with both hands in the same water, one hand feels as if it has just been put in cold water and the other hand feels as if it has just been placed in hot water.  When one thus sees perception so easily fooled by mere contrast, where a simple temperature gauge would make no error, and realizes that cognition mimics perception in being misled by mere contrast, he is well on the way toward understanding, not only how magicians fool one, but also how life will fool one.  This can occur, through deliberate human manipulation or otherwise, if one doesn’t take certain precautions against often-wrong effects from generally useful tendencies in his perception and cognition.  (pg. 4)

Our psychological tendencies are generally useful, being the result of evolution.  But in some situations, these tendencies lead to errors.


(1)  Reward and Punishment Superresponse Tendency

Munger observes that hardly a year passes when he does not get some surprise from how powerful incentives are.

Never, ever think about something else when you should be thinking about incentives.


One of the most important consequences of incentive superpower is what I call ‘incentive caused bias.’  A man has an acculturated nature making him a pretty decent fellow, and yet, driven both consciously and subconsciously by incentives, he drifts into immoral behavior in order to get what he wants, a result he facilitates by rationalizing his bad behavior, like the salesmen at Xerox who harmed customers in order to maximize their sales commissions.  (pg. 6)

Munger gives an example of a surgeon who “over the years sent bushel baskets full of normal gall bladders down to the pathology lab in the leading hospital in Lincoln, Nebraska.”  One of the doctors who participated in the removals was a family friend (of the Mungers), so Munger asked him if the surgeon in question thought, ‘Here’s a way for me to exercise my talents and make a high living by doing a few maimings and murders every year in the course of routine fraud.’  Munger’s friend answered: ‘Hell no, Charlie.  He thought that the gall bladder was the source of all medical evil, and, if you really loved your patients, you couldn’t get that organ out rapidly enough.’

Munger comments:

Now that’s an extreme case, but in lesser strength, the cognitive drift of that surgeon is present in every profession and in every human being.  And it causes perfectly terrible behavior.  Consider the presentations of brokers selling commercial real estate and businesses.  I’ve never seen one that I thought was even with hailing distance of objective truth….

On the other hand, you can use the power of incentives – even using as rewards things you already possess! – to manipulate your own behavior for the better.  The business version of ‘Granny’s Rule’ is to force yourself daily to do the unpleasant and necessary tasks first, before rewarding yourself by proceeding to the pleasant tasks.


(2)  Liking/Loving Tendency


One very practical consequence of Liking/Loving Tendency is that it acts as a conditioning device that makes the liker or lover tend (1) to ignore faults of, and comply with wishes of, the object of his affection, (2) to favor people, products, and actions merely associated with the object of his affection (as we shall see when we get to ‘Influence-from-Mere-Association Tendency,’) and (3) to distort other facts to facilitate love.  (pg. 9)

We’re naturally biased, so we have to be careful in some situations.

On the other hand, Munger points out that loving admirable persons and ideas can be very beneficial.

…a man who is so constructed that he loves admirable persons and ideas with a special intensity has a huge advantage in life.  This blessing came to both Buffett and myself in large measure, sometimes from the same persons and ideas.  One common, beneficial example for us both was Warren’s uncle, Fred Buffett, who cheerfully did the endless grocery-store work that Warren and I ended up admiring from a safe distance.  Even now, after I have known so many other people, I doubt if it is possible to be a nicer man than Fred Buffett was, and he changed me for the better.

Warren Buffett:

If you tell me who your heroes are, I’ll tell you how you’re gonna turn out.  It’s really important in life to have the right heroes.  I’ve been very lucky in that I’ve probably had a dozen or so major heroes.  And none of them have ever let me down.  You want to hang around with people that are better than you are.  You will move in the direction of the crowd that you associate with.


(3)  Disliking/Hating Tendency

Munger notes that Switzerland and the United States have clever political arrangements to “channel” the hatreds and dislikings of individuals and groups into nonlethal patterns including elections.

But the dislikings and hatreds never go away completely…  And we also get the extreme popularity of very negative political advertising in the United States.

Munger explains:

Disliking/Hating Tendency also acts as a conditioning device that makes the disliker/hater tend to (1) ignore virtues in the object of dislike, (2) dislike people, products, and actions merely associated with the object of dislike, and (3) distort other facts to facilitate hatred.

Distortion of that kind is often so extreme that miscognition is shockingly large.  When the World Trade center was destroyed, many Muslims concluded that the Hindus did it, while many Arabs concluded that the Jews did it.  Such factual distortions often make mediation between opponents locked in hatred either difficult or impossible.  Mediations between Israelis and Palestinians are difficult because facts in one side’s history overlap very little with facts from the other side’s.


(4)  Doubt-Avoidance Tendency

Munger says:

The brain of man is programmed with a tendency to quickly remove doubt by reaching some decision.  It is easy to see how evolution would make animals, over the course of eons, drift toward such quick elimination of doubt.  After all, the one thing that is surely counterproductive for a prey animal that is threatened by a predator is to take a long time in deciding what to do.  And so man’s Doubt Avoidance Tendency is quite consistent with the history of his ancient, nonhuman ancestors.

Munger then observes:

What triggers Doubt-Avoidance Tendency?  Well, an unthreatened man, thinking of nothing in particular, is not being prompted to remove doubt through rushing to some decision.  As we shall see later when we get to Social-Proof Tendency and Stress-Influence Tendency, what usually triggers Doubt-Avoidance Tendency is some combination of puzzlement and stress.  Both of these factors naturally occur in facing religious issues.  (page 10)


(5)  Inconsistency-Avoidance Tendency

Munger explains:

The brain of man conserves programming space by being reluctant to change, which is a form of inconsistency avoidance.  We see this in all human habits, constructive and destructive.  Few people can list a lot of bad habits that they have eliminated, and some people cannot identify even one of these.  Instead, practically everyone has a great many bad habits he has long maintained despite their being known as bad….  chains of habit that were too light to be felt before they became too heavy to be broken.

If you’re wise, self-improvement is lifelong:

The rare life that is wisely lived has in it many good habits maintained and many bad habits avoided or cured.


It is easy to see that a quickly reached conclusion, triggered by Doubt-Avoidance Tendency, when combined with a tendency to resist any change in that conclusion, will naturally cause a lot of errors in cognition for modern man.  And so it observably works out.  We all deal much with others whom we correctly diagnose as imprisoned in poor conclusions that are maintained by mental habits they formed early and will carry to their graves.

So great is the bad-decision problem caused by Inconsistency-Avoidance Tendency that our courts have adopted important strategies against it.  For instance, before making decisions, judges and juries are required to hear long and skillful presentations of evidence and argument from the side they will not naturally favor, given their ideas in place.  And this helps prevent considerable bad thinking from ‘first conclusion bias.’  Similarly, other modern decision makers will often force groups to consider skillful counterarguments before making decisions. 

And proper education is one long exercise in high cognition so that our wisdom becomes strong enough to destroy wrong thinking, maintained by resistance to change.

Munger points out that, as humans, we collect many attitudes and conclusions that are wrong:

And so, people tend to accumulate large mental holdings of fixed conclusions and attitudes that are not often reexamined or changed, even though there is plenty of good evidence that they are wrong.

But we can develop good mental habits by modeling people who excel at minimizing their biases.  Munger:

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.  The opposite of what Darwin did is now called confirmation bias, a term of opprobrium.  Darwin’s practice came from his acute recognition of man’s natural cognitive faults arising from Inconsistency-Avoidance Tendency.  He provides a great example of psychological insight correctly used to advance some of the finest mental work ever done. 


(6)  Curiosity Tendency

There is a lot of innate curiosity in mammals, but its nonhuman version is highest among apes and monkeys.  Man’s curiosity, in turn, is much stronger than that of his simian relatives.  In advanced human civilization, culture greatly increases the effectiveness of curiosity in advancing knowledge…  Curiosity, enhanced by the best of modern education… much helps man to prevent or reduce bad consequences arising from other psychological tendencies.  The curious are also provided with much fun and wisdom long after formal education has ended.

Munger has long maintained that you should be a learning machine:

I constantly see people rise in life who are not the smartest, sometimes not even the most diligent, but they are learning machines.  They go to bed every night a little wiser than they were when they got up and boy does that help, particularly when you have a long run ahead of you.


(7)  Kantian Fairness Tendency

Kant’s ‘categorical imperative’ – a sort of ‘golden rule’ – “that required all humans to follow those behavior patterns that, if followed by all others, would make the surrounding human system work best for everybody.  And it is not too much to say that modern acculturated man displays, and expects from others, a lot of fairness as thus defined by Kant.”  (page 12)

Munger gives an example:

In a small community having a one-way bridge or tunnel for autos, it is the norm in the United States to see a lot of reciprocal courtesy, despite the absence of signs or signals.


(8)  Envy/Jealousy Tendency

Envy/jealousy is extreme in myth, religion, and literature wherein, in account after account, it triggers hatred and injury…

And envy/jealousy is also extreme in modern life… 

Munger has pointed out that envy is particularly stupid because there’s no upside.  Buffett has agreed with Munger on this, adding:

Gluttony is a lot of fun.  Lust has its place, too, but we won’t get into that.


It is not greed that drives the world, but envy.


(9)  Reciprocation Tendency


The automatic tendency of humans to reciprocate both favors and disfavors has long been noticed as it is in apes, monkeys, dogs, and many less cognitively gifted animals.  The tendency facilitates group cooperation for the benefit of members.

Unfortunately, hostility can get extreme.  But we have the ability to train ourselves.  Munger:

The standard antidote to one’s overactive hostility is to train oneself to defer reaction.  As my smart friend Tom Murphy so frequently says, ‘You can always tell the man off tomorrow, if it is such a good idea.’  (page 13)

Munger then notes that the tendency to reciprocate favor for favor is also very intense.  He mentions strange pauses in fighting during wars, caused by some minor courtesy or favor by one side which was then reciprocated by the other side.  Furthermore:

It is obvious that commercial trade, a fundamental cause of modern prosperity, is enormously facilitated by man’s innate tendency to reciprocate favors.  In trade, enlightened self-interest joining with Reciprocation Tendency results in constructive conduct.

Reciprocation Tendency operates largely subconsciously, like the other tendencies.

Munger mentions an experiment conducted by the psychology professor Robert Cialdini:

…Cialdini caused his ‘compliance practitioners’ to wander around his campus and ask strangers to supervise a bunch of juvenile delinquents on a trip to a zoo… one person in six out of a large sample actually agreed to do this… His practitioners next wandered around the campus asking strangers to devote a big chunk of time every week for two years to the supervision of juvenile delinquents.  This ridiculous request got him a one hundred percent rejection rate.  But the practitioner had a follow-up question:  ‘Will you at least spend one afternoon taking juvenile delinquents to a zoo?’  This raised Cialdini’s former acceptance rate of 1/6 to 1/2 – a tripling.

What Cialdini’s ‘compliance practitioners’ had done was make a small concession, which was reciprocated by a small concession from the other side.

Munger gives an important example from the real world:

The importance and power of reciprocate-favor tendency was also demonstrated in Cialdini’s explanation of the foolish decision of the attorney general of the United States to authorize the Watergate burglary.  There, an aggressive subordinate made some extreme proposal for advancing Republican interests… When this ridiculous request was rejected, the subordinate backed off, in gracious concession, to merely asking for consent to a burglary, and the attorney general went along.  Cialdini believes that subconscious Reciprocation Tendency thus became one important cause of the resignation of a United States president in the Watergate debacle, and so do I.  Reciprocation Tendency subtlely causes many extreme and dangerous consequences, not just on rare occasions but pretty much all the time.  (page 14)

But, while the Reciprocation Tendency is often dangerous, on the whole it causes more good than bad, says Munger:

Overall, both inside and outside religions, it seems clear to me that Reciprocation Tendency’s constructive contributions to man far outweigh its destructive effects.  In cases of psychological tendencies being used to counter or prevent bad results from one or more other psychological tendencies – for instance, in the case of interventions to end chemical dependency – you will usually find Reciprocation Tendency performing strongly on the constructive side.

And the very best part of human life probably lies in relationships of affection wherein parties are more interested in pleasing than being pleased – a not uncommon outcome in display of reciprocate-favor tendency.

Guilt is also rooted in evolution.  But Munger views it as a positive, on the whole:

…To the extent the feeling of guilt has an evolutionary base, I believe the most plausible cause is the mental conflict triggered in one direction by reciprocate-favor tendency and in the opposite direction by reward superresponse tendency pushing one to enjoy one hundred percent of some good thing.  Of course, human culture has often greatly boosted the genetic tendency to suffer from feeling of guilt.  Most especially, religious culture has imposed hard-to-follow ethical and devotional demands on people…  And if you, like me… believe that, averaged out, feelings of guilt do more good than harm, you may join in my special gratitude for reciprocate-favor tendency, no matter how unpleasant you find feelings of guilt.


(10)  Influence-from-Mere-Association Tendency

Munger observes that advertisers know the power of mere association.  For instance, Coca-Cola advertisements strive to associate Coke with happiness.

However, our minds can be misled by random association, as Munger explains:

Some of the most important miscalculations come from what is accidentally associated with one’s past success, or one’s liking and loving, or one’s disliking and hating, which includes a natural hatred for bad news.  (page 15)

Munger continues:

To avoid being misled by the mere association of some fact with past success, use this memory clue.  Think of Napoleon and Hitler when they invaded Russia after using their armies with much success elsewhere.  And there are plenty of mundane examples of results like those of Napoleon and Hitler.  For instance, a man foolishly gambles in a casino and yet wins.  This unlikely correlation causes him to try the casino again, or again and again, to his horrid detriment.  Or a man gets lucky in an odds-against venture headed by an untalented friend.  So influenced, he tries again what worked before – with terrible results.

Munger advises:

The proper antidotes to being made such a patsy by past success are (1) to carefully examine each past success, looking for accidental, non-causative factors associated with such success that will tend to mislead as one appraises odds implicit in a proposed new undertaking and (2) to look for dangerous aspects of the new undertaking that were not present when past success occurred.

Hating and disliking also cause miscalculation triggered by mere association.  In business, I commonly see people underappraise both the competency and the morals of competitors they dislike.  This is a dangerous practice, usually disguised because it occurs on a subconscious basis. 

Munger later comments on “Persian Messenger Syndrome”:

…Persian Messenger Syndrome is alive and well in modern life, albeit in less lethal versions.  It is actually dangerous in many careers to be a carrier of unwelcome news.  Union negotiators and employer representatives often know this, and it leads to many tragedies in labor relations.  Sometimes lawyers, knowing their clients will hate them if they recommend an unwelcome but wise settlement, will carry on to disaster…

CBS, in its late heyday, was famous for occurrence of Persian Messenger Syndrome because Chairman Paley was hostile to people who brought him bad news.  The result was that Paley lived in a cocoon of unreality, from which he made one bad deal after another, even exchanging a large share of CBS for a company that had to be liquidated shortly thereafter.


(11)  Simple, Pain-Avoiding Psychological Denial

Munger says:

This phenomenon first hit me hard in World War II when the superathlete, superstudent son of a family friend flew off over the Atlantic Ocean and never came back.  His mother, who was a very sane woman, then refused to believe he was dead.  That’s Simple, Pain-Avoiding Psychological Denial.  The reality is too painful to bear, so one distorts the facts until they become bearable.  We all do that to some extent, often causing terrible problems.  The tendency’s most extreme outcomes are usually mixed up with love, death, and chemical dependency.


(12)  Excessive Self-Regard Tendency

Excessive self-regard is one of the more obvious tendencies.

We all commonly observe the excessive self-regard of man.  He mostly misappraises himself on the high side, like the ninety percent of Swedish drivers that judge themselves to be above average.  Such misappraisals also apply to a person’s major ‘possessions.’  One spouse usually overappraises the other spouse.  And a man’s children are likewise appraised to be higher by him than they are likely to be in a more objective view.  Even man’s minor possessions tend to be overappraised.  Once owned, they suddenly become worth more to him than he would pay if they were offered for sale to him and he didn’t already own them.  There is a name in psychology for this overappraise-your-own-possessions phenomenon: the ‘endowment effect.’  And all man’s decisions are suddenly regarded by him as better than would have been the case just before he made them.

Man’s excess of self-regard typically makes him strongly prefer people like himself…  (page 16)

Munger continues:

Some of the worse consequences in modern life come when dysfunctional groups of cliquish persons, dominated by Excessive Self-Regard Tendency, select as new members of their organizations persons who are very much like themselves…

Well, naturally, all forms of excess of self-regard cause much error.  How could it be otherwise?

Moreover, says Munger:

Intensify man’s love of his own conclusions by adding the possessory wallop from the ‘endowment effect,’ and you will find that a man who has already bought a pork-belly future on a commodity exchange now foolishly believes, even more strongly than before, in the merits of his speculative bet.

And foolish sports betting, by people who love sports and think they know a lot about relative merits of teams, is a lot more addictive than race track betting – partly because of man’s automatic overappraisal of his own complicated conclusions.

Also extremely counterproductive is man’s tendency to be, time after time, in games of skill, like golf or poker, against people who are obviously much better players.  Excessive Self-Regard Tendency diminishes the foolish bettor’s accuracy in appraising his relative degree of talent.

Munger then adds:

More counterproductive yet are man’s appraisals, typically excessive, of the quality of the future service he is to provide to his business.  His overappraisal of these prospective contributions will frequently cause disaster.

There is a famous passage somewhere in Tolstoy that illuminates the power of Excessive Self-Regard Tendency.  According to Tolstoy, the worst criminals don’t appraise themselves as all that bad.  They come to believe either (1) that they didn’t commit their crimes or (2) that, considering the pressures and disadvantages of their lives, it is understandable and forgivable that they behaved as they did and become what they became.  (pg. 17)

Munger comments:

The second half of the ‘Tolstoy effect’, where the man makes excuses for his fixable poor performance, instead of providing the fix, is enormously important.  Because a majority of mankind will try to get along by making way too many unreasonable excuses for fixable poor performance, it is very important to have personal and institutional antidotes limiting the ravages of such folly.  On the personal level a man should try to face the two simple facts:

  • fixable but unfixed bad performance is bad character and tends to create more of itself, causing more damage to the excuse giver with each tolerated instance, and
  • in demanding places, like athletic teams and General Electric, you are almost sure to be discarded in due course if you keep giving excuses instead of behaving as you should.

The best antidote to folly from an excess of self-regard is to force yourself to be more objective when you are thinking about yourself, your family and friends, your property, and the value of your past and future activity.  This isn’t easy to do well and won’t work perfectly, but it will work much better than simply letting psychological nature take its normal course.

Most of the time, excessive self-regard harms our ability to make a good decision.  If you have an important decision, you have to learn to slow yourself down and be humble.  Munger:

You’re less pleasing than you think you are.  You know less than you think you do.

It’s easy for us to see the shortcomings in others, but it’s much harder for us to see our own flaws clearly.  It’s good to be able to laugh at yourself.


(13)  Overoptimism Tendency


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

Munger suggests:

One standard antidote to foolish optimism is trained, habitual use of the simple probability math of Fermat and Pascal, taught in my youth to high school sophomores.  The mental rules of thumb that evolution gives you are not adequate.  They resemble the dysfunctional golf grip you would have if you relied on a grip driven by evolution instead of golf lessons.  (page 18)


(14)  Deprival-Superreaction Tendency

Munger states:

The quantity of man’s pleasure from a ten dollar gain does not exactly match the quantity of his displeasure from a ten dollar loss.  That is, the loss seems to hurt much more than the gain seems to help.  Moreover, if a man almost gets something he greatly wants and has it jerked away from him at the last moment, he will react much as if he had long owned the reward and had it jerked away.  I include the natural human reactions to both kinds of loss experience – the loss of the possessed reward and the loss of the almost possessed reward – under one description, Deprival Superreaction Tendency.

In displaying Deprival Superreaction Tendency, man frequently incurs disadvantage by misframing his problems.  He will often compare what is near instead of what truly matters.  For instance, a man with $10 million in his brokerage account will often be extremely irritated by the loss of $100 out of the $300 in his wallet.

Munger observes:

…A man ordinarily reacts with irrational intensity to even a small loss, or threatened loss, of property, love, friendship, dominated territory, opportunity, status, or any other valued thing.  As a natural result, bureaucratic infighting over the threatened loss of dominated territory often causes immense damage to an institution as a whole.  This factor among others, accounts for much of the wisdom of Jack Welch’s long fight against bureaucratic ills at General Electric.  Few business leaders have ever conducted wiser campaigns.

Deprival-Superreaction Tendency often protects ideological or religious views by triggering dislike and hatred directed toward vocal nonbelievers.  This happens, in part, because the ideas of the nonbelievers, if they spread, will diminish the influence of views that are now supported by a comfortable environment including a strong belief-maintenance system.  University liberal arts departments, law schools, and business organizations all display plenty of such ideology-based groupthink that rejects almost all conflicting inputs…

It is almost everywhere the case that extremes of ideology are maintained with great intensity and with great antipathy to non-believers, causing extremes of cognitive dysfunction.  This happens, I believe, because two psychological tendencies are usually acting concurrently toward this same sad result: (1) Inconsistency-Avoidance Tendency, plus (2) Deprival-Superreaction Tendency.

One antidote to intense, deliberate maintenance of groupthink is an extreme culture of courtesy, kept in place despite ideological differences, like the behavior of the justices now serving on the U.S. Supreme Court.  Another antidote is to deliberately bring in able and articulate disbelievers of incumbent groupthink….

Even a one-degree loss from a 180-degree view will sometime create enough Deprival-Superreaction Tendency to turn a neighbor into an enemy, as I once observed when I bought a house from one of two neighbors locked into hatred by a tiny tree newly installed by one of them.

Moreoever, says Munger:

Deprival-Superreaction Tendency and Inconsistency-Avoidance Tendency often join to cause one form of business failure.  In this form of ruin, a man gradually uses up all his good assets in a fruitless attempt to rescue a big venture going bad.  One of the best antidotes to this folly is good poker skill learned young.  The teaching value of poker demonstrates that not all effective teaching occurs on a standard academic path.

Deprival-Superreaction Tendency is also a huge contributor to ruin from compulsion to gamble.  First, it causes the gambler to have a passion to get even once he has suffered loss, and the passion grows with each loss.  Second, the most addictive forms of gambling provide a lot of near misses and each one triggers Deprival-Superreaction Tendency.  Some slot machine creators are vicious in exploiting this weakness of man.  Electronic machines enable these creators to produce a lot of meaningless bar-bar-lemon results that greatly increase play by fools who think they have very nearly won large rewards.  (page 19)


(15)  Social-Proof Tendency

Munger notes:

The otherwise complex behavior of man is much simplified when he automatically thinks and does what he observes to be thought and done around him.  And such followership often works fine…

Psychology professors love Social-Proof Tendency because in their experiments it causes ridiculous results.  For instance, if a professor arranges for some stranger to enter an elevator wherein ten ‘compliance practitioners’ are all standing so that they face the rear of the elevator, the stranger will often turn around and do the same.

Of course, like the other tendencies, Social-Proof has an evolutionary basis.  If the crowd was running in one direction, typically your best response was to follow.

But, in today’s world, simply copying others often doesn’t make sense.  Munger:

And in the highest reaches of business, it is not at all uncommon to find leaders who display followership akin to that of teenagers.  If one oil company foolishly buys a mine, other oil companies often quickly join in buying mines.  So also if the purchased company makes fertilizer.  Both of these oil company buying fads actually bloomed, with bad results.

Of course, it is difficult to identify and correctly weigh all the possible ways to deploy the cash flow of an oil company.  So oil company executives, like everyone else, have made many bad decisions that were triggered by discomfort from doubt.  Going along with social proof provided by the action of other oil companies ends this discomfort in a natural way.  (page 20)

Munger remarks:

When will Social-Proof Tendency be most easily triggered?  Here the answer is clear from many experiments:  Triggering most readily occurs in the presence of puzzlement or stress, and particularly when both exist. 

Because stress intensifies Social-Proof Tendency, disreputable sales organizations, engaged, for instance, in such action as selling swampland to schoolteachers, manipulate targets into situations combining isolation and stress.  The isolation strengthens the social proof provided by both the knaves and the people who buy first, and the stress, often increased by fatigue, augments the targets’ susceptibility to the social proof.  And, of course, the techniques of our worst ‘religious’ cults imitate those of the knavish salesmen.  One cult even used rattlesnakes to heighten the stress felt by conversion targets.

Munger points out that Social-Proof can sometimes be constructive:

Because both bad and good behavior are made contagious by Social-Proof Tendency, it is highly important that human societies (1) stop any bad behavior before it spreads and (2) foster and display all good behavior.

Often people find it difficult to resist the social contagion of bad behavior.  Munger:

…And, therefore, we get “Serpico Syndrome,” named to commemorate the state of a near-totally corrupt New York police division joined by Frank Serpico.  He was then nearly murdered by gunfire because of his resistance to going along with the corruption in the division.  Such corruption was being driven by social proof plus incentives, the combination that creates Serpico Syndrome.  The Serpico story should be taught more than it now is because the didactic power of its horror is aimed at a very important evil, driven substantially by a very important force:  social proof.

Munger gives another example:

In social proof, it is not only action by others that misleads but also their inaction.  In the presence of doubt, inaction by others becomes social proof that inaction is the right course.  Thus, the inaction of a great many bystanders led to the death of Kitty Genovese in a famous incident much discussed in introductory psychology courses.

In the ambit of social proof, the outside directors on a corporate board usually display the near ultimate form of inaction.  They fail to object to anything much short of an axe murder until some public embarrassment of the board finally causes their intervention…

Typically there are many psychological tendencies operating at the same time – such as Liking/Loving, Disliking/Hating, Doubt-Avoidance, Inconsistency-Avoidance, and Social-Proof.  Unchecked, a confluence of such tendencies can lead to extreme situations.  Munger gives an example:

…By now the resources spent by Jews, Arabs, and all others over a small amount of disputed land if divided arbitrarily among land claimants, would have made every one better off, even before taking into account any benefit from reduced threat of war, possibly nuclear.  (pg. 21)


(16)  Contrast-Misreaction Tendency

Munger asserts:

Because the nervous system of man does not naturally measure in absolute scientific units, it must rely instead on something simpler.  The eyes have a solution that limits their programming needs: the contrast in what is seen is registered.  And as in sight, so does it go, largely, in the other senses.  Moreover, as perception goes, so goes cognition.  The result is man’s Contrast-Misreaction Tendency.  Few psychological tendencies do more damage to correct thinking.  Small-scale damages involve instances such as man’s buying an overpriced $1,000 leather dashboard merely because the price is so low compared to this concurrent purchase of a $65,000 car.  Large-scale damages often ruin lives, as when a wonderful woman having terrible parents marries a man who would be judged satisfactory only in comparison to her parents.  Or as when a man takes wife number two who would be appraised all right only in comparison to wife number one.

A particularly reprehensible form of sales practice occurs in the offices of some real estate brokers.  A buyer from out of the city, perhaps needing to shift his family there, visits the office with little time available.  The salesman deliberately shows the customer three awful houses at ridiculously high prices.  Then he shows him a merely bad house at a price only moderately too high.  And, boom, the broker often makes an easy sale.

Munger continues:

Contrast-Misreaction Tendency is routinely used to cause disadvantage for customers buying merchandise and services.  To make an ordinary price seem low, the vendor will very frequently create a highly artificial price that is much higher than the price always sought, then advertise his standard price as a big reduction from his phony price.  Even when people know that this sort of customer manipulation is being attempted, it will often work to trigger buying… [It demonstrates that] being aware of psychological ploys is not a perfect defense.  When a man’s steps are consecutively taken toward disaster, with each step being very small, the brain’s Contrast-Misreaction Tendency will often let the man go too far toward disaster to be able to avoid it.  This happens because each step presents so small a contrast from his present position.


(17)  Stress-Influence Tendency

Munger reflects:

Everyone recognizes that sudden stress, for instance from a threat, will cause a rush of adrenaline in the human body, prompting faster and more extreme reaction.  And everyone who has taken Psych 101 knows that stress makes Social-Proof Tendency more powerful.


(18)  Availability-Misweighing Tendency

Munger observes:

Man’s imperfect, limited-capacity brain easily drifts into working with what’s easily available to it.  And the brain can’t use what it can’t remember or what it is blocked from recognizing because it is heavily influenced by one or more psychological tendencies bearing strongly on it, as the fellow is influenced by the nearby girl in the song.  And so the mind overweighs what is easily available and thus displays Availability-Misweighing Tendency.

Munger mentions antidotes:

The main antidote to miscues from Availability-Misweighing Tendency often involve procedures, including use of checklists, which are almost always helpful. 

Another antidote is to behave somewhat like Darwin did when he emphasized disconfirming evidence.  What should be done is to especially emphasize factors that don’t produce reams of easily available numbers, instead of drifting mostly or entirely into considering factors that do produce such numbers.  Still another antidote is to find and hire some skeptical, articulate people with far-reaching minds to act as advocates for notions that are opposite to the incumbent notions.

If some event is vivid or recent, it will generally be more available.  Munger:

One consequence of this tendency is that extra-vivid evidence, being so memorable and thus more available in cognition, should often consciously be underweighed while less vivid evidence should be overweighed.

Munger offers a suggestion:

The great algorithm to remember in dealing with this tendency is simple:  An idea or a fact is not worth more merely because it is easily available to you.


(19)  Use-It-or-Lose-It Tendency

Munger discusses the importance of practice:

All skills attenuate with disuse… The right antidote to such a loss is to make use of the functional equivalent of the aircraft simulator employed in pilot training.  This allows a pilot to continuously practice all of the rarely used skills that he can’t afford to lose.

Throughout his life, a wise man engages in practice of all his useful, rarely used skills, many of them outside his discipline, as a sort of duty to his better self.  If he reduces the number of skills he practices and, therefore, the number of skills he retains, he will naturally drift into error from man with a hammer tendency.  His learning capacity will also shrink as he creates gaps in the latticework of theory he needs as a framework for understanding new experience.  It is also essential for a thinking man to assemble his skills into a checklist that he routinely uses.  Any other mode of operation will cause him to miss much that is important.  (page 23)

If the skill in question is important enough, gaining fluency is wise, says Munger:

The hard rule of Use-It-or-Lose-It Tendency tempers its harshness for the diligent.  If a skill is raised to fluency, rather than merely being crammed in briefly to enable one to pass some test, then the skill (1) will be lost more slowly and (2) will come back faster when refreshed with new learning.  These are not minor advantages, and a wise man engaged in learning some important skill will not stop until he is really fluent in it.


(20)  Drug-Misinfluence Tendency

“This tendency’s destructive power is so widely known to be intense, with frequent tragic consequences for cognition and the outcome of life, that it needs no discussion here to supplement that previously given under ‘Simple, Pain-Avoiding Psychological Denial’.”


(21)  Senescence-Misinfluence Tendency

All of us naturally decay over time.  Munger points out:

But some people remain pretty good in maintaining intensely practiced old skills until late in life, as one can notice in many a bridge tournament. 

Loving to learn can help:

Continuous thinking and learning, done with joy, can somewhat help delay what is inevitable.


(22)  Authority-Misinfluence Tendency

A disturbingly significant portion of copilots will not correct obvious errors made by the pilot during simulation exercises.  There are also real world examples of copilots crashing planes because they followed the pilot mindlessly.  Munger states:

…Such cases are also given attention in the simulator training of copilots who have to learn to ignore certain really foolish orders from boss pilots because boss pilots will sometimes err disastrously.  Even after going through such a training regime, however, copilots in simulator exercises will too often allow the simulated plane to crash because of some extreme and perfectly obvious simulated error of the chief pilot.

Psychologist Stanley Milgram wanted to understand why so many seemingly normal and decent people engaged in horrific, unspeakable acts during World War II.  Munger:

After Corporal Hitler had risen to dominate Germany, leading a bunch of believing Lutherans and Catholics into orgies of genocide and other mass destruction, one clever psychology professor, Stanley Milgram, decided to do an experiment to determine exactly how far authority figures could lead ordinary people into gross misbehavior.  In this experiment, a man posing as an authority figure, namely a professor governing a respectable experiment, was able to trick a great many ordinary people into giving what they had every reason to believe were massive electric shocks that inflicted heavy torture on innocent fellow citizens.  This experiment did demonstrate a terrible result contributed to by Authority-Misinfluence Tendency, but it also demonstrated extreme ignorance in the psychology professoriate right after World War II.

Almost any intelligent person with my checklist of psychological tendencies in his hand would, by simply going down the checklist, have seen that Milgram’s experiment involved about six powerful psychological tendencies acting in confluence to bring about his extreme experimental result.  For instance, the person pushing Milgram’s shock lever was given much social proof from presence of inactive bystanders whose silence communicated that his behavior was okay…


(23)  Twaddle Tendency

Munger mentions:

Man, as a social animal who has the gift of language, is born to prattle and to pour out twaddle that does much damage when serious work is being attempted.  Some people produce copious amounts of twaddle and others very little.  (page 24)


(24)  Reason-Respecting Tendency

People naturally love thinking, reasoning, and learning:

There is in man, particularly one in an advanced culture, a natural love of accurate cognition and a joy in its exercise.  This accounts for the widespread popularity of crossword puzzles, other puzzles, and bridge and chess columns, as well as all games requiring mental skill.

Always trying to understand WHY things happen is a central part of the learning process, says Munger:

In general, learning is most easily assimilated and used when, life long, people consistently hang their experience, actual and vicarious, on a latticework of theory answering the question: Why?  Indeed, the question ‘Why?’ is a sort of Rosetta stone opening up the major potentiality of mental life.

But often we don’t notice when meaningless or incorrect reasons are given:

Unfortunately, Reason-Respecting Tendency is so strong that even a person’s giving of meaningless or incorrect reasons will increase compliance with his orders and requests.  This has been demonstrated in psychology experiments wherein ‘compliance practitioners’ successfully jump to the head of the lines in front of copying machines by explaining their reason: ‘I have to make some copies.’  This sort of unfortunate byproduct of Reason-Respecting Tendency is a conditioned reflex, based on a widespread appreciation of the importance of reasons.  And, naturally, the practice of laying out various claptrap reasons is much used by commercial and cult ‘compliance practitioners’ to help them get what they don’t deserve.


Can you supply a real world model, instead of a Milgram-type controlled psychology experiment, that uses your system to illustrate multiple psychological tendencies interacting in a plausibly diagnosable way?

The answer is yes.  One of my favorite cases involves the McDonnell Douglas airliner evacuation test.  Before a new airliner can be sold, the government requires that it pass an evacuation test, during which a full load of passengers must get out in some short period of time.  The government directs that the test be realistic.  So you can’t pass by evacuating only twenty-year-old athletes.  So McDonnell Douglas scheduled such a test in a darkened hangar using a lot of old people as evacuees.  The passenger cabin was, say, twenty feet above the concrete floor of the hangar and was to be evacuated through moderately flimsy rubber chutes.  The first test was made in the morning.  There were about twenty very serious injuries, and the evacuation took so long it flunked the time test.  So what did McDonnell Douglas next do?  It repeated the test in the afternoon, and this time there was another failure, with about twenty more serious injuries, including one case of permanent paralysis.

What psychological tendencies contributed to this terrible result?  Well, using my tendency list as a checklist, I come up with the following explanation.  Reward-Superresponse Tendency drove McDonnell Douglas to act fast.  It couldn’t sell its airliner until it passed the test.  Also pushing the company was Doubt-Avoidance Tendency with its natural drive to arrive at a decision and run with it.  Then the government’s direction that the test be realistic drove Authority-Misinfluence Tendency into the mischief of causing McDonnell Douglas to overreact by using what was obviously too dangerous a test method.  By now the course of action had been decided, so Inconsistency Avoidance Tendency helped preserve the near idiotic plan.  When all the old people got to the dark hangar, with its high airline cabin and concrete floor, the situation must have made McDonnell Douglas employees very queasy, but they saw other employees and supervisors not objecting.  Social Proof Tendency, therefore, swamped the queasiness.  And this allowed continued action as planned, a continuation that was aided by more Authority-Misinfluence Tendency.  Then came the disaster of the morning test with its failure, plus serious injuries.  McDonnell Douglas ignored the strong disconfirming evidence from the failure of the first test because confirmation bias, aided by the triggering of strong Deprival Superreaction Tendency favored maintaining the original plan.  McDonnell Douglas’ Deprival Superreaction Tendency was now like that which causes a gambler, bent on getting even after a huge loss, to make his final big bet.  After all, McDonnell Douglas was going to lose a lot if it didn’t pass its test as scheduled.  More psychology-based explanation can probably be made, but the foregoing discussion is complete enough to demonstrate the utility of my system when used in checklist mode.  (page 26)


In the practical world, what good is the thought system laid out in this list of tendencies?  Isn’t practical benefit prevented because these psychological tendencies are so thoroughly programmed into the human mind by broad evolution [the combination of genetic and cultural evolution] that we can’t get rid of them?

Well, the answer is that the tendencies are probably more good than bad.  Otherwise, they wouldn’t be there, working pretty well for man, given his condition and his limited brain capacity.  So the tendencies can’t be simply washed out automatically, and they shouldn’t be.  Nevertheless, the psychological thought system described, when properly understood and used, enables the spread of wisdom and good conduct and facilitates the avoidance of disaster.  Tendency is not always destiny, and knowing the tendencies and their antidotes can often help prevent trouble that would otherwise occur.


Here is a short list of examples reminding us of the great utility of elementary psychological knowledge.

  • Carl Braun’s communication practices.
  • The use of simulators in pilot training.
  • The system of Alcoholics Anonymous.
  • Clinical training methods in medical schools.
  • The rules of the U.S. Constitutional Convention:  totally secret meetings, no recorded vote by name until the final vote, votes reversible at any time before the end of the convention, then just one vote on the whole Constitution.  These are very clever psychology-respecting rules.  If the founders had used a different procedure, many people would have been pushed by various psychological tendencies into inconsistent, hardened positions.  The elite founders got our Constitution through by a whisker only because they were psychologically acute.
  • The use of Granny’s incentive-driven rule to manipulate oneself toward better performance of one’s duties.
  • The Harvard Business School’s emphasis on decision trees.  When I was young and foolish I used to laugh at the Harvard Business School.  I said, ‘They’re teaching twenty-eight year-old people that high school algebra works in real life?’  But later, I wised up and realized that it was very important that they do that to counter some bad effects from psychological tendencies.  Better late than never.
  • The use of autopsy equivalents at Johnson & Johnson.  At most corporations, if you make an acquisition and it turns out to be a disaster, all the people, paperwork, and presentations that caused the foolish acquisition are quickly forgotten.  Nobody wants to be associated with the poor outcome by mentioning it.  But at Johnson & Johnson, the rules make everybody revisit old acquisitions, comparing predictions with outcomes.  That is a very smart thing to do.
  • The great example of Charles Darwin as he avoided confirmation bias, which has morphed into the extreme anti-confirmation-bias method of the “double blind” studies wisely required in drug research by the FDA.
  • The Warren Buffett rule for open-outcry auctions:  Don’t go.


Aren’t there factual and reasoning errors in this talk?

The answer is yes, almost surely yes.  The final revision was made from memory over about fifty hours by a man eighty-one years old, who never took a course in psychology and has read none of it, except one book on developmental psychology, for nearly fifteen years.  Even so.  I think the totality of my talk will stand up very well, and I hope all my descendants and friends will carefully consider what I have said.  I even hope that more psychology professors will join me in:

  • making heavy use of inversion;
  • driving for a complete description of the psychological system so that it works better as a checklist;  and
  • especially emphasizing effects from combinations of psychological tendencies.



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:  http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com




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.

The Essays of Warren Buffett

(Image:  Zen Buddha Silence by Marilyn Barbone.)

August 6, 2017

A chief purpose of this blog is to teach others about business and investing.  (My other passion is artificial intelligence.)  For those curious about these and related subjects, I hope this blog is useful.

The other main purpose of this blog is to create awareness for the Boole Microcap Fund, which I manage.

  • Buffett correctly observes that a low-cost index fund is the best long-term investment for most investors: http://boolefund.com/warren-buffett-jack-bogle/
  • A quantitative value strategy – properly implemented – has high odds of beating an index fund.
  • Buffett, Munger, Lynch, and other top investors started in micro caps because there’s far less competition and far more inefficiency.  An equal weighted microcap approach has outperformed every other size category historically: http://boolefund.com/best-performers-microcap-stocks/
  • If you also screen for value and for improving fundamentals, then a microcap value approach is likely to do significantly better (net of all costs) than an S&P 500 index fund over time.


This week’s blog post covers The Essays of Warren Buffett: Lessons for Corporate America (4th edition, 2015), selected and arranged by Lawrence 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;  but Buffett 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



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. 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…



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.  (page 37)

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.’  (page 39)

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.  (page 41)

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.  (pages 41-42)

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…  (page 44)

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.  (pages 50-51)

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.  (page 51)

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.  (pages 55-56)

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…  (page 67)

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?  (page 79)

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



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.’  (page 89)

Buffett advises most investors to invest in index funds:  http://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?  (page 102)

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: http://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.  (page 106)

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 quotes The 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.  (pages 107-108)

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 of successfully 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.  (page 111)

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.  (page 112)

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.  (page 113)


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 of circle 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.  (page 115)

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 problems must 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.  (pages 117-120)



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.  (pages 123-124)

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.  (pages 125-127)


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.’  (page 130)



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 dimishes, equalling 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.  (pages 169-172)

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



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.  (page 199)

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 accomodate 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.  (page 201)

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.  (pages 209-210)

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 think 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.  (pages 221-222)



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.  (pages 223-224)

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.

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.  (page 226)

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.  (page 237)

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.  (page 239)

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 (see page 247).

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 term owner earnings instead of free cash flow.  Buffett on owner 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.  So owner 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.  (page 254)



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?  (page 259)

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 case, 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.  (pages 272-273)


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.  (page 276)



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.  (pages 289-291)

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.  (page 292)

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



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: http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com


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.

The Second Machine Age

(Image:  Zen Buddha Silence by Marilyn Barbone.)

July 30, 2017

Erik Brynjolfsson and Andrew McAfee are the authors of The 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 Race With Machines:  Recommendations for Individuals
  • Policy Recommendations
  • Long-term Recommendations
  • Technology and the Future



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)



Arthur C. Clarke:

Any sufficiently advanced technology is indistinguishable from magic.

Computers are symbol processors, note Brynjolfsson 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 at Jeopardy!  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 winning Jeopardy!  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, observe Brynjolfsson 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 roughly says that computing power per dollar doubles about every eighteen months.  It’s not a law of nature, note Brynjolfsson 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)


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)



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 a yotta, 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 a yotta.

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.



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 called general 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.



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)



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)



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 as consumer 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)



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 that skill-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.



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)



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)



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.




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, in Drive, 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.



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 book Final 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)



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: http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com


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.

The Most Important Thing Illuminated

(Image:  Zen Buddha Silence by Marilyn Barbone.)

July 16, 2017

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



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.



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: http://boolefund.com/best-performers-microcap-stocks/

Value investing – properly applied – is a way to invest systematically in underpriced stocks.  For details, see: http://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.



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 term moat.  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: http://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.



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)



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 defines risk 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: http://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” (see Poor Charlie’s Alamanack: The Wit and Wisdom of Charles T.  Munger, expanded 3rd edition):

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 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)



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, in The 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: http://boolefund.com/university-berkshire-hathaway/



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)


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



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)



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 rewards always require higher risk.



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.



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



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 the nonsense 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 the confident “I know” folks with the guarded “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 the Wall Street Journal runs 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 future IS largely 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)



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.



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: http://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.



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)



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: http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com


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.

Notes on Value Investing

(Image:  Zen Buddha Silence by Marilyn Barbone.)

July 9, 2017

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



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: http://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: http://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: http://boolefund.com/cognitive-biases/



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.

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.



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.



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: http://boolefund.com/joseph-piotroski-value-investing/



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….



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: http://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 15-25 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 10-15% (at cost), while the average position is 5-7% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees.  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: jb@boolefund.com


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.