One Up On Wall Street

(Image:  Zen Buddha Silence by Marilyn Barbone.)

May 21, 2017

Peter Lynch is one of the great investors.  When Lynch managed Fidelity Magellan from 1977 to 1990, the fund averaged 29.2% per year – more than doubling the S&P 500 Index – making it the best performing mutual fund in the world.

In One Up On Wall Street: How to Use What You Already Know to Make Money in the Market (Fireside, 1989 and 2000), Lynch offers his best advice to individual investors.

Lynch explains how to find tenbaggers – stocks that increase by 10x – by looking among stocks that are too small for most professionals.  Lynch also suggests that you pay attention to small businesses you may come across in your daily life.  If you notice a company that seems to be doing well, then you should research its earnings prospects, financial condition, competitive position, and so forth, in order to determine if the stock is a bargain.

Moreover, Lynch notes that his biggest winners – tenbaggers, twentybaggers, and even a few hundredbaggers – typically have taken at least three to ten years to play out.

The main idea is that a stock tracks the earnings of the underlying company over time.  If you can pay a cheap price relative to earnings, and if those earnings increase over subsequent years, then you can get some fivebaggers, tenbaggers, and even better as long as you hold the stock while the story is playing out.

Lynch also writes that being right six out of ten times on average works well over time.  A few big winners will overwhelm the losses from stocks that don’t work out.

Don’t try to time the market.  Focus on finding cheap stocks.  Some cheap stocks do well even when the market is flat or down.  But over the course of many years, the economy grows and the market goes higher.  If you try to dance in and out of stocks, eventually you’ll miss big chunks of the upside.

Here are the sections in this blog post:

  • Introduction: The Advantages of Dumb Money
  • The Making of a Stockpicker
  • The Wall Street Oxymorons
  • Is This Gambling, or What?
  • Personal Qualities It Takes to Succeed
  • Is This a Good Market? Please Don’t Ask
  • Stalking the Tenbagger
  • I’ve Got It, I’ve Got It – What Is It?
  • The Perfect Stock, What a Deal!
  • Stocks I’d Avoid
  • Earnings, Earnings, Earnings
  • The Two-Minute Drill
  • Getting the Facts
  • Some Famous Numbers
  • Rechecking the Story
  • The Final Checklist
  • Designing a Portfolio
  • The Best Time to Buy and Sell


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Lynch writes that two decades as a professional investor have convinced him that the ordinary individual investor can do just as well as – if not better than – than the average Wall Street expert.

Dumb money is only dumb when it listens to the smart money.  (page 31)

Lynch makes it clear that if you’ve already invested in a mutual fund with good long-term performance, then sticking with it makes sense.  His point is that if you’ve decided to invest in stocks directly, then it’s possible to do as well as – if not better than – the average professional investor.  This means ignoring hot tips and doing your own research on companies.

There are at least three good reasons to ignore what Peter Lynch is buying:  (1) he might be wrong!  (A long list of losers from my own portfolio constantly reminds me that the so-called smart money is exceedingly dumb about 40 percent of the time);  (2) even if he’s right, you’ll never know when he’s changed his mind about a stock and sold;  and (3) you’ve got better sources, and they’re all around you…

If you stay half-alert, you can pick the spectacular performers right from your place of business or out of the neighborhood shopping mall, and long before Wall Street discovers them… and if you work in the industry, so much the better.  This is where you’ll find the tenbaggers.  I’ve seen it happen again and again from my perch at Fidelity.  (page 32)

You can find tenbaggers even in weak markets, writes Lynch.  If you have ten positions at 10% each, then one tenbagger will cause your total portfolio to increase 90% if the other nine positions are flat.  If the other nine positions collectively lose 50% (perhaps in weak market), then your overall portfolio will still be up 45% if you have one tenbagger.

Most often, tenbaggers are in companies like Dunkin’ Donuts rather than in penny stocks that depend on a scientific breakthrough.  Lynch also mentions La Quinta Motor Inns – a tenbagger from 1973 to 1983.  (La Quinta modeled itself on Holiday Inn, but with 30% lower costs and 30% lower prices.  If you’ve attended a Berkshire Hathaway Annual Shareholders meeting in the past decade, you may have stayed at La Quinta along with a bunch of other frugal Berkshire shareholders.)

  • Note: When considering examples Lynch gives in his book, keep in mind that he first wrote the book in 1989.  The examples are from that time period.

Lynch says he came across many of his best investment ideas while he was out and about:

Taco Bell, I was impressed with the burrito on a trip to California;  La Quinta Motor Inns, somebody at the rival Holiday Inn told me about it;  Volvo, my family and friends drive this car;  Apple Computer, my kids had one at home and then the systems manager bought several for the office;  Service Corporation International, a Fidelity electronics analyst… found on a trip to Texas;  Dunkin’ Donuts, I loved the coffee…  (page 36)

Many individual investors think the big winners must be technology companies, but very often that’s not the case:

Among amateur investors, for some reason it’s not considered sophisticated practice to equate driving around town eating donuts with the initial phase of an investigation into equities.  People seem more comfortable investing in something about which they are entirely ignorant.  There seems to be an unwritten rule on Wall Street:  If you don’t understand it, then put your life savings into it.  Shun the enterprise around the corner, which can at least be observed, and seek out the one that manufactures an incomprehensible product.  (page 41)

Lynch is quick to point out that finding a promising company is only the first step.  You then must conduct the research.



Lynch describes his experience at Boston College:

As I look back on it now, it’s obvious that studying history and philosophy was much better preparation for the stock market than, say, studying statistics.  Investing in stocks is an art, not a science, and people who’ve been trained to rigidly quantify everything have a big disadvantage.  (page 49)

Granted, since Lynch first wrote this book in 1989, more quantitative investors – using computer-based models – have come along.  But when it comes to picking stocks that can do well if held for many years, the majority of successful investors still follow a much more traditional process:  gathering information through observation, massive reading, and scuttlebutt, and then making investment decisions that are often partly qualitative in nature.  Here is a partial list of successful stockpickers:

  • Bill Ackman, David Abrams, Lee Ainslie, Chuck Akre, Bruce Berkowitz, Christopher Browne, Warren Buffett, Michael Burry, Leon Cooperman, Christopher Davis, David Einhorn, Jean-Marie Eveillard, Thomas Gayner, Glenn Greenberg, Joel Greenblatt, Mason Hawkins, Carl Icahn, Seth Klarman, Stephen Mandel, Charlie Munger, Bill Nygren, Mohnish Pabrai, Michael Price, Richard Pzena, Robert Rodriguez, Stephen Romick, Thomas Russo, Walter Schloss, Lou Simpson, Guy Spier, Arnold Van Den Berg, Prem Watsa, Wallace Weitz, and Donald Yacktman.



Lynch observes that professional investors are typically not able to invest in most stocks that become tenbaggers.  Perhaps the most important reason is that most professional investors never invest in micro-cap stocks, stocks with market caps up to $500 (or $800) million.  Assets under management (AUM) for many professional investors are just too large to be able to invest in micro-cap companies.  Moreover, even smaller funds usually focus on small caps instead of micro caps.  Often that’s a function of AUM, but sometimes it’s a function of micro-cap companies being viewed as inherently riskier.

There are thousands of micro-cap companies.  And many micro caps are good businesses with solid revenues and earnings, and with healthy balance sheets.  These are the companies you should focus on as an investor.  (There are some micro-cap companies without good earnings or without healthy balance sheets.  Simply avoid these.)

If you invest in a portfolio of solid micro-cap companies, and hold for at least 10 years, then the expected returns are far higher than would you get from a portfolio of larger companies.  There is more volatility along the way, but if you can just focus on the long-term business results, then shorter term volatility is generally irrelevant.  Real risk for value investors is not volatility, or a stock that goes down temporarily.  Real risk is the chance of suffering a permanent loss – when the whole portfolio declines and is unable to bounce back.

Fear of volatility causes many professional investors only look at stocks that have already risen a great deal, so that they’re no longer micro caps.  You’ll miss a lot of tenbaggers and twentybaggers if you can’t even look at micro caps.  That’s a major reason why individual investors have an advantage over professional investors.  Individual investors can look at thousands of micro-cap companies, many of which are in very good shape.

Furthermore, even if a professional investor could look at micro-cap stocks, there are still strong incentives not to do so.  Lynch explains:

…between the chance of making an unusually large profit on an unknown company and the assurance of losing only a small amount on an established company, the normal mutual-fund manager, pension-fund manager, or corporate-portfolio manager would jump at the latter.  Success is one thing, but it’s more important not to look bad if you fail.  (page 59)

As Lynch says, if you’re a professional investor and you invest in a blue chip like General Electric which doesn’t work, clients and bosses will ask, ‘What is wrong with GE?’  But if you invest in an unknown micro-cap company and it doesn’t work, they’ll ask, ‘What is wrong with you?’

Lynch sums it up the advantages of the individual investor:

You don’t have to spend a quarter of your waking hours explaining to a colleague why you are buying what you are buying.  [Also, you can invest in unknown micro-cap companies…]  There’s nobody to chide you for buying back a stock at $19 that you earlier sold at $11 – which may be a perfectly sensible move.  (page 66)



In general, stocks have done better than bonds over long periods of time:

Historically, investing in stocks is undeniably more profitable than investing in debt.  In fact, since 1927, common stocks have recorded gains of 9.8 percent a year on average, as compared to 5 percent for corporate bonds, 4.4 percent for government bonds, and 3.4 percent for Treasury bills.

The long-term inflation rate, as measured by the Consumer Price Index, is 3 percent a year, which gives common stocks a real return of 6.8 percent a year.  The real return on Treasury bills, known as the most conservative and sensible of all places to put money, has been nil.  That’s right.  Zippo.  (page 70)

Many people get scared out of stocks whenever there is a large drop of 20-40% (or more).  But both the U.S. economy and U.S. stocks are very resilient and have always bounced back quickly.  This is especially true since the Great Depression, which was a prolonged period of deep economic stagnation caused in large part by policy errors.  A large fiscal stimulus and/or a huge program of money-printing (monetary stimulus) would have significantly shortened the Great Depression.

Lynch defines good investing as a system of making bets when the odds are in your favor.  If you’re right 60% of the time and you have a good system, then you should do fine over time.  Lynch compares investing to stud poker:

You can never be certain what will happen, but each new occurrence – a jump in earnings, the sale of an unprofitable subsidiary, the expansion into new markets – is like turning up another card.  As long as the cards suggest favorable odds of success, you stay in the hand. 

…Consistent winners raise their bets as their position strengthens, and they exit the game when the odds are against them… 

Consistent winners also resign themselves to the fact that they’ll occasionally be dealt three aces and bet the limit, only to lose to a hidden royal flush.  They accept their fate and go on to the next hand, confident that their basic method will reward them over time.  People who succeed in the stock market also accept periodic losses, setbacks, and unexpected occurrences.  Calamitous drops do not scare them out of the game… They realize the stock market is not pure science, and not like chess, where the superior position always wins.  If seven out of ten of my stocks perform as expected, then I’m delighted.  If six out of ten of my stocks perform as expected, then I’m thankful.  Six out of ten is all it takes to produce on enviable record on Wall Street.  (pages 74-75)

Lynch concludes the chapter by saying that investing is more like 70-card poker than 7-card poker.  If you have ten positions, it’s like playing ten 70-card hands at once.



Lynch writes:

It seems to me the list of qualities ought to include patience, self-reliance, common sense, a tolerance for pain, open-mindedness, detachment, persistence, humility, flexibility, a willingness to do independent research, an equal willingness to admit mistakes, and the ability to ignore general panic…

It’s also important to be able to make decisions without complete or perfect information.  Things are almost never clear on Wall Street, or when they are, then it’s too late to profit from them.  The scientific mind that needs to know all the data will be thwarted here.

And finally, it’s crucial to be able to resist your human nature and your ‘gut feelings.’  It’s the rare investor who doesn’t secretly harbor the conviction that he or she has a knack for divining stock prices or gold prices or interest rates, in spite of the fact that most of us have been proven wrong again and again.  It’s uncanny how often people feel most strongly that stocks are going to go up or the economy is going to improve just when the opposite occurs.  This is borne out by the popular investment-advisory newsletter services, which themselves tend to turn bullish and bearish at inopportune moments. 

According to information published by Investor’s Intelligence, which tracks investor sentiment via the newsletters, at the end of 1972, when stocks were about to tumble, optimism was at an all-time high, with only 15 percent of the advisors bearish.  At the beginning of the stock market rebound in 1974, investor sentiment was at an all-time low, with 65 percent of the advisors fearing the worst was yet to come…. At the start of the 1982 sendoff into a great bull market, 55 percent of the advisors were bears, and just prior to the big gulp of October 19, 1987, 80 percent of the advisors were bulls again.

…Does the success of Ravi Batra’s book The Great Depression of 1990 almost guarantee a great national prosperity?  (pages 80-81)

Lynch summarizes:

…The trick is not to learn to trust your gut feelings, but rather to discipline yourself to ignore them.  Stand by your stocks as long as the fundamental story of the company hasn’t changed.  (page 83, my emphasis)




There’s another theory that we have recessions every five years, but it hasn’t happened that way so far… Of course, I’d love to be warned before we do go into a recession, so I could adjust my portfolio.  But the odds of my figuring it out are nil.  Some people wait for these bells to go off, to signal the end of a recession or the beginning of an exciting new bull market.  The trouble is the bells never go off.  Remember, things are never clear until it’s too late. 

… No matter how we arrive at the latest financial conclusion, we always seem to be preparing ourselves for the last thing that’s happened, as opposed to what’s going to happen next.  This penultimate preparedness is our way of making up for the fact that we didn’t see the last thing coming along in the first place.  (page 86)

Lynch continues:

I don’t believe in predicting markets.  I believe in buying great companies – especially companies that are undervalued and/or underappreciated.  (page 88)

Lynch explains that the stock market itself is irrelevant.  What matters is the current and future earnings of the individual business you’re considering.  Lynch:

Several of my favorite tenbaggers made their biggest moves during bad markets.  Taco Bell soared through the last two recessions.  (page 89)

Focus on specific companies rather than the market as a whole.  Because you’re not restricted as an individual investor – you can look at micro-cap companies and you can look internationally – there are virtually always bargains somewhere.

A few good quotes from Buffett on forecasting:

Market forecasters will fill your ear but never fill your wallet.

We will continue to ignore political and economic forecasts, which are an expensive distraction for many investors and businessmen.

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.



If you work in a particular industry, this can give you an edge:

If you work in the chemical industry, then you’ll be among the first to realize that demand…is going up, prices are going up, and excess inventories are going down.  You’ll be in a position to know that no new competitors have entered the market and no new plants are under construction, and that it takes two to three years to build one.  (page 100)

Lynch also argues that if you’re a consumer of particular products, then you may be able to gain insight into companies that sell those products.  Again, you still need to study the financial statements in order to understand earnings and the balance sheet.  But noticing products that are doing well is a good start.



Most of the biggest moves – from tenbaggers to hundredbaggers – occur in smaller companies.  Micro-cap companies are the best place to look for these multi-baggers, while small-cap companies are the second best place to look.

Lynch identifies six general categories:  slow growers, stalwarts, fast growers, cyclicals, asset plays, and turnarounds.  (Slow growers and stalwarts tend to be larger companies.)

Slow Growers

Many large companies grow slowly, but are relatively dependable and may pay dividends.  A conservative investor may consider this category.


These companies tend to grow annual earnings at 10 to 12 percent a year.  If you buy the stock when it’s a bargain, you can make 30 to 50 percent, then sell and repeat the process.  This is Lynch’s approach to Stalwarts.  Also, Stalwarts can offer decent protection during weak markets.

Fast Growers

Some small, aggressive new companies can grow at 20 to 25 percent a year:

If you choose wisely, this is the land of the 10- to 40-baggers, and even the 200-baggers.  (page 118)

Of course, you have to be careful to identify the risks.  Many younger companies may grow too quickly or be underfinanced.  So look for the fast growers with good balance sheets and good underlying profitability.


A cyclical is a company whose sales and profits rise and fall regularly.  If you can buy when the company and the industry are out of favor – near the bottom of the cycle – then you can do well with a cyclical.  (You also need to sell when the company and the industry are doing well again unless you think it’s a good enough company to hold for a decade or longer.)

Some cyclical stocks – like oil stocks – tend to have a low level of correlation with the broader stock market.  This can help a portfolio, especially when the broader stock market offers few bargains.



Turnaround stocks make up lost ground very quickly… The best thing about investing in successful turnarounds is that of all the categories of stocks, their ups and downs are least related to the general market.  (page 122)

Again, a low correlation with the broader market is especially an advantage when the broader market is overvalued.  You do have to be careful with turnarounds, though.  As Buffett has said, ‘Turnarounds seldom turn.’

Asset Plays

A company may have something valuable that the market has overlooked.  It may be as simple as a pile of cash.  Sometimes it’s real estate.  It may be unrecognized intellectual property.  Or it could be resources in the ground.



Similar to Warren Buffett, Lynch prefers simple businesses:

Getting the story on a company is a lot easier if you understand the basic business.  That’s why I’d rather invest in panty hose than in communications satellites, or in motel chains than in fiber optics.  The simpler it is, the better I like it.  When somebody says, ‘Any idiot could run this joint,’ that’s a plus as far as I’m concerned, because sooner or later any idiot probably is going to be running it.

… For one thing, it’s easier to follow.  During a lifetime of eating donuts or buying tires, I’ve developed a feel for the product line that I’ll never have with laser beams or microprocessors.  (page 130)

Lynch then names thirteen attributes that are important to look for:

It Sounds Dull – Or, Even Better, Ridiculous

Lynch says a boring name is a plus, because that helps a company to stay neglected and overlooked, often causing the stock to be cheap.  Bob Evans Farms is an example of a perfect name.

It Does Something Dull

Crown, Cork, and Seal makes cans and bottle caps, says Lynch.  The more boring the business, the better.  Again, this will help keep the stock neglected, often causing the stock to be cheap.

It Does Something Disagreeable

Safety-Kleen provides gas stations with a machine that washes greasy auto parts.  Gas stations love it.  But it’s a bit disgusting, which can cause the stock to be neglected and thus possibly cheap.

It’s a Spinoff

Large companies tend to make sure that divisions they spin off are in good shape.  Generally spinoffs are neglected by most professional investors, often because the market cap of the spinoff is too small for them to consider.

The Institutions Don’t Own It, and the Analysts Don’t Follow It

With no institutional ownership, the stock may be neglected and possibly cheap.  With no analyst coverage, the chance of neglect and cheapness is even higher.  It’s not only micro-cap companies that are neglected.  When popular stocks fall deeply out of favor, they, too, can become neglected.

Rumors Abound: It’s Involved with Toxic Waste

Waste Management, Inc. was a hundredbagger.  People are so disturbed by sewage and waste that they tend to neglect such stocks.

There’s Something Depressing About It

Funeral home companies tend to be neglected and the stocks can get very cheap at times.

It’s a No-Growth Industry

In a no-growth industry, there’s much less competition.  This gives companies in the industry room to grow.

It’s Got a Niche

The local rock pit has a niche.  Lynch writes that if you have the only rock pit in Brooklyn, you have a virtual monopoly.  A rival two towns away is not going to transport rocks into your territory because the mixed rocks only sell for $3 a ton.

People Have to Keep Buying It

Drugs, soft drinks, razor blades, etc., are products that people need to keep buying.

It’s a User of Technology

A company may be in a position to benefit from ongoing technological improvements.

The Insiders Are Buyers

Insider buying usually means the insiders think the stock is cheap.

Also, you want insiders to own as much stock as possible so that they are incentivized to maximize shareholder value over time.  If executive salaries are large compared to stock ownership, then executives will focus on growth instead of on maximizing shareholder value (i.e., profitability).

The Company Is Buying Back Shares

If the shares are cheap, then the company can create much value through buybacks.



Lynch advises avoiding the hottest stock in the hottest industry.  Usually a stock like that will be trading at an extremely high valuation, which requires a great deal of future growth for the investor just to break even.  When future growth is not able to meet lofty expectations, typically the stock will plummet.

Similarly, avoid the next something.  If a stock is touted as the next IBM, the next Intel, or the next Disney, avoid it because very probably it will not be the next thing.

Avoid diworseifications:  Avoid the stock of companies that are making foolish acquisitions of businesses in totally different industries.  Such diworseifications rarely work out for shareholders.  Two-thirds of all acquisitions do not create value.  This is even more true when acquisitions are diworseifications.

Beware whisper stocks, which are often technological long shots or whiz-bang stories such as miracle drugs.  Lynch notes that he’s lost money on every single whisper stock he’s ever bought.

One trick to avoiding whisper stocks is to wait until they have earnings.  You can still get plenty of tenbaggers from companies that have already proven themselves.  Buying long shots before they have earnings rarely works.  This is a good rule for buying micro-cap stocks in general:  Wait until the company has solid earnings and a healthy balance sheet.



Lynch writes that a stock eventually will track the earnings of the business:

Analyzing a company’s stock on the basis of earnings and assets is no different from analyzing a local Laundromat, drugstore, or apartment building that you might want to buy.  Although it’s easy to forget sometimes, a share of stock is not a lottery ticket.  It’s part ownership of a business.  (pages 161-162)

Lynch gives an example:

And how about Masco Corporation, which developed the single-handle ball faucet, and as a result enjoyed thirty consecutive years of up earnings through war and peace, inflation and recession, with the earnings rising 800-fold and the stock rising 1,300-fold between 1958 and 1987?  What would you expect from a company that started out with the wonderfully ridiculous name of Masco Screw Products?  (page 164)

Lynch advises not to invest in companies with high price-to-earnings (p/e) ratios:

If you remember nothing else about p/e ratios, remember to avoid stocks with excessively high ones.  You’ll save yourself a lot of grief and a lot of money if you do.  With few exceptions, an extremely high p/e ratio is a handicap to a stock, in the same way that extra weight in the saddle is a handicap to a racehorse.  (page 170)

Future earnings may not be predictable, but you can at least check how the company plans to increase future earnings.  Lynch:

There are five basic ways a company can increase earnings:  reduce costs;  raise prices;  expand into new markets;  sell more of its product in the old markets;  or revitalize, close, or otherwise dispose of a losing operation.  These are the factors to investigate as you develop the story.  If you have an edge, this is where it’s going to be most helpful.  (page 173)



Warren Buffett has said that he would have been a journalist if he were not an investor.  Buffett says his job as an investor is to write the story for the company in question.  Lynch has a similar view.  He explains:

Before buying a stock, I like to be able to give a two-minute monologue that covers the reasons I’m interested in it, what has to happen for the company to succeed, and the pitfalls that stand in its path.  The two-minute monologue can be muttered under your breath or repeated out loud to colleagues who happen to be standing within earshot.  Once you’re able to tell the story of a stock to your family, your friends, or the dog… and so that even a child could understand it, then you have a proper grasp of the situation.  (pages 174-175)

Usually there are just a few key variables for a given investment idea.  As value investor Bruce Berkowitz has said:

We’ve always done very well when we can use sixth-grade math on the back of a postcard to show how inexpensive something is relative to its free cash. 

One good way to gain insight is to ask the executive of a company what he or she thinks the competition is doing right.  Lynch discovered La Quinta Motor Inns while he was talking to the vice president of United Inns, a competitor.

Lynch later learned that La Quinta’s strategy was simple:  to have both costs and prices that are 30% lower than Holiday Inn.  La Quinta had everything exactly the same as at a Holiday Inn – same size rooms, same size beds, etc.  However:

La Quinta had eliminated the wedding area, the conference rooms, the large reception area, the kitchen area, and the restaurant – all excess space that contributed nothing to the profits but added substantially to the costs.  La Quinta’s idea was to install a Denny’s or some similar 24-hour place next door to every one of its motels.  La Quinta didn’t even have to own the Denny’s.  Somebody else could worry about the food.  Holiday Inn isn’t famous for its cuisine, so it’s not as if La Quinta was giving up a major selling point…  It turns out that most hotels and motels lose money on their restaurants, and the restaurants cause 95 percent of the complaints.  (pages 177-178)



If it’s a micro-cap business, then you may be able to speak with a top executive simply by calling the company.  For larger companies, often you will only reach investor relations.  Either way, you want to check the story you’ve developed – your investment thesis – against the facts you’re able to glean through conversation (and through reading the financial statements, etc.).

If you don’t have a story developed enough to check, there are two general questions you can always ask, notes Lynch:

  • What are the positives this year?
  • What are the negatives?

Often your ideas will not be contradicted.  But occasionally you’ll learn something unexpected, that things are either better or worse than they appear.  Such unexpected information can be very profitable because it is often not yet reflected in the stock price.  Lynch says he comes across something unexpected in about one out of every ten phone calls he makes.

Lynch also often will visit headquarters.  The goal, he says, is to get a feel for the place.  What Lynch really appreciates is when headquarters is obviously shabby, indicating that the executives are keeping costs as low as possible.  When headquarters is very nice and fancy, that’s generally a bad signal about management.

Kicking the tires can help.  It’s not a substitute for studying the financial statements or for asking good questions.  But it can help you check out the practical side of the investment thesis.  Lynch used to make a point of checking out as many companies as possible this way.

Finally, in addition to reading the financial statements, Lynch recommends Value Line.



If you’re looking at a particular product, then you need to know what percent of sales that particular product represents.

Lynch then notes that you can compare the p/e and the growth rate:

If the p/e of Coca-Cola is 15, you’d expect the company to be growing at about 15 percent a year, etc.  But if the p/e ratio is less than the growth rate, you may have found yourself a bargain.  A company, say, with a growth rate of 12 percent a year… and a p/e of 6 is a very attractive prospect.  On the other hand, a company with a growth rate of 6 percent a year and a p/e ratio of 12 is an unattractive prospect and headed for a comedown.

In general, a p/e that’s half the growth rate is very positive, and one that’s twice the growth rate is very negative.  (page 199)

You can do a similar calculation by taking earnings, adding dividends, and comparing that sum to the growth rate.

High debt-to-equity is something to avoid.  Turnarounds with high debt tend to work far less often than turnarounds with low debt.

Free cash flow is important.  Free cash flow equals net income plus depreciation, depletion, and amortization, minus capital expenditures.  (There may also be adjustments for changes in working capital.)

The important point is that some companies and some industries – such as steel or autos – are far more capital-intensive than others.  Some companies have to spend most of their incoming cash on capital expenditures just to maintain the business at current levels.  Other companies have far lower reinvestment requirements; this means a much higher return on invested capital.  The value that any given company creates over time is the cumulative difference between the return on invested capital and the cost of capital.

Another thing to track is inventories.  If inventories have been piling up recently, that’s not a good sign.  When inventories are growing faster than sales, that’s a red flag.  Lynch notes that if sales are up 10 percent, but inventories are up 30 percent, then you should be suspicious.

Like Buffett, Lynch observes that a company that can raise prices year after year without losing customers can make for a terrific investment.  Such a company will tend to have high free cash flow and high return on invested capital over time.

A last point Lynch makes is about the growth rate of earnings:

All else being equal, a 20-percent grower selling as 20 times earnings (a p/e of 20) is a much better buy than a 10-percent grower selling at 10 times earnings (a p/e of 10).  This may sound like an esoteric point, but it’s important to understand what happens to the earnings of the faster growers that propels the stock price.  (page 218)

Lynch gives an example.  Assume Company A and Company B both start with earnings of $1.00 per share.  But assume that Company A grows at 20 percent a year while Company B grows at 10 percent a year.  (So the stock of Company A starts at $20, while the stock of Company B is at $10.)  What happens after 10 years, assuming the growth rates stay the same?  Company A will be earning $6.19 while Company B will be earning $2.59.

If the multiples haven’t changed, then the stock of Company A will be at $123.80, while the stock of Company B will only be at $26.  Even if the p/e for Company A falls to 15 instead of 20, the stock will still be at $92.  Going from $20 to $92 (or $123.80) is clearly better than going from $10 to $26.

One last point.  In the case of a successful turnaround, the stock of a relatively low-profit margin (and perhaps also high debt) company will do much better than the stock of a relatively high-profit margin (and low debt) company.  It’s just a matter of leverage.

For a long-term stock that you’re going to hold through good times and bad, you want high profit margins and low debt.  If you’re going to invest in a successful turnaround, then you want low profit margins and high debt, all else equal.  (In practice, most turnarounds don’t work, and high debt should be avoided unless you want to specialize in equity stubs.)



Every few months, you should check in on the company.  Are they on track?  Have they made any adjustments to their plan?  How are sales?  How are the earnings?  What are industry conditions?  Are their products still attractive?  What are their chief challenges?  Et cetera.  Basically, writes Lynch, have any new cards been turned over?

In the case of a growth company, Lynch holds that there are three phases.  In the start-up phase, the company may still be working the kinks out of the business.  This is the riskiest phase because the company isn’t yet established.  The second phase is rapid expansion.  This is generally the safest phase, and also where you can make the most money as an investor.  The third phase is the mature phase, or the saturation phase, when growth has inevitably slowed down.  The third phase can be the most problematic, writes Lynch, since it gets increasingly difficult to grow earnings.



Lynch offers a brief checklist.

  • The p/e ratio. Is it high or low for this particular company and for similar companies in the same industry.
  • The percentage of institutional ownership. The lower the better.
  • Whether insiders are buying and whether the company itself is buying back its own shares. Both are positive signs.
  • The record of earnings growth to date and whether the earnings are sporadic or consistent. (The only category where earnings may not be important is in the asset play.)
  • Whether the company has a strong balance sheet or a weak balance sheet (debt-to-equity ratio) and how it’s rated for financial strength.
  • The cash position. With $16 in net cash, I know Ford is unlikely to drop below $16 a share.

Lynch also gives a checklist for each of the six categories.  Then he gives a longer checklist summarizes all the main points (which have already been mentioned in the previous sections).



Some years you’ll make 30 percent, other years you’ll make 2 percent, and occasionally you’ll lose 20 or 30 percent.  It’s important to stick to a disciplined approach and not get impatient.  Stick with the long-term strategy through good periods and bad.  (Even great investors have periods of time when their strategy trails the market, often even several years in a row.)

Regarding the number of stocks to own, Lynch mentions a couple of stocks in which he wouldn’t mind investing his entire portfolio.  But he says you need to analyze each stock one at a time:

…The point is not to rely on any fixed number of stocks but rather to investigate how good they are, on a case-by-case basis.

In my view it’s best to own as many stocks as there are situations in which:  (a) you’ve got an edge; and (b) you’ve uncovered an exciting prospect that passes all the tests of research.  Maybe it’s a single stock, or maybe it’s a dozen stocks.  Maybe you’ve decided to specialize in turnarounds or asset plays and you buy several of those;  or perhaps you happen to know something special about a single turnaround or a single asset play.  There’s no use diversifying into unknown companies just for the sake of diversity.  A foolish diversity is the hobgoblin of small investors.  (pages 239-240)

Lynch then recommends, for small portfolios, owning between 3 and 10 stocks.  It makes sense to concentrate on your best ideas and on what you understand best.  At the same time, it often happens that the tenbagger comes – unpredictably – from your 8th or 9th best idea.  The value investor Mohnish Pabrai has had this experience.

  • Important Note – especially for investors in 2017:  One of the best edges you can have as an individual investor – in addition to being able to focus on micro-cap companies – is that you can have at least a 3- to 5-year holding period.  Because so many investors focus on shorter periods of time, very often the best bargains are stocks that are cheap relative to earnings in 3 to 5 years.

Lynch advises against selling winners and holding on to losers, which is like pulling out the flowers and watering the weeds.  But Lynch’s real point is that price movements are often random.  Just because a stock has gone up or down doesn’t mean the fundamental value of the business has changed.

If business value has increased – or if fundamentals have improved – then it often makes sense to add to a stock even if it’s gone from $11 to $19.

If business value has not declined, or has improved, then it often makes sense to add to a stock that has declined.  On the other hand, if business value has decreased – or if fundamentals have deteriorated – then it often makes sense to sell, regardless of whether the stock has gone up or down.

In general, as long as the investment thesis is intact and business value is sufficiently high, you should hold a stock for at least 3 to 5 years:

If I’d believed in ‘Sell when it’s a double,’ I would never have benefited from a single big winner, and I wouldn’t have been given the opportunity to write a book.  Stick around to see what happens – as long as the original story continues to make sense, or gets better – and you’ll be amazed at the results in several years.  (page 244)



Often there is tax loss selling near the end of the year, which means it can be a good time to buy certain stocks.

As mentioned, you should hold for at least 3 to 5 years as long as the investment thesis is intact and normalized business value is sufficiently high.  Be careful not to listen to negative nellies who yell ‘Sell!’ way before it’s time:

Even the most thoughtful and steadfast investor is susceptible to the influence of skeptics who yell ‘Sell’ before it’s time to sell.  I ought to know.  I’ve been talked out of a few tenbaggers myself.  (page 247)

Lynch took a big position in Toys ‘R’ Us in 1978.  He had done his homework, and he loaded up at $1 per share:

…By 1985, when Toys ‘R’ Us hit $25, it was a 25-bagger for some.  Unfortunately, those some didn’t include me, because I sold too soon.  I sold too soon because somewhere along the line I’d read that a smart investor named Milton Petrie, one of the deans of retailing, had bought 20 percent of Toys ‘R’ Us and that his buying was making the stock go up.  The logical conclusion, I thought, was that when Petrie stopped buying, the stock would go down.  Petrie stopped buying at $5.

I got in at $1 and out at $5 for a fivebagger, so how can I complain?  We’ve all been taught the same adages:  ‘Take profits when you can,’ and ‘A sure gain is always better than a possible loss.’  But when you’ve found the right stock and bought it, all the evidence tells you it’s going higher, and everything is working in your direction, then it’s a shame if you sell.  (pages 248-249)

Lynch continues by noting that individual investors are just as susceptible to selling early as are professional investors:

Maybe you’ve received the ‘Congratulations: Don’t Be Greedy’ announcement.  That’s when the broker calls to say:  ‘Congratulations, you’ve doubled your money on ToggleSwitch, but let’s not be greedy.  Let’s sell ToggleSwitch and try KinderMind.’  So you sell ToggleSwitch and it keeps going up, while KinderMind goes bankrupt, taking all of your profits with it. 

One major problem for all investors is that there are so many market, economic, and political forecasts.  Market forecasts and economic forecasts can be very expensive if you follow them, as Buffett has often noted.  The only thing you can really know is the individual company in which you’ve invested, and what its long-term business value is.  Everything else is noise that can only interfere with your ability to focus on long-term business value.

I quoted Buffett earlier.  Now let’s quote the father of value investing, Buffett’s teacher and mentor, Ben Graham:

… if I have noticed anything over these 60 years on Wall Street, it is that people do not succeed in forecasting what’s going to happen to the stock market.

This has happened in recent years, too.  Ever since 2012 or 2013, some exceptionally intelligent and well-informed people have been predicting some sort of reversion to the mean or bear market.  But it just hasn’t happened.  And if rates stay relatively low for the next 5-10 years, then stocks may not decline much from today’s levels (approaching 2300 on the S&P 500 Index).  On the other hand, there could be a bear market next year.  No one knows.

The main point made by Graham and Buffett is that repeatedly buying stocks at bargain levels relative to business value can work very well over time if you’re patient and disciplined.  Market forecasting can only distract you from what works.

What works is investing in businesses you can understand at sensible prices, and holding each one for at least 3 to 5 years, if not 10 years or longer, as long as the thesis is intact.  Lynch:

Frankly, I’ve never been able to predict which stocks will go up tenfold, or which will go up fivefold.  I try to stick with them as long as the story’s intact, hoping to be pleasantly surprised.  The success of a company isn’t the surprise, but what the shares bring often is.  I remember buying Stop & Shop as a conservative, dividend-paying stock, and then the fundamentals kept improving and I realized I had a fast grower on my hands.  (page 261)

It’s important to note that sometimes years can go by while the stock does nothing.  That, in itself, doesn’t tell you anything.  If the business continues to make progress, and long-term business value is growing (or is sufficiently high), then you should stick with it or perhaps add to the position:

Here’s something else that’s certain to occur:  If you give up on a stock because you’re tired of waiting for something wonderful to happen, then something wonderful will begin to happen the day after you get rid of it.  I call this the postdiverstiture flourish.

…Most of the money I make is in the third or fourth year that I’ve owned something… If all’s right with the company, and whatever attracted me in the first place hasn’t changed, then I’m confident that sooner or later my patience will be rewarded.

…It takes remarkable patience to hold on to a stock in a company that excites you, but which everybody else seems to ignore.  You begin to think everybody else is right and you are wrong.  But where the fundamentals are promising, patience is often rewarded…  (page 266)

Lynch again later:

… my biggest winners continue to be stocks I’ve held for three and even four years.  (page 281, my emphasis)

Lynch offers much commentary on a long list of macro concerns that are going to sink stocks.  (Remember he was writing in 1989.)  Here is a snipet:

I hear every day that AIDS will do us in, the drought will do us in, inflation will do us in, recession will do us in, the budget deficit will do us in, the trade deficit will do us in, and the weak dollar will do us in.  Whoops.  Make that the strong dollar will do us in.  They tell me real estate prices are going to collapse.  Last month people started worrying about that.  This month they’re worrying about the ozone layer.  If you believe the old investment adage that the stock market climbs a ‘wall of worry,’ take note that the worry wall is fairly good-sized now and growing every day.  (page 284)

Like Buffett, Lynch is very optimistic about America and long-term investing in general.  Given the strengths of America, and all the entrepreneurial and scientific energy creating ongoing innovation, it seems to me that Buffett and Lynch are right to be long-term optimists.  That’s not to say there won’t be setbacks, recessions, bear markets, and other problems.  But such setbacks will be temporary.  Over the long term, innovation will amaze, profits will grow, and stocks will follow profits higher.



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:

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

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

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


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

My e-mail:




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 Investment Checklist

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 23, 2017

Michael Shearn is the author of The Investment Checklist (Wiley, 2012), a very good book about how to research stocks.

For investors who have a long-term investment time horizon, micro-cap value stocks should be a major focus.  I launched the Boole Microcap Fund to create a very low-cost way for investors to invest in undervalued micro-cap stocks.  Boole currently uses a fully quantitative investment strategy.  (Ultimately Boole will use an early form of artificial intelligence, which is a natural extension of a fully quantitative strategy.)

For investors who use a fully quantitative strategy, it’s worthwhile to review good investment checklists like Shearn’s.  Although in practice, a quantitative micro-cap strategy can rely primarily on a few simple metrics – for example, a high EBIT/EV and a high Piotroski F-Score – one must regularly look for ways to improve the formula.



Shearn writes that he came up with his checklist by studying his own mistakes, and also by studying mistakes other investors and executives had made.  Shearn says the checklist helps an investor to focus on what’s important.  Shearn argues that a checklist also helps one to fight against the strong human tendency to seek confirming evidence while ignoring disconfirming evidence.

Shearn explains how the book is organized:

The three most common investing mistakes relate to the price you pay, the management team you essentially join when you invest in a company, and your failure to understand the future economics of the business you’re considering investing in.  (page xv)



There are many ways to find investment ideas.  One of the best ways to find the most potential investment ideas is to look at micro-cap stocks trading at high EBIT/EV (or, equivalently, low EV/EBIT) and with a high Piotroski F-Score.

Micro-cap stocks perform best over time.  See:

Low EV/EBIT – equivalently, high EBIT/EV – does better than the other standard measures of cheapness such as low P/E, low P/S, and low P/B.  See:

  • Quantitative Value (Wiley, 2013), by Wesley Gray and Tobias Carlisle
  • Deep Value (Wiley, 2014), Tobias Carlisle

A high Piotroski F-Score is most effective when applied to cheap micro-cap stocks.  See:

In sum, if you focus on micro-cap stocks trading at a high EBIT/EV and with a high Piotroski F-Score, you should regularly find many potentially good investment ideas.  This is essentially the process used by the Boole Microcap Fund.

There are, of course, many other good ways to find ideas.  Shearn mentions forced selling, such as when a stock is dropped from an index.  Also, spin-offs typically involve some forced selling.  Moreover, the 52-week low list and other new-low lists often present interesting ideas.

Looking for the areas of greatest distress can lead to good investment opportunities.  For instance, some offshore oil drillers appear to be quite cheap from a three- to five-year point of view assuming oil returns to a market clearing price of $60-70.



A fully quantitative approach can work quite well.  Ben Graham, the father of value investing, often used a fully quantitative approach.  Graham constructed a portfolio of the statistically cheapest stocks, according to various metrics like low P/E or low P/B.

I’ve already noted that the Boole Microcap Fund uses a fully quantitative approach:  micro-cap stocks with a high EBIT/EV and a high Piotroski F-Score.  This particular quantitative strategy has the potential to beat both the Russell Microcap Index and the S&P 500 Index by solid margins over time.

But there are a few ways that you can possibly do better than the fully quantitative micro-cap approach I’ve outlined.  One way is using the same quantitative approach as a screen, doing in-depth research on several hundred candidates, and then building a very concentrated portfolio of the best 5 to 8 ideas.

In practice, it is extremely difficult to make the concentrated approach work.  The vast majority of investors are better off using a fully quantitative approach (which selects the best 20 to 30 ideas, instead of the best 5 to 8 ideas).

The key ingredient to make the concentrated strategy work is passion.  Some investors truly love learning everything possible about hundreds of companies.  If you develop such a passion, and then apply it for many years, it’s possible to do better than a purely quantitative approach, especially if you’re focusing on micro-cap stocks.  Micro-cap stocks are the most inefficiently priced part of the market because most professional investors never look there.  Moreover, many micro-cap companies are relatively simple businesses that are easier for the investor to understand.

I’m quite passionate about value investing, including micro-cap value investing.  But I’m also passionate about fully automated investing, whether via index funds or quantitative value funds.  I know that low-cost broad market index funds are the best long-term investment for most investors.  Low-cost quantitative value funds – especially if focused on micro caps – can do much better than low-cost broad market index funds.

I am more passionate about perfecting a fully quantitative investment strategy – ultimately by using an early form of artificial intelligence – than I am about studying hundreds of micro-cap companies in great detail.  I know that a fully quantitative approach that picks the best 20 to 30 micro-cap ideas is very likely to perform better than my best 5 to 8 micro-cap ideas over time

Also, once value investing can be done well by artificial intelligence, it won’t be long before the best AI value investor will be better than the best human value investor.  Very few people thought that a computer could beat Garry Kasparov at chess, but IBM’s Deep Blue achieved this feat in 1997.  Similarly, few people thought that a computer could beat human Jeopardy! champions.  But IBM’s Watson trounced Ken Jennings and Brad Rutter at Jeopardy! in 2011.

Although investing is far more complex than chess or Jeopardy!, there is no reason to think that a form of artificial intelligence will not someday be better than the best human investors.  This might not happen for many decades.  But that it eventually will happen is virtually inevitable.  Scientists will figure out, in ever more detail, exactly how the human brain functions.  And scientists will eventually design a digital brain that can do everything the best human brain can do.

The digital brain will get more and more powerful, and faster and faster.  And at some point, the digital brain is likely to gain the ability to accelerate its own evolution (perhaps by re-writing its source code).  Some have referred to such an event – a literal explosion in the capabilities of digital superintelligence, leading to an explosion in technological progress – as the singularity.



If you’re going to try to pick stocks, then, notes Shearn, a good question to ask is: How would you evaluate this business if you were to become its CEO?

If you were to become CEO of a given business, then you’d want to learn everything you could about the industry and about the company.  To really understand a business can easily take 6-12 months or even longer, depending on your prior experience and prior knowledge, and also depending upon the size and complexity of the business.  (Micro-cap companies tend to be much easier to understand.)

You should read at least ten years’ worth of annual reports (if available).  If you’re having difficulty understanding the business, Shearn recommends asking yourself what the customer’s world would look like if the business (or industry) did not exist.

You should understand exactly how the business makes money.  You’d also want to understand how the business has evolved over time.  (Many businesses include their corporate history on their website.)



Shearn writes:

The more you can understand a business from the customer’s perspective, the better position you will be in to value that business, because satisfied customers are the best predictor of future earnings for a business.  As Dave and Sherry Gold, co-founders of dollar store retailer 99 Cent Only Stores, often say, ‘The customer is CEO.’  (page 39)

To gain an understanding of the customers, Shearn recommends that you interview some customers.  Most investors never interview customers.  So if you’re willing to spend the time interviewing customers, you can often gain good insight into the business that many other investors won’t have.

Shearn says it’s important to identify the core customers, since often a relatively small percentage of customers will represent a large chunk of the company’s revenues.  Core customers may also reveal how the business caters to them specifically.  Shearn gives an example:

Paccar is a manufacturer of heavy trucks that is a great example of a company that has built its product around its core customer, the owner operator.  Owner operators buy the truck they drive and spend most of their time in it.  They work for themselves, either contracting directly with shippers or subcontracting with big truck companies.  Owner operators care about quality first, and want amenities, such as noise-proofed sleeper cabins with luxury-grade bedding and interiors.  They also want the truck to look sharp, and Paccar makes its Peterbilt and Kenworth brand trucks with exterior features to please this customer.  Paccar also backs up the driver with service features, such as roadside assistance and a quick spare parts network.  Because owner operators want this level of quality and service, they are less price sensitive, and they will pay 10 percent more for these brands.  (page 42)

Shearn writes that you want to find out how easy or difficult it is to convince customers to buy the products or services.  Obviously a business with a product or service that customers love is preferable as an investment, other things being equal.

A related question is: what is the customer retention rate?  The longer the business retains a customer, the more profitable the business is.  Also, loyal customers make future revenues more predictable, which in itself can lead to higher profits.  Businesses that carefully build long-term relationships with their customers are more likely to do well.  Are sales people rewarded just for bringing in a customer, or are they also rewarded for retaining a customer?

You need to find out what pain the business alleviates for the customer, as well.  Similarly, you want to find out how essential the product or service is.  This will give you insight into how important the product or service is for the customers.  Shearn suggests the question: If the business disappeared tomorrow, what impact would this have on the customer base?



Not only do you want to find out if the business has a sustainable competitive advantage.  But you also want to learn if the industry is good, writes Shearn.  And you want to find out about supplier relations.

Shearn lists common sources of sustainable competitive advantage:

  • Network economics
  • Brand loyalty
  • Patents
  • Regulatory licenses
  • Switching costs
  • Cost advantages stemming from scale, location, or access to a unique asset

If a product or service becomes more valuable if more customers use it, then the business may have a sustainable competitive advantage from network economics.  Facebook becomes more valuable to a wider range of people as more and more people use it.

If customers are loyal to a particular brand and if the business can charge a premium price, this creates a sustainable competitive advantage.  Coca-Cola has a very strong brand.  So does See’s Candies (owned by Berkshire Hathaway).

A patent legally protects a product or service over a 17- to 20-year period.  If a patented product or service has commercial value, then the patent is a source of sustainable competitive advantage.

Regulatory licenses – by limiting competition – can be a source of sustainable competitive advantage.

Switching costs can create a sustainable competitive advantage.  If it has taken time to learn new software, for example, that can create a high switching cost.

There are various cost advantages that can be sustainable.  If there are high fixed-costs in a given industry, then as a business grows larger, it can benefit from lower per-unit costs.  Sometimes a business has a cost advantage by its location or by access to a unique asset.

Sustainable Competitive Advantages Are Rare

Even if a business has had a sustainable competitive advantage for some time, that does not guarantee that it will continue to have one going forward.  Any time a business is earning a high ROIC – more specifically, a return on invested capital that is higher than the cost of capital – competitors will try to take some of those excess returns.  That is the essence of capitalism.  High ROIC usually reverts to the mean (average ROIC) due to competition and/or due to changes in technology.

Most Investment Gains Are Made During the Development Phase

Shearn points out that most of the gains from a sustainable competitive advantage come when the business is still developing, rather than when the business is already established.  The biggest gains on Wal-Mart’s stock occurred when the company was developing.  Similarly for Microsoft, Amazon, or Apple.

Pricing Power

Pricing power is usually a function of a sustainable competitive advantage.  Businesses that have pricing power tend to have a few characteristics in common, writes Shearn:

  • They usually have high customer-retention rates
  • Their customers spend only a small percentage of their budget on the business’s product or service
  • Their customers have profitable business models
  • The quality of the product is more important than the price

Nature of Industry and Competitive Landscape

Some industries, like software, may be considered “good” in that the best companies have a sustainable competitive advantage as represented by a sustainably high ROIC.

But an industry with high ROIC’s, like software, is hyper-competitive.  Competition and/or changes in technology can cause previously unassailable competitive advantages to disappear entirely.

It’s important to examine companies that failed in the past.  Why did they fail?

IMPORTANT:  Stock Price

As a value investor, depending upon the price, a low-quality asset can be a much better investment than a high-quality asset.  This is a point Shearn doesn’t mention but should.  As Howard Marks explains:

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, gets most investors into trouble.

Supplier Relations

Does the business have a good relationship with its suppliers?  Does the business help suppliers to innovate?  Is the business dependent on only a few suppliers?



Shearn explains why the fundamentals – the things a business has to do in order to be successful – are important:

As an investor, identifying and tracking fundamentals puts you in a position to more quickly evaluate a business.  If you already understand the most critical measures of a company’s operational health, you will be better equipped to evaluate unexpected changes in the business or outside environment.  Such changes often present buying opportunities if they affect the price investors are willing to pay for a business without affecting the fundamentals of the business.  (page 99)

Moreover, there are specific operating metrics for a given business or industry that are important to track.  Monitoring the right metrics can give you insight into any changes that may be significant.  Shearn lists the following industry primers:

  • Reuters Operating Metrics
  • Standard & Poor’s Industry Surveys
  • Fisher Investment guides

Shearn also mentions internet search and books are sources for industry metrics.  Furthermore, there are trade associations and trade journals.

Shearn suggests monitoring the appropriate metrics, and writing down any changes that occur over three- to five-year periods.  (Typically a change over just one year is not enough to draw a conclusion.)

Key Risks

Companies list their key risks in the 10-K in the section Risk Factors.  It is obviously important to identify what can go wrong.  Shearn:

…it is important for you to spend some time in this section and investigate whether the business has encountered the risks listed in the past and what the consequences were.  This will help you understand how much impact each risk may have.  (page 106)

You would like to identify how each risk could impact the value of the business.  You may want to use scenario analysis of the value of the business in order to capture specific downside risks.

Shearn advises thinking like an insurance underwriter about the risks for a given business.  What is the frequency of a given risk – in other words, how often has it happened in the past?  And what is the severity of a given risk – if the downside scenario materializes, what impact will that have on the value of the business?  It is important to study what has happened in the past to similar businesses and/or to businesses that were in similar situations.  This allows you to develop a better idea of the frequency – i.e., the base rate – of specific risks.

Is the Balance Sheet Strong or Weak?

A strong balance sheet allows the business not only to survive, but in some cases, to thrive by being able to take advantage of opportunities.  A weak balance sheet, on the other hand, can mean the difference between temporary difficulties and insolvency.

You need to figure out if future cash flows will be enough to make future debt payments.

For value investors in general, the advice given by Graham, Buffett, and Munger is best: Avoid companies with high debt.  The vast majority of the very best value investments ever made involved companies with low debt or no debt.  Therefore, it is far simpler just to avoid companies with high debt.

Occasionally there may be equity stub situations where the potential upside is so great that a few value investors may want to carefully consider it.  Then you would have to determine what the liquidity needs of the business are, what the debt-maturity schedule is, whether the interest rates are fixed or variable, what the loan covenants indicate, and whether specific debts are resource or non-recourse.

Return on Reinvestment or RONIC

It’s not high historical ROIC that counts:

What counts is the ability of a business to reinvest its excess earnings at a high ROIC, which is what creates future value.  (page 129)

You need to determine the RONIC – return on new invested capital.  How much of the excess earnings can the company reinvest and at what rate of return?

How to Improve ROIC

Shearn gives two ways a business can improve its ROIC:

  • Using capital more efficiently, such as managing inventory better or managing receivables better, or
  • Increasing profit margins, instead of through one-time, non-operating boosts to cash earnings.

A supermarket chain has low net profit margins, so it must have very high inventory turnover to be able to generate high ROIC.  On the other hand, a steel manufacturer has low asset turnover, therefore it must achieve a high profit margin in order to generate high ROIC.



Scenario analysis is useful when there is a wide range of future earnings.  As mentioned earlier, some offshore oil drillers appear very cheap right now on the assumption that oil returns to a market clearing price of $60-70 a barrel within the next few years.  If it takes five years for oil to return to $60-70, then many offshore oil drillers will have lower intrinsic value (a few may not survive).  If it takes three years (or less) for oil to return to $60-70, then some offshore drillers are likely very cheap compared to their normalized earnings.

Compare Cash Flow from Operations to Net Income

As Shearn remarks, management has much less flexibility in manipulating cash flow from operations than it does net income because the latter includes many subjective estimates.  Over the past one to five years, cash flow from operations should closely approximate net income, otherwise there may be earnings manipulation.


If the accounting is conservative and straightforward, that should give you more confidence in management than if the accounting is liberal and hard to understand.  Shearn lists some ways management can manipulate earnings:

  • Improperly inflating sales
  • Under- or over-stating expenses
  • Manipulating discretionary costs
  • Changing accounting methods
  • Using restructuring charges to increase future earnings
  • Creating reserves by manipulating estimates

Management can book a sale before the revenue is actually earned in order to inflate revenues.

Management can capitalize an expense over several time periods, which shifts some current expenses to later periods thereby boosting short-term earnings.  Expenses commonly capitalized include start-up costs, R&D expenses, software development, maintenance costs, marketing, and customer-acquisition costs.  Shearn says you can find out whether a business routinely capitalizes its costs by reading the footnotes to the financial statements.

Manipulating discretionary costs is common, writes Shearn.  Most companies try to meet their quarterly earnings goals.  Most great owner operator businesses – like Warren Buffett’s Berkshire Hathaway or Henry Singleton’s Teledyne – spend absolutely no time worrying about short-term (including quarterly) earnings.

Managers often extend the useful life of particular assets, which reduces quarterly depreciation expenses.

A business reporting a large restructuring loss may add extra expenses in the restructuring charge in order to reduce future expenses (and boost future earnings).

Management can overstate certain reserve accounts in order to draw on those reserves during future bad times (in order to boost earnings during those bad times).  Reserves can be booked for: bad debts, sales returns, inventory obsolescence, warranties, product liability, litigation, or environmental contingencies.

Operating Leverage

If a business has high operating leverage, then it is more difficult to forecast future earnings.  Again, scenario analysis can help in this situation.

High operating leverage means that a relatively small change in revenues can have a large impact on earnings.  A business with high fixed costs has high operating leverage, whereas a business with low fixed costs has low operating leverage.

For example, as Shearn records, in 2008, Boeing reported that revenues decreased 8.3 percent and operating income decreased 33.9 percent.

Working Capital

Shearn explains:

The amount of working capital a business needs depends on the capital intensity and the speed at which a business can turn its inventory into cash.  The shorter the commitment or cycle, the less cash is tied up and the more a business can use the cash for other internal purposes.  (page 163)

Boeing takes a long time to turn sheet metal and various electronics into an airplane.  Restaurants, on the other hand, turn inventories into cash quite quickly.

The Cash Conversion Cycle (CCC) tells you how quickly a company can turn its inventory and receivables into cash and pay its short-term obligations.

CCC = Inventory conversion period (Days)

+ Receivables conversion period (Days)

– Payables conversion period (Days)

When a company has more current liabilities than current assets, that means it has negative working capital.  In this situation, the customers and suppliers are financing the business, so growth is less expensive.  Typically cash flow from operations will exceed net income for a business with negative working capital.

Negative working capital is only good as long as sales are growing, notes Shearn.



Sound management is usually essential for a business to do well, although ideally, as Buffett joked, you want a business so good that any idiot can run it, because eventually one will.

Shearn offers good advice on how to judge management:

It is best to evaluate a management team over time.  By not rushing into investment decisions and by taking the time to understand a management team, you can reduce your risk of misjudging them.  Most errors in assessing managers are made when you try to judge their character quickly or when you see only what you want to see and ignore flaws or warning signs.  The more familiar you are with how managers act under different types of circumstances, the better you are able to predict their future actions.  Ideally, you want to understand how managers have operated in both difficult and favorable circumstances.  (pages 174-175)

Types of managers

  • Owner-operator
  • Long-tenured manager
  • Hired hand

An owner-operator is a manager who has a genuine passion for the business and is typically the founder.  Shearn gives examples:

  • Sam Walton, founder of Wal-Mart
  • Dave and Sherry Gold, co-founders of 99 Cent Only Stores
  • Joe Mansueto, founder of Morningstar
  • John Mackey, co-founder of Whole Foods Market
  • Warren Buffett, CEO of Berkshire Hathaway
  • Founders of most family-controlled businesses

Shearn continues:

These passionate leaders run the business for key stakeholders such as customers, employees, and shareholders alike… They typically are paid modestly and have high ownership interests in the business.  (page 177)

(Shearn also defines a second and third type of owner-operator to the extent that the owner-operator runs the business for their own benefit.)

A long-tenured manager has worked at the business for at least three years.  (A second type of long-tenured manager joined from outside the business, but worked in the same industry.)

A hired hand is a manager who has joined from outside the business, but who has worked in a related industry.  (Shearn defines a second type of hired hand who has worked in a completely unrelated industry.)

The Importance of Tenure in Operating the Business

Out of the 500 businesses in the S&P 500, only 28 have CEOs who have held office for more than 15 years (this is as of the year 2012, when Shearn was writing).  Of these 28 long-term CEOs, 25 of them had total shareholder returns during their tenures that beat the S&P 500 index (including dividends reinvested).

Management Style: Lions and Hyenas

Based on an interview with Seng Hock Tan, Shearn distinguishes between Lion Managers and Hyena Managers.

Lion Manager:

  • Committed to ethical and moral values
  • Thinking long term and maintains a long-term focus
  • Does not take shortcuts
  • Thirsty for knowledge and learning
  • Supports partners and alliances
  • Treats employees as partners
  • Admires perseverance

Hyena Manager:

  • Has little interest in ethics and morals
  • Thinks short term
  • Just wants to win the game
  • Has little interest in knowledge and learning
  • A survivor and an opportunist
  • Treats employees as expenses
  • Admires tactics, resourcefulness, and guile

Operating Background

Shearn observes that it can be risky to have a top executive who does not have a background in the day-to-day operations of the business.

Low Salaries and High Stock Ownership

Ideally, managers will be incentivized based high stock ownership (and comparatively low salaries) as a function of building long-term business value.  This aligns management incentives with shareholder interests.

You also want managers who are generous to all employees in terms of stock ownership.  This means the managers and employees have similar incentives (which are aligned with shareholder interests).

Finally, you want managers who gradually increase their ownership interest in the business over time.



Obviously you prefer a good manager, not only because the business will tend to do better over time, but also because you won’t have to spend time worrying.

Shearn on a CEO who manages the business for all stakeholders:

If you were to ask investors whether shareholder value is more important than customer service at a business, most would answer that it is.  What they fail to consider is that shareholder value is a byproduct of a business that keeps its customers happy.  In fact, many of the best-performing stocks over the long term are the ones that balance the interests of all stakeholder groups, including customers, employees, suppliers, and other business partners.  These businesses are managed by CEOs who have a purpose greater than solely generating profits for their shareholders.  (pages 210-211)

Shearn mentions John Mackey, co-founder and CEO of Whole Foods Market, who coined the term conscious capitalism to describe businesses designed to benefit all stakeholders.  Shearn quotes Mackey:

Long-term profits come from having a deeper purpose, great products, satisfied customers, happy employees, great suppliers, and from taking a degree of responsibility for the community and environment we live in.  The paradox of profits is that, like happiness, they are best achieved by not aiming directly for them.

Continuous Incremental Improvement


Contrary to popular belief, most successful businesses are built on hundreds of small decisions, instead of on one well-formulated strategic plan.  For example, when most successful entrepreneurs start their business, they do not have a business plan stating what their business will look like in 2, 5, or 10 years.  They instead build their business day by day, focusing on customer needs and letting these customer needs shape the direction of their business.  It is this stream of everyday decisions over time that accounts for great outcomes, instead of big one-time decisions….

Another common theme among businesses that improve day by day is that they operate on the premise that it is best to repeatedly launch a product or service with a limited number of its customers so that it can use customer reactions and feedback to modify it.  They operate on the premise that it is okay to learn from mistakes…

You need to determine if the management team you are investing in works on proving a concept before investing a lot of capital in it or whether it prefers to put a lot of money in all at once hoping for a big payoff.  (page 215)

PIPER = persistent incremental progress eternally repeated

As CEO, Henry Singleton was one of the best capital allocators in American business history.  Under Singleton, Teledyne stock compounded at 17.9 percent over 25 years (or a 53x return, vs. 6.7x for the S&P 500 Index).

Singleton believed that the best plan was no plan, as he once explained at an annual meeting:

…we’re subject to a tremendous number of outside influences, and the vast majority of them cannot be predicted.  So my idea is to stay flexible.  I like to steer the boat each day rather than plan ahead way into the future.

Shearn points out that one major problem with a strategic plan is the commitment and consistency principle (see Robert Cialdini’s Influence).  When people make a public statement, they tend to have a very difficult time admitting they were wrong and changing course when the evidence calls for it.  Similarly, notes Shearn, strategic plans can make people blind to other opportunities.

When managers give short-term guidance, it can have similar effects as a strategic plan.  People may make decisions that harm long-term business value just in order to hit short-term (statistically meaningless) numbers.  Also, managers may even start borrowing from the future in order to meet the numbers.  Think of Enron, WorldCom, Tyco, Adelphia, and HealthSouth, says Shearn.

Does management value its employees?


…Try to understand if the management team values its employees because the only way it will obtain positive results is through these people.

When employees feel they are partners with their boss in a mutual effort, rather than merely employees of some business run by managers they never see, morale will increase.  Furthermore, when a business has good employee relations, it typically has many other good attributes, such as good customer relations and the ability to adapt quickly to changing economic circumstances.  (page 225)

Are the CEO and CFO disciplined in making capital allocation decisions?

As Shearn observes, operating a business and allocating capital involve two completely different skills sets.  Many CEOs do not have skill in capital allocation.  Capital allocation includes:

  • Investing in new projects
  • Holding cash on the balance sheet
  • Paying dividends
  • Buying back stock
  • Making acquisitions

Shearn writes:

One of the best capital allocators in corporate history was Henry Singleton, longtime CEO of Teledyne, who cofounded the business in 1960 and served as CEO until 1986.  In John Train’s book The Money Masters, Warren Buffett reported that he believes ‘Henry Singleton has the best operating and capital-deployment record in American business.’  When Teledyne’s stock was trading at extremely high prices in the 1960s, Singleton used the high-priced stock as currency to make acquisitions.  Singleton made more than 130 acquisitions of small, high-margin manufacturing and technology businesses that operated in defensible niches managed by strong management.  When the price-to-earnings ratio of Teledyne fell sharply starting in the 1970s, he repurchased stock.  Between 1972 and 1984, he reduced the share count by more than 90 percent.  He repurchased stock for as low as $6 per share in 1972, which by 1987 traded at more than $400 per share.  (page 249)



Does the CEO love the money or the business?

This question comes from Warren Buffett.  Buffett looks for CEOs who love the business.  CEOs who are passionate about their business are more likely to persevere through many difficulties and over long periods of time.  CEOs who are passionate about their business are more likely to excel over the long term.  As Steve Jobs said in his commencement address to Stanford University students in 2005:

The only way to do great work is to love what you do.  If you haven’t found it yet, keep looking.  Don’t settle.

If someone has stayed in one industry for a long time, odds are they love their work.  If a CEO is very focused on the business, and not worried about appearances or large social or charity events, that’s a good sign the CEO is passionate about the business.  Does the CEO direct philanthropic resources to causes they truly care about, or are they involved in ‘social scene philanthropy’?

Are the Managers Lifelong Learners Who Focus on Continuous Improvement?

Lifelong learners are managers who are never satisfied and continually find ways to improve the way they run a business.  This drive comes from their passion for the business.  It is extremely important for management to constantly improve, especially if a business has been successful for a long period of time.  Look for managers who regard success as a base from which they continue to grow, rather than as a final accomplishment.  (page 263)

How Have They Behaved Under Adversity?


You never truly know someone’s character until you have seen it tested by stress, adversity, or a crisis, because a crisis produces extremes in behavior…  (page 264)

You need to determine how a manager responds to a difficult situation and then evaluate the action they took.  Were they calm and intentional in dealing with a negative situation, or were they reactive instead?  (page 266)

The best managers are those who quickly and openly communicate how they are thinking about the problem and outline how they are going to solve it.  (page 267)

Does Management Think Independently?

…The best managers always maintain a long-term focus, which means that they are often building for years before they see concrete results.  For example, in 2009, Jeff Bezos, founder of online retailer, talked about the way that some investors congratulate on success in a single reporting period.  ‘I always tell people, if we have a good quarter, it’s because of the work we did three, four, and five years ago.  It’s not because we did a good job this quarter.’  (page 275)

The best CEOs think independently.



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:

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

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

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


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

My e-mail:




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 Growth Trap

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 16, 2017

Jeremy Siegel is the author of The Future for Investors (Crown Business, 2005).  Warren Buffett commented:  “Jeremy Siegel’s new facts and ideas should be studied by investors.”  (Although the book was published in 2005, most of the facts and ideas still hold.)



Siegel summarizes the main lesson from his previous book, Stocks for the Long Run:

My research showed that over extended periods of time, stock returns not only dominate the returns on fixed-income assets, but they do so with lower risk when inflation is taken into account.  These findings established that stocks should be the cornerstone of all long-term investors’ portfolios.

As you extend forward in time, especially to three or four decades, the real return from stocks is roughly 6.5 to 7 percent, which will nearly always be better than any other investment, such as fixed-income or gold.  Siegel has given many talks on Stocks for the Long Run, and he reports that two questions always come up:

  • Which stocks should I hold for the long run?
  • What will happen to my portfolio when the baby boomers retire and begin liquidating their portfolios?

Siegel says he wrote The Future for Investors in order to answer these questions.  He studied all 500 firms that constituted the S&P 500 Index when it was first formulated in 1957.  His conclusions – that the original firms in the index outperformed the newcomers (those added later to the index) – were surprising:

These results confirmed my feeling that investors overprice new stocks, many of which are in high technology industries, and ignore firms in less exciting industries that often provide investors superior returns.  I coined the term the growth trap to describe the incorrect belief that the companies that lead in technological innovation and spearhead economic growth bring investors superior returns.

The more I investigated returns, the more I determined that the growth trap affected not just individual stocks, but also entire sectors of the market and even countries.  The fastest-growing new firms, industries, and even foreign countries often suffered the worst return.  I formulated the basic principle of investor return, which specifies that growth alone does not yield good returns, but only growth in excess of the often overly optimistic estimates that investors have built into the price of stock.  It was clear that the growth trap was one of the most important barriers between investors and investment success.  (page xii, my emphasis)

As regards the aging of the baby-boom generation, Siegel argues that growth in developing countries (like China and India) will keep the global economy moving forward.  Also, as citizens in developing countries become wealthier, they will buy assets that baby-boomers are selling.  Siegel also holds that information technology will be central to global economic growth.

I am not going to discuss baby-boomer retirement any further in this blog post.  I would only note that there is a good chance that economically significant innovation could surprise on the upside in the next few decades.  See, for instance, The Second Machine Age, by Erik Brynjolfsson and Andrew McAfee (W. W. Norton, 2016).  As Warren Buffett has frequently observed, the luckiest people in history are those being born now.  Life in the future for most people is going to be far better than at any previous time in history.



Siegel opens the first chapter by noting the potential impact of improving technology:

The future for investors is bright.  Our world today stands at the brink of the greatest burst of invention, discovery, and economic growth ever known.

Yet investors must be careful to avoid the growth trap:

The growth trap seduces investors into overpaying for the very firms and industries that drive innovation and spearhead economic expansion.  This relentless pursuit of growth – through buying hot stocks, seeking exciting new technologies, or investing in the fastest-growing countries – dooms investors to poor returns.  In fact, history shows that many of the best-performing investments are instead found in shrinking industries and in slower-growing countries.  (page 3, my emphasis)

Although technology has created amazing wealth and well-being, investing in new technologies is generally a poor investment strategy.  Siegel explains:

How can this happen?  How can these enormous economic gains made possible through the proper application of new technology translate into substantial investment losses?  There’s one simple reason:  in their enthusiasm to embrace the new, investors invariably pay too high a price for a piece of the action.  The concept of growth is so avidly sought after that it lures investors into overpriced stocks in fast-changing and overly competitive industries, where the few big winners cannot begin to compensate for the myriad of losers.  (page 5)

To illustrate his point, Siegel compares Standard Oil of New Jersey (now ExxonMobil) with the new-economy juggernaut, IBM.  Consider the growth rates of these companies from 1950 to 2003:


Growth Measures IBM Standard Oil Advantage
Revenue/Share 12.19% 8.04% IBM
Dividends/Share 9.19% 7.11% IBM
Earnings/Share 10.94% 7.47% IBM
Sector Growth 14.65% -14.22% IBM


IBM performed much better fundamentally than Standard Oil of New Jersey.  Moreover, from 1950 to 2003, the technology sector rose from 3 percent of the market to almost 18 percent, while oil stocks shrank from 20 percent of the market to 5 percent.  Therefore, it seems clear that an investor who had to choose between IBM and Standard Oil in 1950 should have chosen IBM.  But this would have been the wrong decision.

Over the entire period, Standard Oil of New Jersey had an average P/E of 12.97, while IBM had an average P/E of 26.76.  Also, Standard Oil had an average dividend yield of 5.19%, while IBM had an average dividend yield of 2.18%.  As a result, the total returns for the two stocks were as follows:


Return  Measures IBM Standard Oil Advantage
Price Appreciation 11.41% 8.77% IBM
Dividend Return 2.18% 5.19% Standard Oil
Total Return 13.83% 14.42% Standard Oil


Siegel explains:

IBM did very well, but investors expected it to do well, and its stock price was consistently high.  Investors in Standard Oil had very modest expectations for earnings growth and kept the price of its shares low, allowing investors to accumulate more shares through the reinvestment of dividends.  The extra shares proved to be Standard Oil’s margin of victory.  (page 9)

Here are Siegel’s broad conclusions on the S&P 500 Index:

  • The more than 900 new firms that have been added to the index since it was formulated in 1957 have, on average, underperformed the original 500 firms in the index.
  • Long-term investors would have been better off had they bought the original S&P 500 firms in 1957 and never bought any new firms added to the index. By following this buy-and-never-sell approach, investors would have outperformed almost all mutual funds and money managers over the last half century.
  • Dividends matter a lot. Reinvesting dividends is the critical factor giving the edge to most winning stocks in the long run… Portfolios invested in the highest-yielding stocks returned 3 percent per year more than the S&P 500 Index, while those in the lowest-yielding stocks lagged the market by almost 2 percent per year.
  • The return on stocks depends not on earnings growth but solely on whether this earnings growth exceeds what investors expected, and those growth expectations are embodied in the price-to-earnings, or P/E ratio. Portfolios invested in the lowest-P/E stocks in the S&P 500 Index returned almost 3 percent per year more than the S&P 500 Index, while those invested in the high-P/E stocks fell 2 percent per year behind the index.
  • The growth trap holds for industry sectors as well as individual firms. The fastest-growing sector, the financials, has underperformed the benchmark S&P 500 Index, while the energy sector, which has shrunk almost 80 percent since 1957, beat this benchmark index.  The lowly railroads, despite shrinking from 21 percent to less than 5 percent of the industrial sector, outperformed the S&P 500 Index over the last half century.
  • The growth trap holds for countries as well. The fastest-growing country over the last decade has rewarded investors with the worst returns.  China, the economic powerhouse of the 1990s, has painfully disappointed investors with its overpriced shares and falling stock prices.



But how, you will ask, does one decide what [stocks are] ‘attractive’?  Most analysts feel they must choose between two approaches customarily thought to be in opposition: ‘value’ and ‘growth.’… We view that as fuzzy thinking… Growth is always a component of value [and] the very term ‘value investing’ is redundant. 

– Warren Buffett, Berkshire Hathaway annual report, 1992

What was the best-performing stock from 1957 to 2003?  Siegel answers that it was Philip Morris.  Siegel observes:

The superb returns in Philip Morris illustrate an extremely important principle of investing:  what counts is not just the growth rate of earnings but the growth of earnings relative to the market’s expectation.  One reason investors had low expectations for Philip Morris’s growth was because of its potential liabilities.  But its growth has continued apace.  The low expectations combined with high growth and a high dividend yield provide the perfect environment for superb investor returns.  (page 35)

What were the top-performing S&P 500 Survivors from 1957 to 2003?


Rank 2003 Name Accumulation of $1,000 Annual Return
1 Philip Morris $4,626,402 19.75%
2 Abbott Labs $1,281,335 16.51%
3 Bristol-Myers Squibb $1,209,445 16.36%
4 Tootsie Roll Industries $1,090,955 16.11%
5 Pfizer $1,054,823 16.03%
6 Coca-Cola $1,051,646 16.02%
7 Merck $1,003,410 15.90%
8 PepsiCo $866,068 15.54%
9 Colgate-Palmolive $761,163 15.22%
10 Crane $736,796 15.14%
11 H. J. Heinz $635,988 14.78%
12 Wrigley $603,877 14.65%
13 Fortune Brands $580,025 14.55%
14 Kroger $546,793 14.41%
15 Schering-Plough $537,050 14.36%
16 Proctor & Gamble $513,752 14.26%
17 Hershey Foods $507,001 14.22%
18 Wyeth $461,186 13.99%
19 Royal Dutch Petroleum $398,837 13.64%
20 General Mills $388,425 13.58%
S&P 500 $124,486 10.85%


Siegel points out that most of the top twenty performers have strong brands, but are not technology companies per se.  Siegel discusses some of these great companies:

Number four on this list is a most unlikely winner – a small manufacturer originally named the Sweets Company of America.  This company has outperformed the market by 5 percent a year since the index was formulated.  The founder of this firm, an Austrian immigrant, named its product after his five-year-old daughter’s nickname, Tootsie….

The surviving company with the sixth highest return produces a product today with the exact same formula as it did over 100 years ago, much like Tootsie Roll…. Although the company keeps the formula for its drinks secret, it is no secret that Coca-Cola has been one of the best companies you could have owned over the last half century.

…Pepsie also delivered superb returns to its shareholders, coming in at number eight and beating the market by over 4 percent per year.

Two others of the twenty best-performing stocks also manufacture products virtually unchanged over the past 100 years:  the William Wrigley Jr. Company and Hershey Foods.  Wrigley came in at number twelve, beating the market by almost 4 percent per year, whereas Hershey came in at seventeen, beating the market by 3 percent a year.

Wrigley is the largest gum manufacturer in the world, commanding an almost 50 percent share in the global market and selling in approximately 100 countries.  Hershey is currently the number-one U.S.-based publicly traded candy maker (Mars, a private firm, is number one, followed by Swiss-based Nestle).

…Heinz is another strong brand name, one that is virtually synonymous with ketchup.  Each year, Heinz sells 650 million bottles of ketchup and makes 11 billion packets of ketchup and salad dressings – almost two packets for every person on earth.  But Heinz is just not a ketchup producer, and it does not restrict its focus to the United States.  It has the number-one or –two branded business in fifty different countries, with products such as Indonesia’s ABC soy sauce (the second-largest-selling soy sauce in the world) and Honig dry soup, the best-selling soup brand in the Netherlands.

Colgate-Palmolive also makes the list, coming in at number nine.  Colgate’s products include Colgate toothpastes, Speed Stick deoderant, Irish Spring soaps, antibacterial Softsoap, and household cleaning products such as Palmolive and Ajax.

No surprise that Colgate’s rival, Procter & Gamble, makes this list as well, at number sixteen.  Procter & Gamble began as a small, family-operated soap and candle company in Cincinnati, Ohio, in 1837.  Today, P&G sells three hundred products, including Crest, Mr. Clean, Tide, and Tampax, to more than five billion consumers in 140 countries.

…Number twenty on the list is General Mills, another company with strong brands, which include Betty Crocker, introduced in 1921, Wheaties (the ‘Breakfast of Champions’), Cheerios, Lucky Charms, Cinnamon Toast Crunch, Hamburger Helper, and Yoplait yogurt.

What is true about all these firms is that their success came through developing strong brands not only in the United States but all over the world.  A well-respected brand name gives the firm the ability to price its product above the competition and deliver more profits to investors.

…Besides the strong consumer brand firms, the pharmaceuticals had a prominent place on the list of best-performing companies.  It is noteworthy that there were only six health care companies in the original S&P 500 that survive to today in their original corporate form, and all six made it onto the list of best performers.  All of these firms not only sold prescription drugs but also were very successful in marketing brand-name over-the-counter treatments to consumers, very much like the brand-name consumer staples stocks that we have reviewed.

…When these six pharmaceuticals are added to the eleven name-brand consumer firms, seventeen, or 85 percent, of the twenty top-performing firms from the original S&P 500 Index, feature well-known consumer brand names.  (pages 37-41)



What really matters for investors is the price paid today compared to all future free cash flows.  But investors very regularly overvalue high-growth companies and undervalue low-growth or no-growth companies.  This is the key reason value investing works.  As Siegel writes:

Expectations are so important that without even knowing how fast a firm’s earnings actually grow, the data confirm that investors are too optimistic about fast-growing companies and too pessimistic about slow-growing companies.  This is just one more confirmation of the growth trap.  (page 42)

Thus, if you want to do well as an investor, it is best to stick with companies trading at low multiples (low P/E, low P/B, low P/S, etc.).  All of the studies have confirmed this.  See:

Siegel did his own study, dividing S&P 500 Index companies into P/E quintiles.  From 1957 to 2003, the lowest P/E quintile – bought at the beginning of each year – produced an average annual return of 14.07% (with a risk of 15.92%), while the highest P/E quintile produced an average annual return of 9.17% (with a risk of 19.39%).  The S&P 500 Index averaged 11.18% (with a risk of 17.02%)

If you’re doing buy-and-hold value investing – as Warren Buffett does today – then you can pay a higher price as long as it is still reasonable and as long as the brand is strong enough to persist over time.  Buffett has made it clear, however, that if he were managing a small amount of money, he would focus on micro-cap companies available at cheap prices.  That would generate the highest returns, with 50% per year being possible in micro caps for someone like Buffett.

Siegel discusses GARP, or growth at a reasonable price:

Advocates here compute a very similar statistic called the PEG ratio, or price-to-earnings ratio divided by the growth rate of earnings.  The PEG ratio is essentially the inverse of the ratio that Peter Lynch recommended in his book, assuming you add the dividend yield to the growth rate.  The lower the PEG ratio, the more attractively priced a firm is with respect to its projected earnings growth.  According to Lynch’s criteria, you would be looking for firms with lower PEG ratios, preferably 0.5 or less, but certainly less than 1.  (page 46)

It’s important to note that earnings growth is very mean-reverting.  In other words, most companies that have been growing fast do NOT continue to do so, but tend to slow down quite a bit.  That’s why deep value investing – simply buying the cheapest companies (based on low P/E or low EV/EBIT), which usually have low- or no-growth – tends to produce better returns over time than GARP investing does.  This is most true, on average, when you invest in cheap micro-cap companies.

Deep value micro-cap investing tends to work quite well, especially if you also use the Piotroski F-Score to screen for cheap micro-cap companies that also have improving fundamentals.  This is the approach used by the Boole Microcap Fund.

One other way to do very well investing in micro caps is to try to find the ones that will grow for a long time.  So you’re looking for the future Wal-Mart’s or Microsoft’s.  Ian Cassel at the MicroCapClub – – uses this approach and continues to do well with it.



Most investors seem to believe that the fastest-growing industries will yield the best returns.  But this is simply not true.  Siegel compares financials to energy companies:

Of the ten major industries, the financial sector has gained the largest share of market value since the S&P 500 Index was founded in 1957.  Financial firms went from less than 1 percent of the index to over 20 percent in 2003, while the energy sector has shrunk from over 21 percent to less than 6 percent over the same period.  Had you been looking for the fastest-growing sector, you would have sunk your money in financial stocks and sold your oil stocks.

But if you did so, you would have fallen into the growth trap.  Since 1957, the returns on financial stocks have actually fallen behind the S&P 500 Index, while energy stocks have outperformed over the same period.  For the long-term investor, the strategy of seeking out the fastest-growing sector is misguided.  (pages 50-51)

Siegel continues by noting that the GICS (Global Industrial Classification Standard) breaks the U.S. and world economy down into ten sectors:  materials, industrials, energy, utilities, telecommunication services, consumer discretionary, consumer staples, health care, financial, and information technology.  (Recently real estate has been added as an eleventh sector.)

Just as the fastest-growing companies, as a group, underperform the slower growers in terms of investment returns, so new companies underperform the tried and true.  Siegel explains what his data show:

These data confirm my basic thesis:  the underperformance of new firms is not confined to one industry, such as technology, but extends to the entire market.  New firms are overvalued by investors in virtually every sector of the market.  (page 51)

Siegel also answers the question of why energy did so well, despite shrinking from over 21 percent to less than 6 percent of the market:

Why did the energy sector perform so well?  The oil firms concentrated on what they did best:  extracting oil at the cheapest possible price and returning the profits to the shareholders in the form of dividends.  Furthermore investors had low expectations for the growth of energy firms, so these stocks were priced modestly.  The low valuations combined with the high dividends contributed to superior investor returns.  (page 55)

Technology firms have experienced high earnings growth.  Yet investors have tended to expect even higher growth going forward than what subsequently occurs.  Thus we see again that investors systematically overvalue high-growth companies, which leads to returns that trail the S&P 500 Index.  Technology may be the best example of this phenomenon.  Technology companies have grown very fast, but investors have generally expected too much going forward.

The financial sector is another case of high growth but disappointing (or average) returns.  Much of the growth in financials has come from new companies joining the sector and being added to the index.  Siegel:

The tremendous growth in financial products has spurred the growth of many new firms.  This has caused a steady increase in the market share of the financial sector, but competition has kept the returns on financial stocks close to average over the whole period.  (page 58)

To conclude his discussion of the various sectors, Siegel writes:

The data show that three sectors emerge as long-term winners.  They are health care, consumer staples, and energy.  Health care and consumer staples comprise 90 percent of the twenty best-performing surviving fims of the S&P 500 Index.  These two sectors have the highest proportion of firms where management is focused on bringing quality products to the market and expanding brand-name recognition on a global basis.

The energy sector has delivered above-average returns despite experiencing a significant contraction of its market share.  The excellent returns in this sector are a result of two factors:  the relatively low growth expectations of investors (excepting the oil and gas extractors during the late 1970s) and the high level of dividends.  (page 66)



Siegel opens the chapter by remarking:

Economic growth is not the same as profit growth.  In fact, productivity growth can destroy profits and with it stock values.  (page 102)

Siegel continues:

Any individual or firm through its own effort can rise above the average, but every individual and firm, by definition, cannot.  Similarly, if a single firm implements a productivity-improving strategy that is unavailable to its competition, its profits will rise.  But if all firms have access to the same technology and implement it, then costs and prices will fall and the gains of productivity will go to the consumer.  (page 105)

Siegel notes that Buffett had to deal with this type of issue when he was managing Berkshire Hathaway, a textile manufacturer.  Buffett discussed plans presented to him that would improve workers’ productivity and lower costs:

Each proposal to do so looked like an immediate winner.  Measured by standard return-on-investment tests, in fact, these proposals usually promised greater economic benefits than would have resulted from comparable investments in our highly profitable candy and newspaper businesses.

Yet Buffett realized that the proposed improvements were available to all textile companies.  Buffett commented in his 1985 annual report:

[T]he promised benefits from these textile investments were illusory.  Many of our competitors, both domestic and foreign, were stepping up to the same kind of expenditures and, once enough companies did so, their reduced costs became the baseline for reduced prices industrywide.  Viewed individually, each company’s investment decision appeared cost-effective and rational; viewed collectively, the decisions neutralized each other and were irrational (just as happens when each person watching a parade decides he can see a little better if he stands on tiptoes).  After each round of investment, all the players had more money in the game and returns remained anemic.

Eventually, after a decade, Buffett realized he had to close the company.  (He had kept it open for a decade out of concern for the employees, and because management was doing an excellent job with the hand it was dealt.)  Siegel comments:

Buffett contrasts his decision to close up shop with that of another textile company that opted to take a different path, Burlington Industries.  Burlington Industries spent approximately $3 billion on capital expenditures to modernize its plants and equipment and improve its productivity in the twenty years following Buffett’s purchase of Berkshire.  Nevertheless, Burlington’s stock returns badly trailed the market.  As Buffett states, ‘This devastating outcome for the shareholders indicates what can happen when much brain power and energy are applied to a faulty premise.’  (page 106)

Siegel then draws a broader conclusion about technology:

Historical economic data indicate that the fruits of technological change, no matter how great, have ultimately benefited consumers, not the owners of firms.  Productivity lowers the price of goods and raises the real wages of workers.  That is, productivity allows us to buy more with less.

Certainly, technological change has transitory effects on profits.  There is usually a ‘first mover’ advantage.  When one firm incorporates a new technology that has not yet been implemented by others, profits will increase.  But as others avail themselves of this technology, competition ensures that prices will fall and profits will revert to normal.  (page 109)



Siegel quotes Peter Lynch:

As a place to invest, I’ll take a lousy industry over a great industry anytime.  In a lousy industry, one that’s growing slowly if at all, the weak drop out and the survivors get a bigger share of the market.  A company that can capture an ever-increasing share of a stagnant market is a lot better off than one that has to struggle to protect a dwindling share of an exciting market.

Many investors try to look for an industry with a bright future, and then select a company that will benefit from this growth.  As we’ve already seen, this doesn’t work in general because investors systematically overvalue high-growth companies.

A deep value investment strategy looks for companies at low multiples, with terrible performance.  These companies, as a group, have done much better than the market over time.

Although a deep value approach works well even if it is entirely quantitative – which is what Ben Graham, the father of value investing, often did – it can work even better if you can identify a winning management.  Siegel explains:

… some of the most successful investments of the last thirty years have come from industries whose performances have been utterly horrendous.

These companies have bucked the trend.  They all rose above their competitors by following a simple approach:  maximize productivity and keep costs as low as possible.  (page 113)

Siegel gives Southwest Airlines as an example.  Investors have lost more money in the airline industry than in any other.  But Southwest Airlines established itself as ‘the low-fare airline.’  It accomplished this by being the low-cost airline.  It offered only single-class service, with no assigned seats and no meals.  It only operated city-to-city where demand was high enough.  And it only used Boeing 737’s.  As a result of being the low-cost and low-fare airline, the business performed well and the stock followed.

Siegel also mentions the example of Nucor, which pioneered the use of ‘minimill’ technology and the recycling of scrap steel.  While the steel industry as a whole underperformed the market by close to 4 percent a year for thirty years, Nucor outperformed the market by over 5 percent a year over the same time period.  According to Jim Collins and others, at least 80 percent of Nucor’s success had to do with the leadership and culture of the company.  At Nucor, executives actually received fewer benefits than regular workers:

  • All workers were eligible to receive $2,000 per year for each child for up to four years of postsecondary education, while the executives received no such benefit.
  • Nucor lists all of its employees – more than 9,800 – in its annual report, sorted alphabetically with no distinctions for officer titles.
  • There are no assigned parking spots and no company cars, boats, or planes.
  • All employees of the company receive the same insurance coverage and amount of vacation time.
  • Everybody wears the same green spark-proof jackets and hard hats on the floor (in most integrated mills, different colors designate authority).

Siegel quotes Buffett:

It is the classic example of an incentive program that works.  If I were a blue-collar worker, I would like to work for Nucor.  (page 121)

In stark contrast, Bethlehem Steel had executives using the corporate fleet for personal reasons, like taking children to college or weekend vacations.  Bethlehem also renovated a country club with corporate funds, at which shower priority was determined by executive rank, notes Siegel.

Siegel concludes his discussion of Southwest Airlines and Nucor (and Wal-Mart):

The success of these firms must make investors stop and think.  The best-performing stocks are not in industries that are at the cutting edge of the technological revolution; rather, they are often in industries that are stagnant or in decline.  These firms are headed by managements that find and pursue efficiencies and develop competitive niches that enable them to reach commanding positions no matter what industry they are in.  Firms with these characteristics, which are often undervalued by the market, are the ones that investors should want to buy.  (pages 121-122)

Another great example of a company implementing a low-cost business strategy is 3G Capital, a Brazilian investment firm.  (3G was founded in 2004 by Jorge Paulo Lemann, Carlos Alberto Sicupira, and Marcel Herrmann Telles.)  3G is best known for partnering with Buffett’s Berkshire Hathaway for its acquisitions, including Burger King, Tim Hortons, and Kraft Foods.  When 3G acquires a company, they typically implement deep cost cuts.



Siegel explains what can happen to a dividend-paying stock during a bear market:  If the stock price falls more than the dividend, then the higher dividend yield can then be used to reinvest, leading to a higher share count than otherwise.  The Great Depression led to a 25-year period – October 1929 to November 1954 – during which stocks plunged and then took a long time to recover.  Most investors did not do well, often because they could not or did not hold on to their shares.  But investors with dividend-paying stocks who reinvested those dividends did quite well, as Siegel notes:

Instead of just getting back to even in November 1954, stockholders who reinvested their dividends (indicated as ‘total return’) realized an annual rate of return of 6 percent per year, far outstripping those who invested in either long- or short-term government bonds.  In fact, $1,000 invested in stocks at the market peak turned into $4,440 when the Dow finally recovered to its old high on that November day a quarter century later.  Although the price appreciation was zero, the $4,400 that resulted solely from reinvesting dividends was almost twice the accumulation in bonds and four times the accumulation in short-term treasury bills.  (page 140)

Siegel concludes:

There is an important lesson to be taken from this analysis.  Market cycles, although difficult on investors’ psyches, generate wealth for long-term stockholders.  These gains come not through timing the market but through the reinvestment of dividends.

Bear markets are not only painful episodes that investors must endure; they are also an integral reason why investors who reinvest dividends experience sharply higher returns.  Stock returns are generated not by earnings and dividends alone but by the prices that investors pay for these cash flows.  When pessimism grips shareholders, those who stay with dividend-paying stocks are the big winners.  (pages 141-142)

The same logic applies to individual stocks.  If a company is a long-term survivor (or leader), then short-term bad news causing the stock to drop will enhance your long-term returns if you’re reinvesting dividends.  This is also true if you’re dollar-cost averaging.

In theory, share repurchases when the stock is low can work even better than dividends because share repurchases create tax-deferred gains.  In practice, Siegel observes, management is often not as committed to a policy of share repurchases as it is to paying dividends.  Once a dividend is being paid, the market usually views a reduced dividend unfavorably.  Also, shareholders can track dividends more easily than share repurchases.  In sum, when a stock is low, it is usually better for shareholders if they can reinvest dividends instead of relying on management to repurchase shares.



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:

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

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

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


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

My e-mail:




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.

Emotions and Biases

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 9, 2017

Meir Statman, an expert in behavioral finance, has written a good book, What Investors Really Want (McGraw-Hill, 2011).

Here is my brief summary of the important points:



Statman argues that investments bring utilitarian benefits, expressive benefits, and emotional benefits.  The utilitarian benefits relate to being able to achieve financial goals, such as financial freedom or the ability to pay for the education of grandchildren.

Expressive benefits can convey to ourselves and others our values and tastes.  For instance, an investor is, in effect, saying, ‘I’m smart and can pick winning investments.’  Emotional benefits relate to how the activity makes you feel.  As Statman notes, Christopher Tsai said about his father Gerald Tsai, Jr. – a pioneer of the go-go funds in the 1960s:  “He loved doing transactions.  He loved the excitement of it.”

Statman tells the story of an engineer who learned that Statman is a professor of finance.  The engineer asked where he could buy the Japanese yen.  Statman asked him why, and the engineer said that the yen would zoom past the dollar based on macroeconomic fundamentals.  Statman replied:

Buying and selling Japanese yen, American stocks, French bonds, and all other investments is not like playing tennis against a practice wall, where you can watch the ball hit the wall and place yourself at just the right spot to hit it back when it bounces.  It is like playing tennis against an opponent you’ve never met before.  Are you faster than your opponent?  Will your opponent fool you by pretending to hit the ball to the left side, only to hit it to the right?  (page ix)

Later, Statman continues:

I tried to dissuade my fellow dinner guest from trading Japanese yen but I have probably failed.  Perhaps I failed to help my fellow dinner guest overcome his cognitive error, learn that trading should be framed as playing tennis against a possibly better player, and refrain from trading.  Or I might have succeeded in helping my fellow guest overcome his cognitive error and yet failed to dissuade him from trading because he wanted the expressive and emotional benefits of the trading game, the fun of playing and the thrill of winning.  (page xiii)

Statman explains that, in many fields of life, emotions are helpful in good decision-making.  Yet when it comes to areas such as investing, emotions tend to be harmful.

There is often a tension between what we should do and what we want to do.  And if we are stressed or fatigued, then it becomes even harder to do what we should do instead of what we want to do.

Moreover, our emotional reactions to changing stock prices generally mislead us.  When stocks are going up, we typically feel more confident and want to own more stocks.  When stocks are going down, we tend to feel less confident and want to own fewer stocks.  But this is exactly the opposite of what we should do if we want to maximize our long-term investment results.



Beat-the-market investors have always been searching for investments with returns higher than risks.  But such investments are much rarer than is commonly supposed.  For every investor who beats the market, another must trail the market.  And that is before fees and expenses.  After fees and expenses, there are very few investors who beat the market over the course of several decades.

Statman mentions a study of stock traders.  Those who traded the most trailed the index by more than 7 percent per year on average.  Those who traded the least trailed the index by only one-quarter of 1 percent.  Furthermore, a study of Swedish investors showed that heavy traders lose, on average, nearly 4 percent of their total financial wealth each year.



Framing means that people can react differently to a particular choice based on how it is presented.  Framing is everywhere in the world of investments.  Statman explains:

Some frames are quick and intuitive, but frames that come to mind quickly and intuitively are not always correct… The beat-the-market frame that comes to mind quickly and intuitively is that of tennis played against a practice wall, but the correct frame is tennis played against a possibly better player.  Incorrect framing of the beat-the-market game is one cognitive error that fools us into believing that beating the market is easy.  (page 18)

Statman has some advice for overcoming the framing error:

It is not difficult to overcome the framing error.  All we need to do is install an app in our minds as we install apps on our iPhones.  When we are ready to trade it would pipe in, asking, ‘Who is the idiot on the other side of the trade?  Have you considered the likelihood that the idiot is you?’  (page 21)

The broader issue (discussed below) is that most of us, by nature, are overconfident in many areas of life, including investing.  Overconfidence is the most widespread cognitive bias that we have.  Using procedures such as a checklist can help reduce errors from overconfidence.  Also, keeping a journal of every investment decision – what the hypothesis is, what the evidence is, and what ended up happening – can help you to improve over time, hopefully reducing cognitive errors such as overconfidence.



Heuristics are mental shortcuts that often work, but sometimes don’t.  There is a good discussion of the representativeness heuristic on Wikipedia:

Daniel Kahneman and Amos Tversky defined representativeness as:

the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated.

When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely.  The key issue is sample size versus base rate.

Many people mistakenly assume that a small sample – even as small as a single example – is representative of the relevant population.  This mistake is called the law of small numbers.

If you have a small sample, you cannot take it as representative of the entire population.  In other words, a small sample may differ significantly from the base rate.  If you have a large enough sample, then by the law of large numbers, you can conclude that the large sample approximates the base rate (the entire population).

For instance, if you flip a coin ten times and get 8 heads, you cannot conclude that flipping the same coin thousands of times will yield approximately 80% heads.  But if you flip a coin ten thousand times and get 5,003 heads, you can conclude that the base rate for heads is 50%.

If a mutual fund manager beats the market five years out of six, we conclude that it must be due to skill even though that is far too short a period for such a conclusion.  By randomness alone, there will be many mutual fund managers who beat the market five years out of six.



Our brains are good at finding patterns.  But when the data are highly random, our brains often find patterns that don’t really exist.

For example, there is no way to time the market.  Yet many investors try to time the market, jumping in and out of stocks.  Nearly everyone who tries market timing ends up trailing a simple index fund over time.

Part of the problem is that the brain only notices and remembers the handful of investors who were able to time the market successfully.  What investors should examine is the base rate:  Out of all investors who have tried market timing, how many have succeeded?  A very tiny percentage.



When our sentiment is positive, we expect our investments to bring returns higher than risk.  When our sentiment is negative, we expect our investments to bring returns lower than risk.

People expect the stocks of admired companies to do better than the stocks of spurned companies, but the opposite is true.  That’s a key reason deep value investing works:  on average, people are overly negative on out-of-favor or struggling companies, and people are overly positive on companies currently doing well.

People even expect higher returns if the name of a stock is easier to pronounce!

Finally, many investors think they can get rich from a new technological innovation.  In the vast majority of the cases, this is not true.  For every Ford, for every Microsoft, for every Google, for every Amazon, there are many companies in the same industry that failed.



A sense of control, like optimism, is generally beneficial, helping us to overcome challenges and feel happier.  A sense of control is good in most areas of life, but – like overconfidence – it is generally harmful in areas that involve much randomness, such as investing.

Statman explains:

A sense of control gained through lucky charms or rituals can be useful.  In a golfing experiment, some people were told they were receiving a lucky ball; others received the same ball and were told nothing.  Everyone was instructed to take ten putts.  Players who were told that their ball was lucky made 6.42 putts on average while those with the ordinary ball made only 4.75.  People in another experiment were asked to bring a personal lucky charm to a memory test.  Half of them kept the charm with them, but the charms of the other half were kept in another room.  People who had the charms with them reported that they had greater confidence that they would do well on the test than the people whose charms were kept away, and people who had the charms with them indeed did better on the memory test.

The outcomes of golf and memory tasks are not random; they are tasks that can be improved by concentration and effort.  A sense of control brought about by lucky charms or lucky balls can help improve performance if a sense of control brings real control.  But no concentration or effort can improve performance when outcomes are random, not susceptible to control, as is often true in much of investing and trading.  (page 50)

Statman describes one experiment involving traders who saw an index move up or down.  The task was to raise the index as much as possible by the end of each of four rounds.  Traders were also told that three keys on their keyboard have special effect.

In truth, movements in the index were random and the three keys had no effect on outcomes.  Any sense of control was illusory.  Still, some traders believed that they had much control while others believed that they had little.  It turned out that the traders with the highest sense of control displayed the lowest level of performance.  (page 51)



Statman also discusses cognitive biases.  He remarks that cognitive biases affect each one of us slightly differently.  Some may fall prey to hindsight bias more often.  Some have more trouble with availability.  Others may be more overconfident, and so forth.

Before examining some cognitive biases, it’s worth briefly reviewing Daniel Kahneman’s definition of two different mental systems that we have:

System 1:   Operates automatically and quickly;  makes instinctual decisions based on heuristics.

System 2:   Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including complex computations involving logic, math, or statistics.

Kahneman writes – in Thinking, Fast and Slow – that System 1 and System 2 usually work quite well together:

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.

Yet in some circumstances – especially if a good judgment requires complex computations such as logic, math, or statistics – System 1 has cognitive biases, or systematic errors that it tends to make.

The systematic errors of System 1 happen predictably in areas such as investing or forecasting.  These areas involve so much randomness that the intuitive statistics of System 1 lead predictably and consistently to errors.



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

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

Statman comments on the availability bias and on the near-miss effect:

Availability errors compound representativeness errors, misleading us further into the belief that beating the market is easy.  Casinos exploit availability errors.  Slot machines are quiet when players lose, but they jingle cascading coins when players win.  We exaggerate the likelihood of winning because the loud voice of winning is available to our minds more readily than the quiet voice of losing… Scans of the brains of gamblers who experience near-misses show activation of a reward-related brain circuitry, suggesting that near-misses increase the transmission of dopamine.  This makes gambling addiction similar to drug addiction.  (page 29)

Statman pens the following about mutual fund marketing:

Mutual fund companies employ availability errors to persuade us to buy their funds.  Morningstar, a company that rates mutual funds, assigns to each fund a number of stars that indicate its relative performance, one star for the bottom group, three stars for the average group, and five stars for the top group.  Have you ever seen an advertisement for a fund with one or two stars?  But we’ve all seen advertisements for four- and five-star funds.  Availability errors lead us to judge the likelihood of finding winning funds by the proportion of four- and five-start funds available to our minds.  (page 29-30)



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

Confirmation bias makes it quite difficult for many of us to improve upon or supplant our existing beliefs or hypotheses.  This bias also tends to make most of us overconfident about our existing beliefs or hypotheses, since all we can see are supporting data.

It’s clear that 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 our 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 we 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, Thinking, Fast and Slow)

Thus, the habit of always looking for disconfirming evidence of our hypotheses – especially our best-loved hypotheses (Charlie Munger’s term) – 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 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, 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.  (my emphasis)

As Statman states:

Confirmation errors contribute their share to the perception that winning the beat-the-market game is easy.  We commit the confirmation error when we look for evidence that confirms our intuition, beliefs, claims, and hypotheses, but overlook evidence that disconfirms them… The remedy for confirmation errors is a structure that forces us to consider all the evidence, confirming and disconfirming alike, and guides us to tests that tell us whether our intuition, beliefs, claims, or hypotheses are confirmed by the evidence or disconfirmed by it.

One manifestation of confirmation errors is the tendency to trim disconfirming evidence from stories… The fact that a forecast of an imminent stock market crash was made years before its coming is unappetizing, so we tend to trim it off our stock market stories.  (page 31)



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

This is a very powerful bias that we have.   Because we view the past as much more predictable than it actually was, we also view the future as much more predictable than it actually is.

Hindsight bias is also called the knew-it-all-along effect or creeping determinism.  (See:

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)

Statman elucidates:

So, if an introverted man marries a shy woman, it must be because, as we have known all along, ‘birds of a feather flock together’ and if he marries an outgoing woman, it must be because, as we have known all along, ‘opposites attract.’  Similarly, if stock prices decline after a prolonged rise, it must be, as we have known all along, that ‘trees don’t grow to the sky’ and if stock prices continue to rise, it must be, as we have equally known all along, that ‘the trend is your friend.’  Hindsight errors are a serious problem for all historians, including stock market historians.  Once an event is part of history, there is a tendency to see the sequence that led to it as inevitable.  In hindsight, poor choices with happy endings are described as brilliant choices, and unhappy endings of well-considered choices are attributed to horrendous choices.  (page 33)

Statman later writes about Warren Buffett’s understanding of hindsight bias:

Warren Buffett understands well the distinction between hindsight and foresight and the temptation of hindsight.  Roger Lowenstein mentioned in his biography of Buffett the events surrounding the increase in the Dow Jones Industrial Index beyond 1,000 in early 1966 and its subsequent decline by spring.  Some of Buffett’s partners called to warn him that the market might decline further.  Such calls, said Buffett, raised two questions:

If they knew in February that the Dow was going to 865 in May, why didn’t they let me in on it then; and

If they didn’t know what was going to happen during the ensuing three months back in February, how do they know in May?

Statman concludes:  We will always be normal, never rational, but we can increase the ratio of smart normal behavior to stupid normal behavior by recognizing our cognitive errors and devising methods to overcome them.

One of the best ways to minimize errors from cognitive bias is to use a fully automated investment strategy.  A low-cost broad market index fund will allow you to beat at least 90% of all investors over several decades.  If you adopt a quantitative value approach, you can do even better.



Overconfidence is such as widespread cognitive bias among people that Kahneman devotes Part 3 of his book, Thinking, Fast and Slow, 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)

As Statman describes:

Investors overestimate the future returns of their investments relative to the returns of the average investor.  Investors even overestimate their past returns relative to the returns of the average investor.  Members of the American Association of Individual Investors overestimated their own investment returns by an average of 3.4 percentage points relative to their actual returns, and they overestimated their own returns relative to those of the average investor by 5.1 percentage points.  The unrealistic optimism we display in the investment arena is similar to the unrealistic optimism we display in other arenas.  (page 45)

Statman also warns that stockbrokers and stock exchanges have good reasons to promote overconfidence because unrealistically optimistic investors trade far more often.



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

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

As with the other cognitive biases, often self-attribution bias makes us happier and stronger.  But we have to learn to slow ourselves down and take extra care in areas – like investing – where overconfidence will hurt us.



In Behavioural Investing (Wiley, 2007), 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)

Here are the results:

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.



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.

Thanks to evolution, System 1 is usually right when it assumes causality.  For example, there was movement in the grass, probably caused by a predator, so run.  And even in the modern world, as long as cause-and-effect is straightforward and not statistical, System 1 is amazingly 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.  (Kahneman)

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 (based on current knowledge).  In these areas, System 1 is often very wrong when it creates coherent stories or makes predictions.  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.



anchoring effect:   we 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.

Kahneman and 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 ran his own experiment on anchoring.   People were asked to write down the last four digits of their phone number.   Then they were 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 reported 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 doctors.  (Behavioural Investing, 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)



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:

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

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

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


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

My e-mail:




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.

Our Process and Vision

(Image:  Zen Buddha Silence by Marilyn Barbone.)

April 2, 2017

If you’re investing small sums, you can earn the highest returns by focusing on microcap stocks.  That’s why many top value investors started in micro caps.  For instance, Warren Buffett concentrated on micro caps when he managed his partnership starting in 1957, which produced the highest returns of his career.  And Buffett has repeatedly said that in today’s market, he could get 50% per year if he could invest in micro caps.

Look at 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


(CRSP is the Center for Research in Security Prices at the University of Chicago.  You can find the data for various deciles here:

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, the annual returns from microcap stocks 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, versus 11% per year for an equal weighted large-cap approach.

Still, if you can do 3% better per year than the S&P 500 Index (on average) – even with only a part of your total portfolio – that really adds up after a couple 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.



By adding a value screen to a microcap strategy, it is possible to add at least 2-3% per year.  There are several ways to measure cheapness, such as low EV/EBIT, low P/E, and low P/CF.

Tobias Carlisle and Wesley Gray tested these and other measures of cheapness from 1964 to 2011 – see Quantitative Value (Wiley, 2013).  They found that EV/EBIT outperformed all the other measures of cheapness.

Furthermore, Carlisle and Gray tested simple EV/EBIT, based on trailing one-year figures, against various combinations (including multi-year).  Simple EV/EBIT was still the best performer.



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:



In sum, over the course of several decades, a systematic value strategy – applied to cheap microcap stocks with improving fundamentals – has high odds of returning at least 7-9% more per year than a low-cost S&P 500 Index fund.



An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.

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

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

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



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

My e-mail:



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.

How To Master Yourself As An Investor

(Image:  Zen Buddha Silence by Marilyn Barbone.)

March 12, 2017

James Montier, an expert in behavioral finance, has written several excellent books, including The Little Book of Behavioral Investing: How Not to Be Your Own Worst Enemy (Wiley, 2010).

Montier begins with a quote from the father of value investing, Ben Graham, who clearly understood (decades before behavioral finance was invented) that overcoming your own emotions is often the greatest challenge for an investor:

The investor’s chief problem – and even his worst enemy – is likely to be himself.

The problem, as Daniel Kahneman observes in his great book Thinking, Fast and Slow, is that we can easily recognize cognitive errors in others, but not in ourselves.  Yet we all suffer from cognitive biases.

If you can learn to recognize when your brain may make a cognitive error and if you can learn systems to overcome that, then you can do much better as an investor.  Here are three systems that can help you to minimize the impact of cognitive biases in order to maximize your long-term investment results:

  • Invest in low-cost broad market index funds. If you do this, then you’ll do better than 90% of all investors after several decades.  Also, this approach takes very little time to implement and maintain.
  • Invest in a quantitative value fund (or in several such funds). This is a fund that automatically buys the statistically cheapest stocks, year in and year out.  If done properly, this approach should do better than broad market index funds over time.  One of the most successful quantitative value investors is LSV Asset Management.  See:
  • Do value investing on your own. If you really enjoy learning about various businesses and if you enjoy the process of value investing, then with the right systems, you can learn to do well over time.  As an individual investor, you can look at tiny stocks overlooked by most professionals.  Also, you can easily concentrate your portfolio if you happen to come across any extremely cheap stocks.  (A young Warren Buffett once put his entire personal portfolio into one stock: GEICO.)

Note that the first two systems involve fully automated investing, which drastically reduces the number of decisions you make and therefore almost entirely removes the possibility of cognitive error.  The vast majority of investors are much better off using a fully automated approach, whether index funds or quantitative value funds.

(There are some actively managed value funds whose managers possess great skill, but usually it’s not possible for an ordinary investor to invest with them.  Also, many of these funds have gotten quite large, so performance over the next decade or two will likely be noticeably lower.)



It is now broadly recognized that there are two different systems operating in the human brain:

System 1:  Operates automatically and quickly; makes instinctual decisions based on heuristics.

System 2:  Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including logic, statistics, and complex computations.

As Daniel Kahneman points out (see Thinking, Fast and Slow), System 1 is amazingly good at what it does.  Its models of familiar situations are accurate.  Its short-term predictions are usually accurate.  And its initial reactions to challenges are swift and generally appropriate.  System 1 does all of these things automatically, and without needing any help from System 2.

The problem is that there are some situations in modern life – especially if a good decision requires the proper use of logic or statistics – where System 1 suffers from cognitive biases, causing systematic errors.  Most people, even if highly educated, rely largely on System 1.

System 2 can be trained over time to do logic, math, and statistics.  But in the presence of high levels of stress or fatigue, System 2 can easily be derailed or not activated at all.  Self-aware people recognize this, and can learn countermeasures such as meditation (which can dramatically reduce stress) or postponing important decisions (if possible).

To show how most people rely on System 1, even for math and logic, Montier describes the Cognitive Reflection Test (CRT), invented by Shane Frederick.

If you’re reading this, try answering these three questions:

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

Montier writes that only 17% out of 3,500 people tested by Frederick got all three questions right.  33% got none right!  Even among the best performing group – MIT students – only 48% managed to get all three right.

Montier gave the CRT to 600 professional investors.  Only 40% got all three right.  10% didn’t get any right.

Not doing well on the CRT is correlated with many behavioral errors, says Montier.  Doing well in investing is more about temperament and rationality than it is about IQ.  Montier quotes Buffett:

Success in investing doesn’t correlate with IQ once you’re above the level of 125.  Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.

It takes years of work to improve as an investor.  But the great thing is that you can improve at investing your entire life.  All the knowledge and experience is cumulative, as Buffett says.



The empathy gap is our inability to predict how we will behave while under emotional strain.  In the world of investing, many react emotionally to negative news or to falling stock prices, which leads to poor decisions.

In order to prevent ourselves from making poor decisions, we need to pre-commit to a plan, or (even better) follow a fully automated investment strategy.

Sir John Templeton explains how to do well as an investor:

The time of maximum pessimism is the best time to buy, and the time of maximum optimism is the best time to sell.

To do this consistently, we need to pre-commit to a plan or use fully automated investing.  System 1 reacts to dropping prices with fear and loss aversion.  Fear causes people to ignore great bargains when stock prices are plummeting.  And fearful people become even more fearful if they have already suffered from losses.  Many investors just say, ‘Get me out of the market [or the stock] at any price’ in order to relieve their pain.  But what they should be doing is buying (or holding) the cheapest stocks available.

In order to overcome his own cognitive biases, John Templeton pre-committed to buying cheap stocks during a bear market: he placed buy orders for many stocks well below their current prices.

Typically during a bear market, the investors who have large cash positions fail to invest until after they’ve already missed much of the market recovery.  Again, only by having pre-committed to a battle plan before a bear market can you increase the odds of buying when you should be buying – when stocks are cheap.  As Jeremy Grantham wrote (quoted by Montier):

There is only one cure for terminal paralysis: you absolutely must have a battle plan for reinvestment and stick to it.  Since every action must overcome paralysis, what I recommend is a few large steps, not many small ones….

It is particularly important to have a clear definition of what it will take for you to be fully invested.  Without a similar program, be prepared for your committee’s enthusiasm to invest (and your own for that matter) to fall with the market.  You must get them to agree now – quickly before rigor mortis sets in… Finally, be aware that the market does not turn when it sees light at the end of the tunnel.  It turns when all looks black, but just a subtle shade less black than the day before.

The legendary value investor Seth Klarman describes a bear market as follows (quoted by Montier):

The chaos is so extreme, the panic selling so urgent, that there is almost no possibility that sellers are acting on superior information; indeed, in situation after situation, it seems clear that investment fundamentals do not factor into their decision making at all… While it is always tempting to try and time the market and wait for the bottom to be reached (as if it would be obvious when it arrived), such a strategy proved over the years to be deeply flawed.  Historically, little volume transacts at the bottom or on the way back up and competition from other buyers will be much greater when the markets settle down and the economy begins to recover.  Moreover, the price recovery from a bottom can be very swift.  Therefore, an investor should put money to work amidst the throes of a bear market, appreciating that things will likely get worse before they get better.

So a battle plan for reinvestment is a “schedule of pre-commitments” that can help you overcome the fear induced by plummeting prices.  Such a plan will require that you buy stocks when they are cheap.  Such a plan will require that you average down when stocks continue to get cheaper.

Although this discussion has focused mostly on a broad market sell-off, sometimes there are sell-offs (for non-fundamental, short-term reasons) in particular industries or in individual stocks.  In these cases, too, having a battle plan can help you to ignore the crowd – which often is focused only on the short term – and buy the stocks that are likely the cheapest based on long-term fundamentals.



The majority of drivers say they are above average.  Most students say they will finish in the top half of their class.  Indeed, overconfidence is the pattern for nearly all human activities.  Montier asked 600 professional fund managers how many were above average, and 74% said they were.

Earlier I mentioned Shane Frederick’s CRT test.  People who ace that test tend to suffer from fewer cognitive biases.  However, this does not apply to overconfidence, which is a deep-seated and very wide-spread cognitive bias.  Nearly everyone suffers from overconfidence, although some people learn to tame it.

The trouble is that knowing a great deal about overconfidence does not remove the bias.  Daniel Kahneman, one of the best psychologists, especially when it comes to cognitive biases such as overconfidence, admits that he himself is still “wildly overconfident” as a default setting.  Unless there is a serious threat – a possible predator in the grass – our System 1 automatically makes us feel at ease and overconfident about nearly everything.

Now, in most spheres of human activity, overconfidence and optimism are actually good things.  Overconfident and optimistic people deal with problems better, are happier, and live longer.  Perhaps we developed overconfidence a long time ago when hunting.  Fearless hunters were generally better hunters.

The problem is that in fields (like investing) that require the proper use of logic or statistics, overconfidence leads to poor decisions and often disasters.

There are several ways that we can overcome overconfidence.  Committing to a fully automated approach to investing is the best way to minimize all cognitive biases.

  • Buying and holding a low-cost broad market index fund will allow you to do better than 90% of all investors over several decades.
  • A quantitative value strategy can be even better than an index fund. Such an approach automatically buys the statistically cheapest stocks.

If you want to do value investing yourself because you enjoy it, then using a checklist and other specific processes can greatly improve your results over time.  Guy Spier has outstanding discussions of systems and processes in The Education of a Value Investor.  See:

Montier also mentions some good questions you should ask if you’re doing value investing for yourself.  “Must I believe this?” is a better question than “Can I believe this?”  In other words, what would invalidate the investment hypothesis?

It’s important to understand that System 1 automatically looks for confirming evidence for all of its views.  Even more importantly, System 2 uses a positive test strategy, which means that System 2 also looks for confirming evidence rather than disconfirming evidence.

Yet we know that the advance of science depends directly on the search for disconfirming evidence.  Charles Darwin, for instance, trained himself always to look for disconfirming evidence.  In this careful, painstaking manner, Darwin, though not at all brilliant, was eventually able to produce some of the finest mental work ever done.  The bottom line for investors is simple:

Always look for disconfirming evidence rather than for confirming evidence.

Many of the best value investors have developed the habit of always looking for disconfirming evidence.  If you look the investment management firms run by Seth Klarman, Richard Pzena, and Ray Dalio – to give just a few examples out of many – there are processes in place to ensure that disconfirming evidence is ALWAYS sought.  For instance, if one analyst is responsible for the bullish case on a stock, another analyst will be assigned to argue the bearish case.  Investors who have a deeply ingrained process of searching for disconfirming evidence have generally enjoyed a boost to their long-term performance as a result.

Montier also writes that “Why should I own this investment?” is a better question than “Why shouldn’t I own this investment?”  The default should be non-ownership until the investment hypothesis has been formed and tested.  One major reason Buffett and Munger are among the very best investors is because of their extreme discipline and patience.  They are super selective, willing to pass on a hundred possibilities in order to find the one investment that is the most supported by the evidence.  (This was even more true when they were managing less capital.  These days, they have so much capital – as a result of their great successes over time – that they have to overlook many of the best opportunities, which involve companies too tiny to move the needle at Berkshire Hathaway.)



By nature, we tend to like people who sound confident.  Financial commentators typically sound very confident on TV.  But when it comes to financial forecasting, no one can do it well.  Some forecasters will be right on occasion, but you never know ahead of time who that will be.  And six months from now, some other forecasters – different ones – will be right.

The best thing, as strongly advised by many top investors including Warren Buffett, is to ignore all financial forecasts.  Either stick with a fully automated investment program – whether index fund or quantitative value – or stay focused on individual companies if you’re doing value investing for yourself.

If you’re doing value investing yourself, then even when the broader market is overvalued, you can usually find tiny companies that are extremely cheap.  Most professional investors never look at microcap stocks, which is why it’s usually the best place to look for the cheapest stocks.

Warren Buffett has never relied on a financial forecast while creating the greatest 57-year (and counting) track record of all time:

I have no use whatever for projections or forecasts.  They create an illusion of apparent precision.  The more meticulous they are, the more concerned you should be.

I make no effort to predict the course of general business or the stock market.  Period.

Anything can happen anytime in markets.  And no advisor, economist, or TV commentator – and definitely not Charlie nor I – can tell you when chaos will occur.  Market forecasters will fill your ear but will never fill your wallet.

Similarly, the great investor Peter Lynch never relied on financial forecasts:

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.

The way you lose money in the stock market is to start off with an economic picture. I also spend fifteen minutes a year on where the stock market is going.

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:

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.

Furthermore, as noted by Montier (page 46), when people are told that someone is an expert, they partially switch off the part of the brain associated with logic and statistics.  (This has been shown in experiments.)



There are still quite a few investors who continue to listen to financial forecasters, despite the fact that financial forecasts on the whole do not add value.  There are several cognitive errors happening here.  Many investors are governed mostly by emotions.  They watch the daily fluctuations of stock prices – which have no meaning at all for long-term investors (except when they are low enough to create a buying opportunity).  They listen to financial experts giving mostly useless forecasts.  And they anchor on various forecasts.

Part of the problem is that many people will believe or do almost anything if it comes from a perceived authority.

Stanley Milgram ran a famous experiment about authority because he was trying to understand why so many previously good people decided to commit evil during WWII by joining the Nazi’s.

In Milgram’s experiment, subjects were told they would administer electric shocks to a “learner.”  If the learner answered a question incorrectly, the subject was told to deliver the shock.  The subjects met the learners before the experiment, but the learners were in a separate room during the experiment.  The subjects could hear the learners, however, as they answered questions and as they were shocked.  Behind the subject stood an authority figure wearing a white lab coat, carrying a clipboard, and telling the subject when to give the shock.

Before running the experiment, Milgram asked a panel of 40 psychiatrists what they thought the subjects would do.  The psychiatrists thought that only 1% of the subjects would go to the maximum shock level (450 volts with labels of “extreme danger” and “XXX”).  After all, they reasoned, the subjects were Americans and Americans wouldn’t do that, right?

100% of the subjects went up to 135 volts (at which point the learner is asking to be released).  80% of the subjects were willing to go up to 285 volts (at which point all they could hear were screams of agony).  And more than 62% of the subjects were willing to administer the maximum 450 volts (despite the labels of “extreme danger” and “XXX”) – at which point there was no sound at all from the learner.

Milgram ran several variants of his experiment, but the results were essentially the same.



A major reason why financial forecasters continue to forecast, despite adding no value on the whole, is simply that there are enough fearful people who need to hear financial forecasts.

But you might wonder: If a forecaster has been wrong so often, how can that person continue to forecast, even when there is a demand for it?

Montier discusses a landmark, 20-year study done by the psychologist Philip Tetlock.  (See Expert Political Judgment: How Good Is It? How Can We Know?)  Tetlock gathered over 27,000 expert predictions, and he found that they were little better than pure chance.

Tetlock also found that forecasters tend to use the same excuses over and over.  The five most common excuses for failed forecasts were the following:

The “If only” defense – If only the Federal Reserve had raised rates, then the prediction would have been true.  Effectively, the experts claim that they would have been correct if only their advice had been followed.

The ceteris paribus” defense – Something outside of the model of analysis occurred, which invalidated the forecast; therefore it isn’t my fault.

The “I was almost right” defense – Although the predicted outcome didn’t occur, it almost did.

The “It just hasn’t happened yet” defense – I wasn’t wrong, it just hasn’t occurred yet.

The “Single prediction” defense – You can’t judge me by the performance of a single forecast.

How can forecasters continue to be confident in their forecasts after years or decades of mostly being wrong?  Typically, they invoke one of the five defenses just listed.  As a result, most forecasters maintain just as high a level of overconfidence, even after a long string of mostly incorrect forecasts.



A fully automated deep value investing strategy will probably yield excellent results over time (especially if focused on microcap stocks), when compared with most other investment strategies.  If you have doubts about that, then low-cost broad market index funds are a great choice for your long-term investing.

If you’re picking individual stocks using a value investing approach, then the key is to be a learning machine.  And there are many books you should read, including all the books I’ve written about previously on this weekly blog.

There’s one book in particular that I recommend because it essentially uses Ben Graham’s conservative approach to picking individual stocks.  The book is Value Investing: From Graham to Buffett and Beyond (Wiley, 2004), by Professor Bruce Greenwald (and others).

If you could forecast the future free cash flows of a given company, and if you had a reasonable estimate of the discount rate, then you could value that company.  The problem with this DCF approach is that very tiny changes in certain variables, like long-term growth rates or the discount rate, can dramatically change the intrinsic value estimate.

Greenwald explains two approaches that depend entirely on the currently known facts about the company:

  • Net asset value
  • Earnings power value

Many value investors over the years have done extraordinarily well by estimating net asset value and then buying below that value.  Peter Cundill, for example, is a great value investor who always focused on buying below liquidation value.  See:

Other value investors have done extremely well by estimating the earnings power value – or normalized earnings – and consistently buying below that level.

Often the earnings power value of a company will exceed its net asset value.  So in a sense, net asset value is a conservative estimate of intrinsic value, while earnings power value may represent potential upside.  But even with earnings power value, there is zero forecasting involved.  There is no estimate of future growth.  This is especially helpful for value investors because the studies have shown that the stocks of ‘high-growth’ companies underperform, while the stocks of ‘low-growth’ or ‘no-growth’ stocks outperform.  See:



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:

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

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

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


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

My e-mail:




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 Ugliest Stocks Are the Best Stocks

(Image:  Zen Buddha Silence by Marilyn Barbone.)

March 5, 2017

Quantitative value investor Tobias Carlisle has written an excellent book entitled Deep Value: Why Activists and Other Contrarians Battle for Control of LOSING Corporations (Wiley, 2014).

The book has many important and counterintuitive lessons for quantitative value investors.  This blog post is a brief summary of some chief lessons.



Companies with low growth or no growth that are trading at cheap valuations significantly outperform companies with high growth.  In other words, as a group, companies that have been doing terribly and that are trading at cheap prices – seemingly justifiably – do much better than companies that have been doing well and growing fast.

It’s important to note that these findings apply to groups of stocks, not individual stocks.  Cheap value stocks, as a group (and as a portfolio), significantly outperform companies that have been doing well and whose stocks have been doing well.  Moreover, in states of the world including bear markets and recessions, value stocks do better than growth stocks.  So value stocks are less risky as a group than growth stocks.

On an individual level, a value stock is riskier than an average stock.  Whereas an average stock has a 50% chance of underperforming the market, a value stock – if it is just chosen based on cheapness alone, without additional criteria – has a greater than 50% chance of underperforming the market.  But as a group (and as a portfolio), value stocks – when compared to either growth stocks or average stocks – are less risky and perform better over time.

If you are following a deep value approach, there are additional criteria that you can apply in the stock selection process to reduce the percentage of deep value stocks that underperform the market.  One example is the Piotroski F-Score, which identifies companies that show improving fundamentals (e.g., increased cash flows or reduced debt levels).  Joseph Piotroski came up with the F-Score because he discovered that while value stocks outperform as a group, there are many individual value stocks dragging down the overall performance of the value portfolio.

In sum, a portfolio of ugly value stocks far outperforms the market over time.  Remarkably, this already significant outperformance can be noticeably improved by using the Piotroski F-Score to cut off the left tail of the return distribution.  See:



If you only look at value stocks, which as a group outperform, doesn’t it make sense to focus on the cheap stocks where the companies have been doing well – in terms of growth – rather than the cheap stocks where the companies have been doing terribly?  No.  Carlisle comments:

This is a fascinating finding.  Intuitively, we are attracted to high growth and would assume that high-growth value stocks are high-quality stocks available at a bargain price.  The data show, however, that the low- or no-growth value stocks are the better bet.  It seems that the uglier the stock, the better the return, even when the valuations are comparable.  (page 133)

This same logic also applies to excellent, A+ companies versus unexcellent, D companies.  Carlisle again:

Buying well-run companies with good businesses seems to make so much sense.  Buying well-run companies with good businesses at bargain prices seems to make even more sense.  The research shows, however, that the better investment – rather than the better company – is the value stock, the scorned, the unexcellent, the Ds, the loss-making net nets.  And the better value stock, according to Lakonishok, Shleifer, and Vishny’s research, is the low- or ­no-growth value stock, what they describe as ‘contrarian value,’ and what I regard as deep value; the ugliest of the ugly.  (page 140)

Link to the famous 1994 paper by Josef Lakonishok, Andrei Schleifer, and Robert Vishny:



Investing in general is difficult to do effectively, which is why Warren Buffett advises most people to invest in low-cost index funds.

Deep value investing can be even more difficult because it requires consistently buying what everyone else hates – the ugliest, the worst, the cheapest stocks available.  Deep value stocks are almost always facing enormous business problems, and quite often the industries in which deep value stocks are found are doing horribly (for example, oil-related stocks with the oil price down from over $100 a barrel to below $30 or $40 a barrel).

Thus, the best way for most investors to benefit from deep value stocks is to use a quantitative (statistical) approach.  Carlisle explains:

Most deeply undervalued, fundamentally weak stocks are that way because their futures appear uncertain – they are losing money or marginally profitable – and, on an individual basis, don’t appear to be good candidates for purchase.  We know, however, that in aggregate they provide excellent returns, outperforming the market in the long run and suffering fewer down years than the market.  This is an area where our native intuition fails us.  As we have seen, no matter how well trained we are, humans tend to have difficulty with probabilistic, uncertain, and random processes… Since the 1950s, social scientists have been comparing the predictive abilities of traditional experts and what are known are statistical prediction rules.  The studies have found almost uniformly that statistical prediction rules are more consistently accurate than the very best experts.  (page 141)

I wrote about this here:

The conclusion is that, for a surprisingly wide range of prediction problems – including investing – statistical prediction rules are more reliable than human experts.  Many people have objected that experts could do better than simple statistical prediction rules if they had the ability to override the rule in specific cases.  But this turns out not to be true.  The statistical prediction rules are a ceiling from which the expert detracts rather than a floor to which the expert adds.

As Daniel Kahneman explains so well in his book Thinking, Fast and Slow – see especially Part III – we humans are generally very overconfident about our ability to predict the future.  Philip Tetlock did a landmark, 20-year study of experts making political and economic predictions.  What Tetlock found based on more than 27,000 predictions over the course of two decades is that the experts were little better than chance.  See Tetlock’s Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, 2005).

People, especially experts, are simply way overconfident about their ability to predict many future events.  Even Kahneman himself, after spending most of his life studying overconfidence, admits that he is “wildly overconfident” by nature, just like most people.  Overconfidence is related to many cognitive biases that people have, especially hindsight bias:

Statistical Prediction Rules Applied to Deep Value Investing

If you don’t understand value investing – or if trailing the market for a couple of years would make you abandon a value strategy – then your best long-term investment is a low-cost broad market index fund.  Such an index fund will allow you to beat 85-90% of all investors over the course of several decades.  And it takes very little time to implement and maintain this approach.

If you understand value investing, then you should consider a quantitative value fund.  A quantitative value fund – like the Boole Microcap Fund – is a fund that systematically picks cheap stocks.  Typically, systematic stock selection is fully automated, thereby maximizing long-term results by minimizing human error.



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:

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

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

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


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

My e-mail:




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.

Best Performers: Microcap Stocks

(Image:  Zen Buddha Silence by Marilyn Barbone.)

February 26, 2017

Are you a long-term investor?  If so, are you interested in maximizing long-term results without taking undue risk?

Warren Buffett, arguably the best investor ever, has repeatedly said that most people should invest in a low-cost broad market index fund.  Such an index fund will allow you to do better than at least 90% of all investors, net of costs, after several decades.

Buffett has also said that you can do better than an index fund by investing in microcap stocks – as long as you have a sound method.  Take a look at 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

(CRSP is the Center for Research in Security Prices at the University of Chicago.  You can find the data for various deciles here:

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, the annual returns from microcap stocks 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, versus 11% per year for an equal weighted large-cap approach.

Still, if you can do 3% better per year than the S&P 500 Index (on average) – even with only a part of your total portfolio – that really adds up after a couple 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.



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.  If you would like to maximize the odds of achieving this additional margin of outperformance, then you should adopt a systematic, quantitative investment strategy.



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:



In sum, over the course of several decades, a systematic 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.



An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.

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

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

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


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

My e-mail:



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.


Are Humans Rational?

(Image:  Zen Buddha Silence by Marilyn Barbone.)

February 12, 2017

I’ve always deeply admired both great scientists and great artists.  Science and art – each in its own way – attempt to grasp the nature of ultimate reality, or at least the nature of reality as far as we currently can see it.  As Einstein wrote in his essay, “The World As I See It”:

The ideals which have lighted me on my way and time after time have given me new courage to face life cheerfully, have been Truth, Goodness, and Beauty.  Without the sense of fellowship with people of like mind, of preoccupation with the objective, the eternally unattainable in the field of art and science, life would have seemed to me empty.

The pursuit of truth, goodness, and beauty is really everything that makes life worthwhile.  (This includes, by definition, friendship and love.)

As a business person, I hasten to add that having the freedom to pursue one’s passion is obviously essential.  As science moves forward and as the world economy evolves, ever more people are gaining a measure of freedom (including better access to healthcare and education).  But there is still a long way to go.

What does this have to do with investing?  Only that the scientific study of human nature – including biology, genetics, psychology, neuroscience, economics, and sociology – is as fascinating as other areas in science, such as physics and astronomy.



When people make investment decisions, they are making decisions under uncertainty – decisions where there are unknown future states of the world.  Many economists have assumed that people make rational decisions under uncertainty.  Most of these economists have held that people always maximize their expected utility.  Maximizing expected utility means that people assign a probability and a reward to each possible future state, and then make a decision (e.g., an investment) that will maximize the expected utility.  Expected utility is just the sum of each probability times each reward.

Expected Utility Example:  Atwood Oceanics

The Boole Microcap Fund bought stock in Atwood Oceanics, Inc. (NYSE: ATW) at $6.46 in late January.  Currently, ATW is at roughly $11.70, but it is still probably quite cheap, at least from a 3- to 5-year point of view.

Here is how to apply the expected utility framework.  First note that under “normal” economic conditions, the market clearing price of oil is about $60-70.  A market clearing price just means a price where the daily supply of oil meets the daily demand for oil.  Let’s consider three scenarios, any of which could occur over the next 3 to 5 years, however long it takes for the oil markets to return to “normal”:

  • Oil at $55 and ATW earns $3.00 per share. Under this scenario, ATW may be worth $30 per share (a P/E of 10).
  • Oil at $65 and ATW earns $4.50 per share. Under this scenario, ATW may be worth $45 per share (a P/E of 10).
  • Oil at $75 and ATW earns $6.00 per share. Under this scenario, ATW may be worth $60 per share (a P/E of 10).

Assume that each scenario is equally likely, so there is a 33.3% chance for each scenario to occur.  To get the expected value of an investment in ATW today at $11.70, for each scenario we take the probability times the outcome (stock price).  Then we add those numbers together.  So we have $30 x .333 = $10, plus $45 x .333 = $15, plus $60 x .333 = $20.  So we expect ATW to hit $45 = $10 + $15 + $20, and currently we can buy the stock for $11.70.  That would be an expected return of almost 300% (or nearly double that if we were lucky enough to buy the stock at $6).

We could adjust the probabilities, oil prices, earnings, and P/E ratio’s for each scenario (as long as the probabilities add up to 100%).  We could also decide to use more (or less) than three scenarios.

A Few More Expected Utility Examples

An obvious business example of expected utility is whether a company should invest in Product A.  One tries to guess different scenarios (and their associated probabilities), perhaps including one that is highly profitable and another involving large losses.  One also must compare an investment in Product A with the next best investment opportunity.

In theory, we can apply the expected utility framework to nearly any situation where the future states are unknown, even life and death. 

I don’t think George Washington explicitly did an expected utility calculation.  But like many young men of his day, Washington was eager to serve in the military.  He had a couple of horses shot from under him.  And there were several occasions when bullets went whizzing right past his head (which he thought sounded “charming”).  In short, Washington was lucky to survive, and the luck of Washington has arguably reverberated for centuries.

Had Washington been able to do an expected utility calculation about his future war experience, it might have been something like this (crudely):

  • 25% chance of death (even when he was a general, Washington often charged to the front)
  • 75% chance of honor and wealth

A more realistic expected utility calculation – but not what Washington could have guessed – might have been the following:

  • 50% chance of death
  • 50% chance of great honor (due to great service to his future country) and wealth

(I’m oversimplifying in order to illustrate the generality of the expected utility framework.)

Von Neumann and Morgenstern

What’s fascinating is that the expected utility framework does a very good job describing how any rational agent should make decisions.  The genius polymath John von Neumann and the economist Oskar Morgenstern invented this framework.  (For some details on the most general form of the framework, see:

But ever since, many economists have assumed that people actually do make decisions with full rationality.  Economists have argued that rationalist economics is the best approximation for how humans behave.

Others scientists and observers have long suspected (or, indeed, realized) that people are NOT fully rational.  But it’s one thing to hold the view that much human behavior is irrational.  It’s another thing to prove human irrationality scientifically.

The psychologists Daniel Kahneman and Amos Tversky did hundreds of experiments over the course of decades of people making decisions under uncertainty.  What Kahneman and Tversky demonstrated conclusively is that most human beings are NOT fully rational when they make decisions under uncertainty.

Behavioral economics – based on the research of Kahneman, Tversky, and many others – is a fascinating field (and the reason I went to graduate school).  But it has not yet developed to the point where its models can predict a wide range of human behavior better than the rationalist economic models.  The rationalist economic models are still the best approximation for much human behavior.



In his great book Misbehaving: The Making of Behavioral Economics (W. W. Norton, 2015), Richard Thaler discusses the development of behavioral economics.  According to Kahneman, Richard Thaler is “the creative genius who invented the field of behavioral economics.”

Thaler defines “Econs” as the fully rational human beings that traditional economists have always assumed for their models.  “Humans,” on the other hand, are the actual people who make various decisions, including often less than fully rational decisions under uncertainty.

For this blog post, I will focus on Part VI (Finance, pages 203-253).  But first, a quotation Thaler has at the beginning of his book:

The foundation of political economy and, in general, of every social science, is evidently psychology.  A day may come when we shall be able to deduce the laws of social science from the principles of psychology.  – Vilfredo Pareto, 1906



Chicago economist Eugene Fama coined the term efficient market hypothesis, or EMH for short.  Thaler writes that the EMH has two (related) components:

  • the price is right – the idea is that any asset will sell for its intrinsic value.  If the rational valuation of a company – based on normalized earnings or net assets – is $100 million, then the company’s market cap (as reflected by its stock price) will be $100 million.
  • no free lunch– EMH holds that all publically available information is already reflected in current stock prices, thus there is no reliable way to “beat the market” over time.

NOTE:  If prices are always right, that means that assets can never be overvalued (there are no bubbles) or undervalued.

Thaler observes that finance did not become a mainstream topic in economics departments before the advent of cheap computer power and great data.  The University of Chicago was the first to develop a comprehensive database of stock prices going back to 1926.  After that, research took off, and by 1970 EMH was well-established.

Thaler also points out that the economist J. M . Keynes was “a true forerunner of behavioral finance.”  Keynes, who was also an investor, thought that animal spirits – emotions – played an important role in financial markets.

Keynes also thought that professional investors were playing an intricate guessing game, similar to picking out the prettiest faces from a set of photographs:

… It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest.  We have reached the third degree where we devote our intelligences to anticipating what average opinion expects average opinion to be.  And there are some, I believe, who practice the fourth, fifth, and higher degrees.



On average, investors overreact to recent poor performance for low P/E stocks, which is why the P/E’s are low.  And, on average, investors overreact to recent good performance for high P/E stocks, which is why the P/E’s are high.

Having said that, Thaler is quick to quote a warning by Ben Graham (the father of value investing) about timing: ‘Undervaluations caused by neglect or prejudice may persist for an inconveniently long time, and the same applies to inflated prices caused by overenthusiasm or artificial stimulus.’  Thaler gives the example of the late 1990s:  for years, Internet stocks just kept going up, while value stocks just kept massively underperforming.

According to Thaler, most academic financial economists overlooked Graham’s work:

It was not so much that anyone had refuted Graham’s claim that value investing worked;  it was more that the efficient market theory of the 1970s said that value investing couldn’t work.  But it did.  Late that decade, accounting professor Sanjoy Basu published a thoroughly competent study of value investing that fully supported Graham’s strategy.  However, in order to get such papers published at the time, one had to offer abject apologies for the results.  (page 221)

Thaler and his research partner Werner De Bondt came up with the following.  Suppose that investors are overreacting.  Suppose that investors are overly optimistic about the future growth of high P/E stocks, thus driving the P/E’s “too high.”  And suppose that investors are excessively pessimistic about low P/E stocks, thus driving the P/E’s “too low.”  Then subsequent high returns from value stocks and low returns from growth stocks represent simple reversion to the mean.  But EMH says that:

  • The price is right: Stock prices cannot diverge from intrinsic value
  • No free lunch: Because all information is already in the stock price, it is not possible to beat the market. Past stock prices and the P/E cannot predict future price changes

Thaler and De Bondt took all the stocks listed on the New York Stock Exchange, and ranked their performance over three to five years.  They isolated the worst performing stocks, which they called “Losers.”  And they isolated the best performing stocks, which they called “Winners.”  Writes Thaler:

If markets were efficient, we should expect the two portfolios to do equally well.  After all, according to the EMH, the past cannot predict the future.  But if our overreaction hypothesis were correct, Losers would outperform Winners.  (page 223)

What did they find?

The results strongly supported our hypothesis.  We tested for overreaction in various ways, but as long as the period we looked back at to create the portfolios was long enough, say three years, then the Loser portfolio did better than the Winner portfolio.  Much better.  For example, in one test we used five years of performance to form the Winner and Loser portfolios and then calculated the returns of each portfolio over the following five years, compared to the overall market.  Over the five-year period after we formed our portfolios, the Losers outperformed the market by about 30% while the Winners did worse than the market by about 10%.



In response to widespread evidence that ‘Loser’ stocks (low P/E) – as a group – outperform ‘Winner’ stocks, defenders of EMH were forced to argue that ‘Loser’ stocks are riskier as a group.

NOTE: On an individual stock basis, a low P/E stock may be riskier.  But a basket of low P/E stocks generally far outperforms a basket of high P/E stocks.  The question is whether a basket of low P/E stocks is riskier than a basket of high P/E stocks.  Furthermore, one can use other metrics such as low P/B (low price to book) and get the same results as the low P/E studies.

According to the CAPM (Capital Asset Pricing Model), the measure of the riskiness of a stock is its correlation with the rest of the market, or beta.  If a stock has a beta of 1.0, then its volatility is similar to the volatility of the whole market.  If a stock has a beta of 2.0, then its volatility is double the volatility of the whole market (e.g., if the whole market goes up or down by 10%, then this individual stock will go up or down by 20%).

According to CAPM, if the basket of Loser stocks subsequently outperforms the market while the basket of Winner stocks underperforms, then the Loser stocks must have high betas and the Winner stocks must have low betas.  But Thaler and De Bondt found the opposite.  Loser stocks (value stocks) were much less risky as measured by beta.

Eventually Eugene Fama himself, along with research partner Kenneth French, published a series of papers documenting that, indeed, both value stocks and small stocks earn higher returns than predicted by CAPM.  In short, “the high priest of efficient markets” (as Thaler calls Fama) had declared that CAPM was dead.

But Fama and French were not ready to abandon the EMH (Efficient Market Hypothesis).  They came up with the Fama-French Three Factor Model.  They showed that value stocks are correlated – a value stock will tend to do well when other value stocks are doing well.  And they showed that small-cap stocks are similarly correlated.

The problem, again, is that there is no evidence that a basket of value stocks is riskier than a basket of growth stocks.  And there is no theoretical reason to believe that value stocks, as a group, are riskier.

According to Thaler, the debate was settled by the paper ‘Contrarian Investment, Extrapolation, and Risk’ published in 1994 by Josef Lakonishok, Andrei Shleifer, and Robert Vishny.  This paper shows clearly that value stocks outperform, and value stocks are, if anything, less risky than growth stocks.  Lakonishok, Shleifer, and Vishny launched the highly successful LSV Asset Management based on their research.  Here is a link to the paper:

(Recently Fama and French have introduced a five-factor model, which includes profitability.  Profitability was one of Ben Graham’s criteria.)



If you held a stock forever, it would be worth all future dividends discounted back to the present.  Even if you sold the stock, as long as you held it for a very long time, the distant future sales price (discounted back to the present) would be a negligible part of the intrinsic value of the stock.  The stock price is really the present value of all expected future dividend payments.

Bob Shiller collected data on stock prices and dividends starting in 1871:

… for each year he computed what he called the ‘ex post rational’ forecast of the stream of future dividends that would accrue to someone who bought a portfolio of the stocks that existed at that time.  He did this by observing the actual dividends that got paid out and discounting them back to the year in question.  After adjusting for the well-established trend that stock prices go up over long periods of time, Shiller found that the present value of dividends was… highly stable.  But stock prices, which we should interpret as attempts to forecast the present value of dividends, are highly variable….  (231-232)

October 1987 provides yet another example of stock prices moving much more than fundamental values.  The U.S. stock market dropped more than 25% from Thursday, October 15, 1987 to Monday, October 19, 1987.  This happened in the absence of any important news, financial or otherwise.  Writes Thaler:

If prices are too variable, then they are in some sense ‘wrong.’  It is hard to argue that the price at the close of trading on Thursday, October 15, and the price at the close of trading the following Monday – which was more than 25% lower – can both be rational measures of intrinsic value, given the absence of news.



It’s very important to note (again) that although the assumption of rationality and the EMH have been demonstrated not to be true, behavioral economists have not invented a model of human behavior that can supplant rationalist economics.  Therefore, rationalist economics, and not behaviorist economics, is still the chief basis by which economists attempt to predict human behavior.

In other words, scientists must learn much more – genetics, neuroscience, psychology, etc. – in order to better predict human behavior.  But even then, individual human behavior (to say nothing of group behavior) may remain partly unpredictable for a long period of time.

At the sub-atomic level, most top physicists believe that a part of reality is inherently unpredictable.  However, it’s always possible that this “inherent unpredictability” is a result of current limitations in theoretical physics.  Once physicists make further advances, it’s at least possible that what is currently “inherently unpredictable” may turn out to be predictable after all.  Einstein may be right when it comes to sub-atomic physics: “God does not play dice.”

Similarly with human behavior.  What will be the state of genetics, neuroscience, psychology, physics, artificial intelligence, etc. one thousand years from today?  Human behavior may be largely predictable.  But it then seems to get paradoxical.  How could one accurately predict one’s own behavior all the time, given that one could choose to act differently from one’s own predictions?  Or, if only a super-advanced artificial intelligence could aggregate enough data to predict all the behavior of a human being, when would it be legal to do so?  And who would own the super-advanced artificial intelligence, or would it be a benevolent entity guarding the universe?

In any case, rationalist economic models may continue to be useful for a long time.  In fact, rationalist models, including game theory, may also be central to predicting how various artificially intelligent agents will compete against one another.



In an efficient market, the same asset cannot sell simultaneously for two different prices.  Thaler gives the standard example of gold selling for $1,000 an ounce in New York and $1,010 an ounce in London.  If transaction costs were small enough, a smart trader could buy gold in New York and sell it in London.  This would eventually cause the two prices to converge.

But there is one obvious example that violates this law of one price:  closed-end funds, which had already been written about by Ben Graham.

For an open-end fund, all trades take place at NAV (net asset value).  Investors can purchase a stake in an open-end fund on the open market, without there having to be a seller.  So the total amount invested in an open-end fund can vary depending upon what investors do.

But for a closed-end fund, there is an initial amount invested in the fund, say $100 million, and then there can be no further investments and no withdrawals.  A closed-end fund is traded on an exchange.  So an investor can buy partial ownership of a closed-end fund, but this means that a previous owner must sell that stake to the buyer.

According to EMH, closed-end funds should trade at NAV.  But in the real world, many closed-end funds trade at prices different from NAV (sometimes a premium and sometimes a discount).  This is an obvious violation of the law of one price.

Charles Lee, Andrei Shleifer, and Richard Thaler wrote a paper on closed-end funds in which they identified four puzzles:

  • Closed-end funds are often sold by brokers with a sales commission of 7%. But within six months, the funds typically sell at a discount of more than 10%.  Why do people repeatedly pay $107 for an asset that in six months is worth $90?
  • More generally, why do closed-end funds so often trade at prices that differ from the NAV of its holdings?
  • The discounts and premia vary noticeably across time and across funds. This rules out many simple explanations.
  • When a closed-end fund, often under pressure from shareholders, changes its structure to an open-end fund, its price often converges to NAV.

The various premia and discounts on closed-end funds simply make no sense.  These mispricings would not exist if investors were rational because the only rational price for a closed-end fund is NAV.

Lee, Shleifer, and Thaler discovered that individual investors are the primary owners of closed-end funds.  So Thaler et al. hypothesized that individual investors have more noticeably shifting moods of optimism and pessimism.  Says Thaler:

We conjectured that when individual investors are feeling perky, discounts on closed-end funds shrink, but when they get depressed or scared, the discounts get bigger.  This approach was very much in the spirit of Shiller’s take on social dynamics, and investor sentiment was clearly one example of ‘animal spirits.’  (pages 241-242)

In order to measure investor sentiment, Thaler et al. used the fact that individual investors are more likely than institutional investors to own shares of small companies.  Thaler et al. reasoned that if the investor sentiment of individual investors changes, it would be apparent both in the discounts of closed-end funds and in the relative performance of small companies (vs. big companies).  And this is exactly what they found upon doing the research.  The greater the discounts to NAV for closed-end funds, the more the returns for small stocks lagged (on average) the returns for large stocks.



Years later, Thaler revisited the law of one price with a Chicago colleague, Owen Lamont.  Owen had spotted a blatant violation of the law of one price involving the company 3Com.  3Com’s main business was in networking computers using Ethernet technology, but through a merger they had acquired Palm, which made a very popular (at the time) handheld computer the Palm Pilot.

In the summer of 1999, as most tech stocks seemed to double almost monthly, 3Com stock seemed to be neglected.  So management came up with the plan to divest itself of Palm.  3Com sold about 4% of its stake in Palm to the general public and 1% to a consortium of firms.  As for the remaining 95% of Palm, each 3Com shareholder would receive 1.5 shares of Palm for each share of 3Com they owned.

Once this information was public, one could infer the following:  As soon as the initial shares of Palm were sold and started trading, 3Com shareholders would in a sense have two separate investments.  A single share of 3Com included 1.5 shares of Palm plus an interest in the remaining parts of 3Com – what’s called the “stub value” of 3Com.  Note that the remaining parts of 3Com formed a profitable business in its own right.  So the bottom line is that one share of 3Com should equal the “stub value” of 3Com plus 1.5 times the price of Palm.

When Palm started trading, it ended the day at $95 per share.  So what should one share of 3Com be worth?  It should be worth the “stub value” of 3Com – the remaining profitable businesses of 3Com (Ethernet tech, etc.) – PLUS 1.5 times the price of Palm, or 1.5 x $95, which is $143.

Again, because the “stub value” of 3Com involves a profitable business in its own right, this means that 3Com should trade at X (the stub value) plus $143, so some price over $143.

But what actually happened?  The same day Palm started trading, ending the day at $95, 3Com stock fell to $82 per share.  Thaler writes:

That means that the market was valuing the stub value of 3Com at minus $61 per share, which adds up to minus $23 billion!  You read that correctly.  The stock market was saying that the remaining 3Com business, a profitable business, was worth minus $23 billion.  (page 246)

Thaler continues:

Think of it another way.  Suppose an Econ is interested in investing in Palm.  He could pay $95 and get one share of Palm, or he could pay $82 and get one share of 3Com that includes 1.5 shares of Palm plus an interest in 3Com.

Thaler observes that two things are needed for such a blatant violation of the law of one price to emerge and persist:

  • You need some traders who want to own shares of the now publicly traded Palm, traders who appear not to realize the basic math of the situation. These traders are called noise traders, because they are trading not based on real information (or real news), but based purely on “noise.”  (The term noise traders was invented by Fischer Black.  See:
  • There also must be something preventing smart traders from driving prices back to where they are supposed to be. After all, the sensible investor can buy a share of 3Com for $82, and get 1.5 shares of Palm (worth $143) PLUS an interest in remaining profitable businesses of 3Com.  Actually, the rational investor would go one step further:  buy 3Com shares (at $82) and then short an appropriate number of Palm shares (at $95).  When the deal is completed and the rational investor gets 1.5 shares of Palm for each share of 3Com owned, he can then use those shares of Palm to repay the shares he borrowed earlier when shorting the publicly traded Palm stock. This was a CAN’T LOSE investment.  Then why wasn’t everyone trying to do it?

The problem was that there were very few shares of Palm being publicly traded.  Some smart traders made tens of thousands.  But there wasn’t enough publicly traded Palm stock available for any rational investor to make a huge amount of money.  So the irrational prices of 3Com and Palm were not corrected.

Thaler also tells a story about a young Benjamin Graham.  In 1923, DuPont owned a large number of shares of General Motors.  But the market value of DuPont was about the same as its stake in GM.  DuPont was a highly profitable firm.  So this meant that the stock market was putting the “stub value” of DuPont’s highly profitable business at zero.  Graham bought DuPont and sold GM short.  He made a lot of money when the price of DuPont went up to more rational levels.

In mid-2014, says Thaler, there was a point when Yahoo’s holdings of Alibaba were calculated to be worth more than the whole of Yahoo.

Sometimes, as with the closed-end funds, obvious mispricings can last for a long time, even decades.  Andrei Shleifer and Robert Vishny refer to this as the limits of arbitrage.



What are the implications of these examples?  If the law of one price can be violated in such transparently obvious cases such as these, then it is abundantly clear that even greater disparities can occur at the level of the overall market.  Recall the debate about whether there was a bubble going on in Internet stocks in the late 1990s….

Despite many market inefficiencies, the EMH – a part of rationalist economics – is still very useful:

So where do I come down on the EMH?  It should be stressed that as a normative benchmark of how the world should be, the EMH has been extraordinarily useful.  In a world of Econs, I believe that the EMH would be true.  And it would not have been possible to do research in behavioral finance without the rational model as a starting point.  Without the rational framework, there are no anomalies from which we can detect misbehavior.  Furthermore, there is not as yet a benchmark behavioral theory of asset prices that could be used as a theoretical underpinning of empirical research.  We need some starting point to organize our thoughts on any topic, and the EMH remains the best one we have.  (pages 250-251)

When it comes to the EMH as a descriptive model of asset markets, my report card is mixed.  Of the two components, using the scale sometimes used to judge claims made by political candidates, I would judge the no-free-lunch component to be ‘mostly true.’  There are definitely anomalies:  sometimes the market overreacts, and sometimes it underreacts.  But it remains the case that most active money managers fail to beat the market…

Most investors agree with Thaler on the no-free-lunch component of the EMH.  In practice, it is very difficult for any investor to beat the market over time.

But do prices always accurately reflect intrinsic value?

I have a much lower opinion about the price-is-right component of the EMH, and for many important questions, this is the more important component…

My conclusion:  the price is often wrong, and sometimes very wrong.  Furthermore, when prices diverge from fundamental value by such wide margins, the misallocation of resources can be quite big.  For example, in the United States, where home prices were rising at a national level, some regions experienced especially rapid price increases and historically high price-to-rental ratios.  Had both homeowners and lenders been Econs, they would have noticed these warning signals and realized that a fall in home prices was becoming increasingly likely.  Instead, surveys by Shiller showed that these were the regions in which expectations about the future appreciation of home prices were the most optimistic.  Instead of expecting mean reversion, people were acting as if what goes up must go up even more.

Thaler adds that policy-makers should realize that asset prices are often wrong, and sometimes very wrong, instead of assuming that prices are always right.



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:

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

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

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


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

My e-mail:




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.

Lions and Tigers and Bears, Oh My!

(Image:  Zen Buddha Silence by Marilyn Barbone.)

January 29, 2017

As humans, the ancient part of our brains – including the amygdala – alerts us subconsciously if there is the slightest sight or sound of something that could be dangerous (e.g., a lion, a tiger, or a bear).  Such an alert happens so fast that our body prepares for fight or flight long before we are even conscious of the potential threat.

Unfortunately, plummeting stock prices very often trigger the same fear response in our ancient brains.  This often causes us to make bad decisions.



One key to investing is to learn to minimize the number of mistakes we make.  A great way to minimize our mistakes is to follow an automatic system for investment decisions.

For many investors, the best automatic approach is simply to buy and hold low-cost broad market index funds.  These investors, if they stick with it over several decades, will do better than 90-95% of all investors.  And this winning strategy requires very little time to implement.

For investors who want a proven, relatively low-risk way to beat the market, a quantitative value investment approach is a good option.  One of the best research papers on why a value investing strategy beats the market over time, without taking more risk, is “Contrarian Investment, Extrapolation, and Risk,” by Josef Lakonishok, Andrei Schleifer, and Robert Vishny.  Link:

Lakonishok, Schleifer, and Vishny were so convinced by the evidence that they started LSV Asset Management, which today has over $87 billion in assets under management.  LSV uses a quantitative value investment approach which automatically selects a portfolio of stocks based on the factors supported by their research, including low price to earnings and low price to cash flow.  See:



The truth is that most of us do not know ourselves very well at all.  But recent advances in psychology and neuroscience have made it possible for us to know ourselves much better.  Slowly but surely, studying our own mistakes and studying the best psychology can help us to make better decisions over time.

When it comes to decision making under uncertainty – and this includes investing – there are many great books that are absolute must-reads, including the following:

  • Daniel Kahneman, Thinking, Fast and Slow (Farrar, Straus and Giroux, 2013, 2011)
  • Charles T. Munger, Poor Charlie’s Almanack, edited by Peter Kaufman (Walsworth Publishing Company, Expanded Third Edition, 2005)

There are many more great authors on decision making under uncertainty, including Michael Mauboussin, James Montier, and Richard Thaler, to name just a few.  But in the interest of brevity, this blog post focuses on a chapter from James Montier’s Value Investing (Wiley, 2009), Chapter 13: The Psychology of Bear Markets.



Montier writes that Abraham Lincoln relayed the following story:

Solomon once charged his wise men to invent him a sentence, to be ever in view, which should be true and appropriate in all times and situations.  They presented him with the words ‘And this, too, shall pass away’.  How much it expresses!  How chastening in the hour of pride.  How consoling in the depths of affliction…  (page 123)

Unfortunately, says Montier, when it comes to investing, we are often overwhelmed by fear or greed instead of training ourselves to slow down and to use careful logical analysis.  We have two mental systems, one that is automatic and intuitive, and the other that is capable of deliberate, rational thinking.  The automatic system is the more ancient system, and we share it with many other animals.

The problem is that paper stock losses cause fear.  The fear response is very powerful and largely unconscious initially, originating in the amygdala.  It prepares the body for fight or flight even before the conscious mind has had time to analyze the situation.

Most of the time in our evolutionary history, a fear response to movement in the grass was appropriate for maximizing the chances of survival.  A rapid response to a potential threat had a low cost in the case of a false positive, notes Montier.  But a false negative – not reacting to the movement in the grass, but having it turn out to be a lion – was potentially fatal.

Yet a fear response to paper stock losses is almost always harmful for long-term investors.  When stocks drop a lot (10-40%), many long-term investors with time horizons of at least ten or twenty years are overwhelmed by fear and sell when they should be buying.  For instance, many long-term investors were overwhelmed by fear (and loss aversion) in the bear market of late 2008 to early 2009.  Long-term investors who sold missed out on a more than 200% recovery (from the lows) for the S&P 500 Index.

Conversely, long-term investors who have mastered their fears – by having an automatic investment system, or by having trained their own responses, or both – bought in late 2008 and early 2009 and are much better off as a result.



James Montier describes a study by Shiv et al. (2005).  It is a game at the start of which each player receives $20.  The game has 20 rounds.  Each round, the player can decide to bet $1 or not.  Then a fair coin is flipped.  If it comes up heads, the player who bet $1 gets $2.50 back.  If it comes up tails, the player who bet $1 loses the $1.

It is optimal for the player to bet $1 in each of the 20 rounds because the game has a clear positive expected value – the wins will outweigh the losses on average.  Also, the decision to bet $1 each round should not depend upon whether one won or lost in the previous round.  Each flip is independent of the previous flip.

What actually happened, however, was that people invested less than 40% of the time after suffering a loss.  Furthermore, the longer the game continued, the lower the percentage of players who decided to bet.

As losses add up, people become more and more fearful, and less and less willing to invest even though the best time to invest is during a period of extended losses (like late 2008 and early 2009).  Montier writes:

The parallels with bear markets are (I hope) obvious.  The evidence above suggests that it is outright fear that drives people to ignore bargains when they are available in the market, if they have previously suffered a loss.  The longer they find themselves in this position the worse their decision making appears to become.  (page 125)



Montier observes:

Investors should consider trying to adopt the Buddhist approach to time.  That is to say, the past is gone and can’t be changed, the future is unknown, and so we must focus on the present.  The decision to invest or not should be a function of the current situation (from my perspective the degree of value on offer) not governed by our prior experiences (or indeed our future hopes).  However, this blank slate is mentally very hard to achieve.  Our brains seem to be wired to focus on the short term and to fear loss in an extreme fashion.  These mental hurdles are barriers to sensible investment decision making in a bear market.  (page 127)

A bear market can occur at virtually any time.  At some point in the next five or so years – possibly even this year – there will likely be a bear market in which stocks drop at least 20-25% (and possibly much more).  It’s essential for all investors that we have an automatic, checklist-like approach to plummeting stock prices in order to maximize rational decisions and minimize irrational decisions.  It’s also essential for many of us as investors that we develop a keen understanding of our own emotional responses to plummeting stock prices.

Knowing in detail how we will feel in the face of plummeting stock prices, and having a rational set of responses (ideally automatic and preprogrammed), is one of the keys to successful long-term investing.



One important rule for long-term investors is to check stock prices as infrequently as humanly possible.  A flashing, plummeting stock price is a subliminal message that very often will activate our subconscious fear response – SELL!  SELL!

For a long-term index fund investor, it is possible to check stock prices only a few times per year or less.  Similarly for a long-term value investor, one should check prices as infrequently as possible.

If we are tempted to check stock prices, especially when prices are plummeting, there are simple techniques we can use to avoid the temptation.  Obviously being deeply engaged in a work project is ideal.  Or we can read, watch a movie, or even go to the beach.  Many long-term value investors recommend going to the beach – ideally with a stack of great books or annual reports – instead of checking or worrying about cratering prices.

Only buy something that you’d be perfectly happy to hold if the market shut down for 10 years.  – Warren Buffett

Invest only if you would be comfortable owning a stock even if you had no way of knowing its daily share price.  – Ben Graham

When we own stocks, we are part owners of the underlying businesses.  So if we are happy with the businesses we own, we should never waste time worrying about or checking falling prices.  If we understand and believe in the underlying business, then a dropping stock price is a wonderful opportunity to increase our ownership of the business.  A good rule is to add to our position for each 10% or 15% drop in price.

If we own a low-cost S&P 500 index fund, then we are part owners of five hundred American businesses.  As long as we believe the U.S. economy will do well over the coming decades – and it will do well, even if grows a bit more slowly than in the past – then when the index drops a great deal, it’s a wonderful opportunity to add to our position as a part owner of American businesses.

As long as we have uncorrelated or negatively correlated positions in our overall portfolio – such as cash, cash equivalents, or gold – it is simple to implement an automatic rebalancing rule.  For instance, during or after a period of falling stock prices, we would use some cash (or gold) to increase our ownership of the businesses we like best.  Ideally, this rebalancing is preprogrammed to occur at the end of a specified time period (e.g., quarter or year).

Just for fun, a 30-second link from the Wizard of Oz:



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:

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

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

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


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

My e-mail:




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.