Boole Microcap Fund

(Image: Zen Buddha Silence by Marilyn Barbone.)

July 2, 2017

In Investing: The Last Liberal Art (Columbia University Press, 2^nd edition, 2013), Robert Hagstrom has an excellent chapter on decision making. Hagstrom examines Philip Tetlock’s discussion of foxes versus hedgehogs.

 

PHILIP TETLOCK’S STUDY OF POLITICAL FORECASTING

Philip Tetlock, professor of psychology at the University of Pennsylvania, spent fifteen years (1988-2003) studying the political forecasts made by 284 experts. As Hagstrom writes:

All of them were asked about the state of the world; all gave their prediction of what would happen next. Collectively, they made over 27,450 forecasts. Tetlock kept track of each one and calculated the results. How accurate were the forecasts? Sadly, but perhaps not surprisingly, the predictions of experts are no better than ‘dart-throwing chimpanzees.’ (page 149)

In other words, one could have rolled a 6-sided dice 27,450 times over the course of fifteen years, and one would have achieved the same level of predictive accuracy as this group of top experts. (The predictions were in the form of: more of X, no change in X, or less of X. Rolling a 6-sided dice would be one way to generate random outcomes among three equally likely scenarios.)

In a nutshell, political experts generally achieve high levels of knowledge (about history, politics, etc.), but most of this knowledge does not help in making predictions. When it comes to predicting the future, political experts suffer from overconfidence, hindsight bias, belief system defenses, and lack of Bayesian process, says Hagstrom.

Although the overall record of political forecasting is dismal, Tetlock was still able to identify a few key differences:

The aggregate success of the forecasters who behaved most like foxes was significantly greater than those who behaved like hedgehogs. (page 150)

The distinction between foxes and hedgehogs goes back to an essay by Sir Isaiah Berlin entitled, ‘The Hedgehog and the Fox: An Essay on Tolstoy’s View of History.’ Berlin defined hedgehogs as thinkers who viewed the world through the lens of a single defining idea, and foxes as thinkers who were skeptical of grand theories and instead drew on a wide variety of ideas and experiences before making a decision.

 

FOXES VERSUS HEDGEHOGS

Hagstrom clearly explains key differences between Foxes and Hedgehogs:

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

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

Tetlock tells us Foxes have three distinct cognitive advantages.

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

Hedgehogs start with one big idea and follow through – no matter the logical implications of doing so. Foxes stitch together a collection of big ideas. They see and understand the analogies and then create an aggregate hypothesis. I think we can say the fox is the perfect mascot for the College of Liberal Arts Investing. (pages 150-151)

 

KNOWING WHAT YOU DON’T KNOW

We have two classes of forecasters: Those who don’t know – and those who don’t know they don’t know. – John Kenneth Galbraith

Last year, I wrote about The Most Important Thing, a terrific book by the great value investor Howard Marks. See: https://boolefund.com/howard-marks-the-most-important-thing/

One of the sections from that blog post, ‘Knowing What You Don’t Know,’ is directly relevant to the discussion of foxes versus hedgehogs. We can often ‘take the temperature’ of the stock market. Thus, we can have some idea that the market is high and may fall after an extended period of increases.

But we can never know for sure that the market will fall, and if so, when precisely. In fact, the market does not even have to fall much at all. It could move sideways for a decade or two, and still end up at more normal levels. Thus, we should always focus our energy and time on finding individual securities that are undervalued.

There could always be a normal bear market, meaning a drop of 15-25%. But that doesn’t conflict with a decade or two of a sideways market. If we own stocks that are cheap enough, we could still be fully invested. Even when the market is quite high, there are usually cheap micro-cap stocks, for instance. Buffett made a comment indicating that he would have been fully invested in 1999 if he were managing a small enough sum to be able to focus on micro caps:

If I was running $1 million, or $10 million for that matter, I’d be fully invested.

There are a few cheap micro-cap stocks today. Moreover, some oil-related stocks are cheap from a 5-year point of view.

Warren Buffett, when he was running the Buffett Partnership, knew for a period of almost ten years (roughly 1960 to 1969) that the stock market was high (and getting higher) and would either fall or move sideways for many years. Yet he was smart enough never to predict precisely when the correction would occur. Because Buffett stayed focused on finding individual companies that were undervalued, Buffett produced an outstanding track record for the Buffett Partnership. Had he ever not invested in cheap stocks because he knew the stock market was high, Buffett would not have produced such an excellent track record. (For more about the Buffett Partnership, see: https://boolefund.com/warren-buffetts-ground-rules/)

Buffett on forecasting:

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.

Here is what Ben Graham, the father of value investing, said about forecasting the stock market:

…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.

Howard Marks has tracked (in a limited way) many macro predictions, including U.S. interest rates, the U.S. stock market, and the yen/dollar exchange rate. He found quite clearly that most forecasts were not correct.

I can elaborate on two examples that I spent much time on (when I should have stayed focused on finding individual companies available at cheap prices):

• the U.S. stock market
• the yen/dollar exchange

The U.S. stock market

A secular bear market for U.S. stocks began (arguably) in the year 2000 when the 10-year Graham-Shiller P/E – also called the CAPE (cyclically adjusted P/E) – was over 30, its highest level in U.S. history. The long-term average CAPE is around 16. Based on over one hundred years of history, the pattern for U.S. stocks in a secular bear market would be relatively flat or lower until the CAPE approached 10.

However, ever since Greenspan started running the Fed in the 1980’s, the Fed has usually had a policy of stimulating the economy and stocks by lowering rates or keeping rates as low as possible. This has caused U.S. stocks to be much higher than otherwise. For instance, with rates today staying near zero, U.S. stocks could easily be at least twice as high as ‘normal’ indefinitely, assuming the Fed decides to keep rates low for many more years. Furthermore, as Buffett has noted, very low rates for many decades would eventually mean price/earnings ratios on stocks of 100.

In addition to the current Fed regime, there are several additional reasons why rates may stay low. As Jeremy Grantham recently wrote:

• We could be between waves of innovation, which suppresses growth and the demand for capital.
• Population in the developed world and in China is rapidly aging. With more middle-aged savers and less high-consuming young workers, the result could be excess savings that depresses all returns on capital.
• Nearly 100% of all the recovery in total income since 2009 has gone to the top 0.1%.

Grantham discusses all of these possible reasons for low rates in the Q3 2016 GMO Letter: https://www.gmo.com/docs/default-source/research-and-commentary/strategies/gmo-quarterly-letters/hellish-choices-what’s-an-asset-owner-to-do-and-not-with-a-bang-but-a-whimper.pdf?sfvrsn=8

Grantham gives more detail on income inequality in the Q4 2016 GMO Letter: https://www.gmo.com/docs/default-source/research-and-commentary/strategies/gmo-quarterly-letters/is-trump-a-get-out-of-hell-free-card-and-the-road-to-trumpsville-the-long-long-mistreatment-of-the-american-working-class.pdf?sfvrsn=6

(In order to see GMO commentaries, you may have to register but it’s free.)

Around the year 2012 (or even earlier), some of the smartest market historians – including Russell Napier, author of Anatomy of the Bear – started predicting that the S&P 500 Index would fall towards a CAPE of 10 or lower, which is how every previous U.S. secular bear market concluded. It didn’t happen in 2012, or in 2013, or in 2014, or in 2015, or in 2016. Moreover, it may not happen in 2017 or even 2018.

Again, there could always be a normal bear market involving a drop of 15-25%. But that doesn’t conflict with a sideways market for a decade or two. Grantham suggests total returns of about 2.8% per year for the next 20 years.

Grantham, an expert on bubbles, also pointed out that the usual ingredients for a bubble do not exist today. Normally in a bubble, there are excellent economic fundamentals combined with a euphoric extrapolation of those fundamentals into the future. Grantham in Q3 2016 GMO Letter:

• Current fundamentals are way below optimal – trend line growth and productivity are at such low levels that the usually confident economic establishment is at an obvious loss to explain why. Capacity utilization is well below peak and has been falling. There is plenty of available labor hiding in the current low participation rate (at a price). House building is also far below normal.
• Classic bubbles have always required that the geopolitical world is at least acceptable, more usually well above average. Today’s, in contrast, you can easily agree isunusually nerve-wracking.
• Far from euphoric extrapolations, the current market has been for a long while and remains extremely nervous. Investor trepidation is so great that many are willing to tie up money in ultra-safe long-term government bonds that guarantee zero real return rather than buy the marginal share of stock! Cash reserves are high and traditional measures of speculative confidence are low. Most leading commentators are extremely bearish. The net effect of this nervousness is shown in the last two and a half years of the struggling U.S. market…so utterly unlike the end of the classic bubbles.
• …They – the bubbles in stocks and houses – all coincided with bubbles in credit…Credit is, needless to say, complex…What is important here is the enormous contrast between the credit conditions that previously have been coincident with investment bubbles and the lack of a similarly consistent and broad-based credit boom today.

The yen/dollar exchange

As for the yen/dollar exchange, some of the smartest macro folks around predicted (in 2010 and later) that shorting the yen vs. the U.S. dollar would be the ‘trade of the decade,’ and that the yen/dollar exchange would exceed 200. In 2007, the yen/dollar was over 120. By 2011-2012, the yen/dollar had gone to around 76. In late 2014 and for most of 2015, the yen/dollar again exceeded 120. However, in late 2015, the BOJ decided not to try to weaken their currency further by printing even larger amounts of money. The yen/dollar declined from over 120 to about 106. Since then, it has remained below 120.

The ‘trade of the decade argument’ was the following: the debt-to-GDP in Japan has reached stratospheric levels (over 400-500%, including over 250% for government debt-to-GDP), government deficits have continued to widen, and the Japanese population is actually shrinking. Since long-term GDP growth is essentially population growth plus productivity growth, it should become mathematically impossible for the Japanese government to pay back its debt without a significant devaluation of their currency. If the BOJ could devalue the yen by 67% – which would imply a yen/dollar exchange rate of well over 200 – then Japan could repay the government debt in seriously devalued currency. In this scenario – a yen devaluation of 67% – Japan effectively would only have to repay 33% of the government debt. Currency devaluation – inflating away the debts – is what most major economies throughout history have done.

Although the U.S. dollar may be stronger than the yen or the euro, all three governments want to devalue their currency over time. Therefore, even if the yen loses value, it’s not at all clear how long this will take when you consider the yen versus the dollar. The yen ‘collapse’ could be delayed by many years. So if you compare a yen/dollar short position versus a micro-cap value investment strategy, it’s likely that the micro-cap value investment strategy will produce higher returns with less risk.

• Similar logic applies to market timing. You may get lucky once in a row trying to time the market. But simply buying cheap stocks – and holding them for at least 3 to 5 years before buying cheaper stocks – is likely to do much better over the course of decades. Countless extremely intelligent investors throughout history have gone mostly to cash based on a market prediction, only to see the market continue to move higher for many years or even decades. Again: Even if the market is high, it can go sideways for a decade or two. If you buy baskets of cheap micro-cap for a decade or two, there is virtually no chance of losing money, and there’s an excellent chance of doing well.

Also, the total human economy is likely to be much larger in the future, and there may be some way to help the Japanese government with its debts. The situation wouldn’t seem so insurmountable if Japan could grow its population. But this might happen in some indirect way if the total economy becomes more open in the future, perhaps involving the creation of a new universal currency.

TWO SCHOOLS: ‘I KNOW’ vs. ‘I DON’T KNOW’

Financial forecasting cannot be done with any sort of consistency. Every year, there are many people making financial forecasts, and so purely as a matter of chance, a few will be correct in a given year. But the ones correct this year are almost never the ones correct the next time around, because what they’re trying to predict can’t be predicted with any consistency. Howard Marks writes:

I am not going to try to prove my contention that the future is unknowable. You can’t prove a negative, and that certainly includes this one. However, I have yet to meet anyone who consistently knows what lies ahead macro-wise…

One way to get to be right sometimes is to always be bullish or always be bearish; if you hold a fixed view long enough, you may be right sooner or later. And if you’re always an outlier, you’re likely to eventually be applauded for an extremely unconventional forecast that correctly foresaw what no one else did. But that doesn’t mean your forecasts are regularly of any value…

It’s possible to be right about the macro-future once in a while, but not on a regular basis. It doesn’t do any good to possess a survey of sixty-four forecasts that includes a few that are accurate; you have to know which ones they are. And if the accurate forecasts each six months are made by different economists, it’s hard to believe there’s much value in the collective forecasts.

Marks gives one more example: How many predicted the crisis of 2007-2008? Of those who did predict it – there was bound to be some from pure chance alone – how many of those then predicted the recovery starting in 2009 and continuing until today (early 2017)? The answer is ‘very few.’ The reason, observes Marks, is that those who got 2007-2008 right “did so at least in part because of a tendency toward negative views.” They probably were negative well before 2007-2008, and more importantly, they probably stayed negative afterward. And yet, from a close of 676.53 on March 9, 2009, the S&P 500 Index has increased more than 240% to a close of 2316.10 on February 10, 2017.

Marks has a description for investors who believe in the value of forecasts. They belong to the ‘I know’ school, and it’s easy to identify them:

• They think knowledge of the future direction of economies, interest rates, markets and widely followed mainstream stocks is essential for investment success.
• They’re confident it can be achieved.
• They know they can do it.
• They’re aware that lots of other people are trying to do it too, but they figure either (a) everyone can be successful at the same time, or (b) only a few can be, but they’re among them.
• They’re comfortable investing based on their opinions regarding the future.
• They’re also glad to share their views with others, even though correct forecasts should be of such great value that no one would give them away gratis.
• They rarely look back to rigorously assess their record as forecasters. (page 121)

Marks contrasts the confident ‘I know’ folks with the guarded ‘I don’t know’ folks. The latter believe you can’t predict the macro-future, and thus the proper goal for investing is to do the best possible job analyzing individual securities. If you belong to the ‘I don’t know’ school, eventually everyone will stop asking you where you think the market’s going.

You’ll never get to enjoy that one-in-a-thousand moment when your forecast comes true and the Wall Street Journal runs your picture. On the other hand, you’ll be spared all those times when forecasts miss the mark, as well as the losses that can result from investing based on overrated knowledge of the future.

No one likes investing on the assumption that the future is unknowable, observes Marks. But if the future IS largely unknowable, then it’s far better as an investor to acknowledge that fact than to pretend otherwise.

Furthermore, says Marks, the biggest problems for investors tend to happen when investors forget the difference between probability and outcome (i.e., the limits of foreknowledge):

• when they believe the shape of the probability distribution is knowable with certainty (and that they know it),
• when they assume the most likely outcome is the one that will happen,
• when they assume the expected result accurately represents the actual result, or
• perhaps most important, when they ignore the possibility of improbable outcomes.

Marks sums it up:

Overestimating what you’re capable of knowing or doing can be extremely dangerous – in brain surgery, transocean racing or investing. Acknowledging the boundaries of what you can know – and working within those limits rather than venturing beyond – can give you a great advantage. (page 123)

Or as Warren Buffett wrote in the 2014 Berkshire Hathaway Letter to Shareholders:

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.

Link: http://berkshirehathaway.com/letters/2014ltr.pdf

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(Image: Zen Buddha Silence by Marilyn Barbone.)

June 18, 2017

Richard H. Thaler recently published a book entitled Misbehaving: The Making of Behavioral Economics. It’s an excellent book. According to Nobel Laureate Daniel 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” are often less than fully rational, as demonstrated not only by decades of experiments, but also by the history of various asset prices.

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

 

THE BEAUTY CONTEST

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 is $100 million, then its stock will trade such that the market cap of the firm is $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 there can never be bubbles in asset prices. It also implies that there are no undervalued stocks, at least none that an investor could consistently identify. There is no way to “beat the market” over a long period of time except by luck. Warren Buffett was lucky.

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 famous economist J. M. Keynes was “a true forerunner of behavioral finance.” Keynes, who was a great value investor, thought that “animal spirits” play an important role in financial markets.

Keynes also observed that professional investors areplaying 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.

 

DOES THE STOCK MARKET OVERREACT?

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 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 present 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)

Results:

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%.

 

THE REACTION TO OVERREACTION

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.

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.

Thaler asserts thatthe 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. Link to paper: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

Lakonishok, Shleifer, and Vishny launched the highly successful LSV Asset Management based on their research: http://lsvasset.com/

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

 

THE PRICE IS NOT RIGHT

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 historical data on stock prices and dividends.

Then, 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, my emphasis)

Shiller demonstrated that a stock price typically moves around much more than the intrinsic value of the underlying business.

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.

THE BATTLE OF CLOSED-END FUNDS

It’s important to note that although the assumption of rationality and the EMH have been demonstrated not to be true – at least strictly speaking – 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.

Neuroscientists, psychologists, biologists, and other scientists will undoubtedly learn much more about human behavior in the coming decades. But even then, human behavior, due to its complexity, may remain partly unpredictable for some time. Thus, rationalist economic models may continue to be useful.

• Rationalist models, including game theory, may also be central to understanding and predicting artificially intelligent agents.
• It’s also possible (as hard as it may be to believe) that human beings will evolve – perhaps partly with genetic engineering and/or with help from AI – and become more rational overall.

The Law of One Price

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 Thaler et al. found upon doing the research. The greater the discounts to NAV for closed-end funds, the larger the difference was in returns between small stocks and large stocks.

 

NEGATIVE STOCK PRICES

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: http://www.e-m-h.org/Blac86.pdf)
• 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.”

 

THALER’S CONCLUSIONS ABOUT BEHAVIORAL FINANCE

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 these was a bubble going on in Internet stocks in the late 1990s…. (page 250)

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.

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…

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. (my emphasis)

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.

 

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(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’S CHECKLIST

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)

 

FINDING IDEAS

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

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

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.

 

CONCENTRATED VS. QUANTITATIVE

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 notedthat 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.

Inpractice, 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 overtime

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.

 

UNDERSTANDING THE BUSINESS

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.)

 

UNDERSTANDING THE BUSINESS – FROM THE CUSTOMER PERSPECTIVE

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?

 

EVALUATING THE STRENGTHS AND WEAKNESSES OF A BUSINESS AND INDUSTRY

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 occurredwhen 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?

 

MEASURING THE OPERATING AND FINANCIAL HEALTH OF THE BUSINESS

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.

 

EVALUATING THE DISTRIBUTION OF EARNINGS (CASH FLOWS)

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.

Accounting

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.

MANAGEMENT – BACKGROUND AND CLASSIFICATION

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 incentiveswith 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.

 

MANAGEMENT – COMPETENCE

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

Shearn:

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?

Shearn:

…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)

 

MANAGEMENT – POSITIVE AND NEGATIVE TRAITS

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?

Shearn:

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 Amazon.com, talked about the way that some investors congratulate Amazon.com 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.

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(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 makea 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: http://lsvasset.com/
• 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.)

 

TWO SYSTEMS

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.

 

PREPARE, PLAN, PRE-COMMIT

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.

 

OVERCOMING OVERCONFIDENCE

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: https://boolefund.com/the-education-of-a-value-investor/

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.)

 

DON’T LISTEN TO FINANCIAL FORECASTERS

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.)

 

THE DANGER OF PERCEIVED AUTHORITY

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.

 

WHY DO FINANCIAL FORECASTERS CONTINUE TO FORECAST?

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.

 

NET ASSET VALUE AND EARNINGS POWER VALUE

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: https://boolefund.com/peter-cundill-discount-to-liquidation-value/

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: https://boolefund.com/the-ugliest-stocks-are-the-best-stocks/

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(Image: Zen Buddha Silence by Marilyn Barbone.)

February 12, 2017

I’ve always deeply admiredboth 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). Butthere 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.

 

DECISION MAKING UNDER UNCERTAINTY

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 acompany 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 ofhorses 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: https://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenstern_utility_theorem)

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 arestill the best approximation for much human behavior.

 

THE MAKING OF BEHAVIORAL ECONOMICS

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

 

THE BEAUTY CONTEST

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 meansthat 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.

 

DOES THE STOCK MARKET OVERREACT?

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%.

 

THE REACTION TO OVERREACTION

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: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf

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

 

THE PRICE IS NOT RIGHT

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.

IS HUMAN BEHAVIOR INHERENTLY UNPREDICTABLE?

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.

 

THE BATTLE OF CLOSED-END FUNDS

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 foundupon 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.

 

NEGATIVE STOCK PRICES

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: http://www.e-m-h.org/Blac86.pdf)
• 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.

 

THALER’S CONCLUSIONS ABOUT BEHAVIORAL FINANCE

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.

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(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.

 

THE VIRTUES OF AUTOMATED INVESTING

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: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf?m=1360042367

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: http://lsvasset.com/

 

KNOWING OURSELVES

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.

 

KING SOLOMON

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.

 

FEAR AND BEAR MARKETS

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)

 

THE BLANK SLATE

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.

 

CHECK STOCK PRICES AS INFREQUENTLY AS POSSIBLE

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 aboutcratering 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 fallingprices. 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: https://www.youtube.com/watch?v=NecK4MwOfeI

 

BOOLE MICROCAP FUND

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

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

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

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

 

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

My e-mail: [email protected]

 

 

 

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

(Image: Zen Buddha Silence by Marilyn Barbone.)

January 8, 2017

John Bogle wrote The Little Book of Common Sense Investing in 2007 (Wiley). It’s a terrific book for all investors. Here are some key points:

INTRODUCTION – Don’t Allow a Winner’s Game to Become a Loser’s Game

Owning a slice of all American businesses is a positive-sum game, especially over the course of many decades. On the whole, the capitalist system creates enormous wealth for its owners over the very long term due to innovation and growth.

Trying to do better than long-term owners of all American businesses–that is, trying to beat the broad market index over time–is a zero-sum game. For each investor who does better than the broad market index, another investor must do worse. Beating the stock market is a zero-sum game, but that is BEFORE the deduction of costs.

AFTER the deduction of costs, beating the market is a loser’s game. Bogle explains:

So who wins? You know who wins. The man in the middle (actually, the men and women in the middle, the brokers, the investment bankers, the money managers, the marketers, the lawyers, the accountants, the operations departments of our financial system) is the only sure winner in the game of investing. Our financial croupiers always win. In the casino, the house always wins. In horse racing, the track always wins. In the powerball lottery, the state always wins. Investing is no different.

The vast majority of investors who invest directly in stocks trail the market, net of costs, over the longer term. Even more so, the vast majority of investors who invest in mutual funds trail the market, net of costs, over the longer term.

The basic arithmetic has a simple bottom line: The vast majority of all investors would be best off, over the longer term (several decades or more), by simply buying and holding low-cost broad market index funds.

Many top investors, including Warren Buffett and Charlie Munger, have emphatically agreed with John Bogle’s conclusion.

 

A PARABLE – The Gotrocks Family

Warren Buffett tells the parable of the Gotrocks family in the 2005 Berkshire Hathaway Annual Letter: http://berkshirehathaway.com/letters/2005ltr.pdf

Bogle gives his version of the parable: At the beginning, a large family called the Gotrocks owns 100 percent of every stock in the United States, thus benefitting from the long-term productivity of all businesses.

Their investment had compounded over the decades, creating enormous wealth, because the Gotrocks family was playing a winner’s game.

But then a few ‘fast-talking Helpers’ arrive, and they convince some ‘smart’ Gotrocks cousins that they can earn a larger share than their relatives by selling some of their shares to other family members and by buying some shares from others in return.

The net result is that the family wealth starts compounding at a slower rate overall because some of the return is now given to the Helpers as trading fees.

The cousins decide that they need more help in order to actually do better than their relatives, so they hire another set of Helpers to pick which stocks to buy and which to sell. Now the family wealth overall compounds at an even slower rate, since it is reduced by two layers of fees to Helpers.

So the cousins decide to hire even more Helpers, who promise to help the cousins pick the right stock-picking Helpers. Once again, the family wealth overall now compounds even more slowly.

After some period of time, the family calls a meeting. They ask:

“How is it that our original 100 percent share of the pie–made up each year of all those dividends and earnings–has dwindled to just 60 percent?”

The wisest family member, an old uncle, notes that the entire reduction in overall returns is exactly equal to the fees and expenses related to all of the Helpers. Thus, in order for the family to again benefit from 100 percent of the growth of all American businesses, they should simply get rid of all the Helpers.

The family follows the uncle’s advice and soon they are again benefitting 100 percent from the wealth created by all American businesses over time.

Owning a low-cost index fund is exactly the same idea. Bogle quotes Jack R. Meyer, former president of Harvard Management Company:

Most people think they can find managers who can outperform, but most people are wrong. I will say that 85 to 90 percent of managers fail to match their benchmarks. Because managers have fees and incur transaction costs, you know that in the aggregate they are deleting value.

Burton Malkiel:

Active management as a whole cannot achieve gross returns exceeding the market as a whole and therefore they must, on average, underperform the indexes by the amount of these expense and transaction costs disadvantages.

 

RATIONAL EXUBERANCE – Business Reality Trumps Market Expectations

Warren Buffett:

The most that owners in the aggregate can earn between now and Judgment Day is what their businesses in the aggregate earn.

Bogle adds:

… History, if only we would take the trouble to look at it, reveals the remarkable, if essential, linkage between the cumulative long-term returns earned by business–the annual dividend yield plus the annual rate of earnings growth–and the cumulative returns earned by the U.S. stock market.

From 1900 to 2005, the average annual return on stocks was 9.6 percent, nearly equal to the investment return of 9.5 percent–4.5 percent from dividend yield and 5 percent from earnings growth.

The extra 0.1 percent can be related to a speculative return, or it may reflect “an upward long-term trend in stock valuations.”

The results from 106 years of compounding:

Each dollar initially invested in 1900 at an investment return of 9.5 percent grew by the close of 2005 to $15,062.

The initial amount is thus multiplied over 15,000 times. Bogle calls this “the ultimate winner’s game.”

Borrowing from John Maynard Keynes, Bogle says there are two components that drive long-term stock market returns: a fundamental component and a speculative component. The fundamental driver of long-term stock market returns has averaged 9.5 percent, 4.5 percent from dividends and 5 percent from earnings growth.

The speculative component of long-term stock market returns is determined by changes inP/E multiples. Some periods see P/E multiples rise, causing the average annual returns to be higher than what the fundamental component alone would dictate. Other periods see P/E multiples fall, causing the average annual returns to be lower than what the fundamental component alone would dictate.

These speculative swings are due to many factors, including investor confidence, the level of interest rates, and changes in general price levels (high inflation or deflation). However, the speculative swings cannot be predicted at all. Bogle says:

After more than 55 years in this business, I have absolutely no idea how to forecast these swings… I’m not alone. I don’t know anyone who has done so successfully, or even anyone who knows anyone who has done so. In fact, 70 years of financial research shows that no one has done so.

Great investors such as Warren Buffett and Peter Lynch have made the same point: No one can forecast accurately with any sort of consistency. By chance alone, there will always be some forecasters in any given period who turn out to be correct. But it’s impossible to say ahead of time which forecasters those will be. And when one does find, after the fact, the forecasters who were correct, that says nothing about which forecasters will be correct in the next period.

By the mid 1990’s, P/E multiples had been increasing for at least a decade and were high by historical standards, so one would have thought they would then go lower. But they kept increasing until they reached a new record in 2000, far higher than ever before. Similarly, in 2012 to 2013, assuming history was any guide, one would have thought that P/E multiples would decline for awhile, but that hasn’t been true at all.

In fact, if one assumes that most major central banks are going to keep rates very low for decades, then P/E multiples will continue to be much higher on average than has been the case historically. As Warren Buffett remarked recently, near-zero rates indefinitely would mean P/E multiples of 50 or more.

Moreover, the return on invested capital–on the whole–may be structurally higher, since technology companies have become much more important in the U.S. and global economies. All else equal, this also implies higher P/E multiples than has been the case historically.

Some of the smartest have been predicting lower U.S. stocks since 2012-2013, but the opposite has happened. This is just one example out of many throughout history where very smart folks made highly confident financial forecasts that turned out to be completely wrong.

The rational investor simply buys and holds for the longer term, keeping fees and expenses at an absolute minimum, without trying to time the market. This approach, buying and holding low-cost broad market index funds, will allow any investor to beat roughly 90-95% of all investors over the course of decades. Writes Bogle:

So how do you cast your lot with business? Simply by buying a portfolio that owns the shares of every business in the United States and then holding it forever. It is a simple concept that guarantees you will win the investment game played by most other investors who–as a group–are guaranteed to lose.

 

HOW MOST INVESTORS TURN A WINNER’S GAME INTO A LOSER’S GAME – The Relentless Rules of Humble Arithmetic

After subtracting the costs of financial intermediation–all those management fees, all those brokerage commissions, all those sales loads, all those advertising costs, all those operating costs–the returns of investors as a group must, and will, and do fall short of the market return by an amount precisely equal to the aggregate amount of those costs.

It’s obvious based on humble arithmetic that:

(1) Beating the market BEFOREcosts is a zero-sum game;

(2) Beating the market AFTER costs is a loser’s game.

Most individual investors choose mutual funds rather than individual stocks. Bogle describes the costs:

In equity mutual funds, management fees and operating expenses–combined, called the expense ratio–average about 1.5 percent per year of fund assets. Then add, say, another 0.5 percent in sales charges, assuming that a 5 percent initial sales charge is spread over a 10-year holding period. If the shares are held for five years, the cost would be twice that figure–1 percent per year.

But then add a giant additional cost, all the more pernicious by being invisible. I am referring to the hidden cost of portfolio turnover, estimated at a full 1 percent per year.

Bottom line

… the ‘all-in’ cost of equity fund ownership can come to as much as 3 percent to 3.5 percent per year.

Nonetheless, observes Bogle, most investors continue to ignore this reality, perhaps because “it flies in the face of their deep-seated beliefs, biases, overconfidence, and uncritical acceptance of the way that financial markets have worked, seemingly forever.”

Of course, financial intermediaries on the whole have very strong financial incentives to ignore the fact that low-cost index funds would be best for the vast majority of investors. Bogles paraphrases Upton Sinclair:

It’s amazing how difficult it is for a man to understand something if he’s paid a small fortune not to understand it.

Moreover, it’s easy for investors to ignore the costs of investing, writes Bogle. Many costs (including transaction costs, sales charges, and taxes) are largely hidden from view. Moreover, if it has been a good period for the market–with reasonably high returns–then the ‘all-in’ costs of 2.5 or 3 percent per year can be much less noticeable. Finally, investors tend to focus on short-term returns (a few years). But it’s only after several decades that the enormous superiority of a low-cost index fund–versus the average equity mutual fund–is obvious.

Bogle gives an example: Assume, reasonably enough, that the stock market total return will be 8 percent per year over the coming 50 years. The costs of the average mutual fund can be estimated at 2.5 percent per year. After the first ten years, an index investment will be $21,600, while the average mutual fund investment will reach $17,100. Many investors may not really notice it at this point. But after 50 years, a simple index investment will reach $469,000, while the average mutual fund investment will reach $145,400.

Thus, after 50 years, costs for the mutual fund investor will have taken almost 70 percent of the index returns:

The investor, who put up 100 percent of the capital and assumed 100 percent of the risk, earned only 31 percent of the market return. The system of financial intermediation, which put up zero percent of the capital and assumed zero percent of the risk, essentially confiscated 70 percent of that return–surely the lion’s share. What you see here–and please don’t ever forget it!–is that over the long term, the miracle of compounding returns is overwhelmed by the tyranny of compounding costs.

 

THE GRAND ILLUSION – Surprise! The Returns Reported by Mutual Funds Aren’t Actually Earned by Mutual Fund Investors

Historically, while mutual funds have trailed the index by roughly 2.5 percent per year on average, the average investor in mutual funds has done much worse (typically 2-3 percent worse, meaning the average investor in mutual funds trailed the market by 4.5 to 5.5 percent per year).

The average investor in mutual funds has repeatedly made two large mistakes: (1) buying after an extended period during which the stock market has done well, and selling after the stock market has done poorly; (2) buying mutual funds that have done well recently, and selling mutual funds that have done poorly recently. This is just the opposite of what generally would help individual investors. The late 1990’s are a good example given by Bogle:

As the market soared, investors poured ever larger sums of money into equity funds. They invested a net total of only $18 billion in 1990 when stocks were cheap, but $420 billion in 1999 and 2000, when stocks were overvalued… What’s more, they also chose overwhelmingly the highest-risk growth funds, to the virtual exclusion of more conservative value-oriented funds. While only 20 percent of their money went into risky aggressive growth funds in 1990, they poured fully 95 percent into such funds when they peaked during 1999 and early 2000.

Bogle continues:

When the annual returns of these aggressive funds are compounded over the full period, the deterioration is stunning: a cumulative fund return averaging more than 112 percent; a cumulative shareholder return averaging negative 4.5 percent. That’s a lag of more than 117 percentage points! This astonishing penalty, then, makes clear the perils of fund selection and timing. It also illustrates the value of indexing and the necessity of setting a sound course and then sticking to it, come what may.

One great virtue of buying and holding low-cost index funds is that it can neutralize cognitive biases. Cognitive biases are largely innate, and if left unchecked, they inevitably lead most investors to make the certain mistakes again and again, such as overconfidently extrapolating the recent past. For more on cognitive biases, see:https://boolefund.com/cognitive-biases/

Another virtue of index fund investing, as mentioned, is that it requires very little effort or time to implement. The index fund investor need not worry about individual stocks or mutual funds, nor need she waste time trying to predict what the stock market or the economy will do in any given year.

Bogle quotes Charles Schwab, who himself prefers index funds:

It’s fun to play around… it’s human nature to try to select the right horse… (But) for the average person, I’m more of an indexer… The predictability is so high… For 10, 15, 20 years you’ll be in the 85^th percentile of performance. Why would you screw it up?

Finally, for investors with taxable accounts, index fund investors have had roughly a third the taxes as the average mutual fund investor. This is due to the much lower turnover of index funds.

 

SELECTING LONG-TERM WINNERS

Bogle looks back (from 2007) to 1970 to see how many mutual funds from that year have done well over time. In 1970, there were 355 mutual funds. 223 of those were no longer in business by 2007. In addition to the 223 mutual funds that closed, there was another 60 mutual funds that significantly underperformed the S&P 500 Index by more than 1% per year. So together, 283 funds–almost 80% of the original 355–were not long-term winners. And another 48 funds provided returns that essentially matched the S&P 500 Index.

So that leaves only 24 mutual funds–one out of every 14–that did better than the S&P 500 Index from 1970 to 2007. Writes Bogle:

Let’s face it: those are terrible odds! What’s more, the margin of superiority of 15 of those 24 funds over the S&P 500 Index was less than 2 percentage points per year, a superiority that may be due as much to luck as skill.

Bogle says that there were nine long-term winners, funds that outpaced the market by 2 percentage points or more of annual return over 35 years. But Bogle points out that six out of those nine long-term winners achieved their superior records many years ago, often when they were of much smaller size. Increasing fund size is an inevitable drag on long-term investment performance. Bogle says that one of these funds peaked in 1982, and two others peaked in 1983. The other three peaked in 1993.

So there are only three funds out of the original 355–only 8/10 of 1%–that both survived and created an excellent long-term record that is still ongoing (as of 2007).

For the individual investor looking at the next 35 years, what are the odds of picking a mutual fund that will be able to clearly outpace the broad market, net of costs? If the last 35 years are any guide, the odds may be 1-3% (or even less).

Thus, writes Bogle, a low-cost index fund tends to grow its margin of superiority over time. After several decades, an investor in low-cost index funds is likely to be better off, net of costs, than roughly 90% of all investors. After 50 years, an investor in low-cost index funds is likely to be better off, net of costs, than roughly 95% (or more) of all investors.

If one can be virtually guaranteed, by simple arithmetic, to outpace roughly 90-95% (or more) of all investors over time, with very little time or effort required, why wouldn’t one choose this option? It is, in fact, the best option for the vast majority of investors.

 

YESTERDAY’S WINNERS, TOMORROW’S LOSERS

Bogle shows a fascinating chart of the top 10 performing mutual funds from 1997 to 1999. These top performing mutual funds had the following rankings in 2000 to 2002, out of a total of 851 mutual funds altogether:

• 841
• 832
• 845
• 791
• 801
• 798
• 790
• 843
• 851
• 793

If you think those rankings are bad, the average investor in these funds, on a cumulative basis, did much worse! Over the six year period of 1997 to 2002, these funds had a net gain of 13 percent overall. But investors in these funds, by piling in largely after the funds had gone up, and by selling largely after the funds had gone down, incurred a net loss of 57 percent overall.

The biggest mistake that most investors make is overconfidently extrapolating the recent past, i.e., buying after a fund has done well and selling after a fund has done poorly. But market timing very rarely works except by luck. (One approach that can work well is to have a portion in stocks and a portion in fixed-income and cash – with the portions decided ahead of time based on risk tolerance and age – and then rebalance to the target allocations periodically.)

 

THE HUMBLE, RELENTLESS ARITHMETIC OF INDEX FUNDS

Warren Buffett himself, arguably the best investor ever, has very consistently argued that thevast majorityof all investors would be best off just buying and holding a simple, low-cost index fund.

By periodically investing in an index fund, for example, the know-nothing investor can actually out-perform most investment professionals. Paradoxically, when ‘dumb’ money acknowledges its limitations, it ceases to be dumb.–Warren Buffett, 1993 Letter to shareholders.

Link:http://www.berkshirehathaway.com/letters/1993.html

See also the 2014 Berkshire Letter, such as this passage (page 19):

If the investor, instead, fears price volatility, erroneously viewing it as a measure of risk, he may, ironically, end up doing some very risky things. Recall, if you will, the pundits who six years ago bemoaned falling stock prices and advised investing in ‘safe’ Treasury bills or bank certificates of deposit. People who heeded this sermon are now earning a pittance on sums they had previously expected would finance a pleasant retirement. (The S&P 500 was then below 700; now it is about 2,100.) If not for their fear of meaningless price volatility, these investors could have assured themselves of a good income for life by simply buying a very low-cost index fund whose dividends would trend upward over the years and whose principal would grow as well (with many ups and downs, to be sure).

Investors, of course, can, by their own behavior, make stock ownership highly risky. And many do. Active trading, attempts to ‘time’ market movements, inadequate diversification, the payment of high and unnecessary fees to managers and advisors, and the use of borrowed money can destroy the decent returns that a life-long owner of equities would otherwise enjoy. Indeed, borrowed money has no place in the investor’s tool kit: 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.

The commission of the investment sins listed above is not limited to ‘the little guy.’ Huge institutional investors, viewed as a group, have long underperformed the unsophisticated index-fund investor who simply sits tight for decades. A major reason has been fees: Many institutions pay substantial sums to consultants who, in turn, recommend high-fee managers. And that is a fool’s game.

There are a few investment managers, of course, who are very good–though in the short run, it’s difficult to determine whether a great record is due to luck or talent. Most advisors, however, are far better at generating high fees than they are at generating high returns. In truth, their core competence is salesmanship. Rather than listen to their siren songs, investors–large and small–should instead read Jack Bogle’s The Little Book of Common Sense Investing.

Link: http://berkshirehathaway.com/letters/2014ltr.pdf

 

BOOLE MICROCAP FUND

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

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

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

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees. The Boole Fund has low fees.

 

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

My e-mail: [email protected]

 

 

 

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

December 18, 2016

There are several ways to measure cheapness, including low EV/EBIT, low P/E, and low P/B. Historically, these metrics have effectively identified groups of cheap stocks that outperform the market over time. Many academic studies of value investing have focused on low P/B stocks (equivalently, high book-to-market stocks).

If you look at the cheapest group of low P/B stocks (quintile or decile), you find that, during most historical periods, this group has outperformed the market and has done so with less risk. Here “risk” is defined–following Lakonishok, Shleifer, and Vishny–in terms of performance in bear markets, recessions, or other “bad states” of the world, and in terms of more traditional betas and standard deviations. Link to the 1994 paper by Lakonishok, Schleifer, and Vishny: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf?m=1360042367

And yet each individual low P/B stock is more likely than the average stock to underperform the market and is, in that sense, riskier.

Joseph Piotroski, a professor of accounting at the University of Chicago (currently at Stanford), noticed this disparity. Even though low P/B stocks as a group have beaten the market during most historical time periods, Piotroski found that 57% of the low P/B stocks underperformed the market. The outperformance of the low P/B group has been driven by only 43% of the stocks in the group, while it has been held back by the other 57%.

Given that low P/B stocks are often distressed, Piotroski wondered whether you could distinguish between “cheap and strong” companies and “cheap but weak” companies. Piotroski invented a measure called the F_Score for this purpose. Applying the F_Score to the group of low P/B stocks (the cheapest quintile) improved performance by 7.5% per year between 1976 and 1996. The biggest improvements in performance were concentrated in cheap micro caps with no analyst coverage.

 

THE PIOTROSKI F_SCORE

Piotroski invented his F_Score by thinking about what measures you would expect to distinguish between “cheap and strong” companies and “cheap but weak” companies. He focused on recent changes in fundamentals that could be detected in a company’s financial statements. Piotroski identified three areas to look for improving fundamentals:

• Profitability and Cash Flow
• Leverage and Liquidity
• Operating Efficiency

Piotroski thought of four measures for profitability and cash flow, three for leverage and liquidity, and two for operating efficiency. Each measure is binary, “1” for good and “0” for bad. Thus for a given cheap company, a total score of “9”–a “1” on each of the nine measures–would indicate the maximum possible financial strength, whereas a “0” would indicate the minimum. Because the F_Score is applied to cheap–often distressed–stocks, it’s reasonable to expect it to help in general. And it does.

Link to Piotroski’s paper, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers” (2002, University of Chicago Graduate School of Business): http://www.chicagobooth.edu/~/media/FE874EE65F624AAEBD0166B1974FD74D.pdf

 

PROFITABILITY AND CASH FLOW

As Piotroski notes, current profitability and cash flow are indicative of a firm’s ability to generate cash internally. A distressed company showing a positive trend in earnings and cash flows is more likely to do well.

Piotroski has four measures for profitability and cash flow:

• ROA measures current net income before extraordinary items.
• CFO measures current cash flow from operations.
• ΔROA measures this year’s ROA minus last year’s ROA.
• ACCRUAL measures cash flow from operations versus net income before extraordinary items.

If ROA > 0–if net income before extraordinary items is positive–then the indicator variable F_ROA = 1, otherwise F_ROA = 0.

If CFO > 0–if cash flow from operations is positive–then the indicator variable F_CFO = 1, otherwise F_CFO = 0.

If ΔROA > 0–if net income before extraordinary items has increased from the prior year–then the indicator variable F_ΔROA = 1, otherwise F_ΔROA = 0.

If CFO > ROA–if cash flow from operations is greater than net income before extraordinary items–then F_ACCRUAL = 1, otherwise F_ACCRUAL = 0.

The logic is straightforward. If the firm in question–which is cheap and likely to be distressed–is showing positive net income and cash flow, the firm is generating cash internally. Similarly, an improvement in net income is a positive signal. Finally, cash flow from operations being larger than net income before extraordinary items is positive: distressed firms have an incentive to distort or “manage” earnings (to prevent covenant violations), but it is much harder to distort cash.

 

LEVERAGE AND LIQUIDITY

Because many cheap firms are distressed, an increase in debt, a decrease in liquidity, or the use of external financing is a bad signal. Thus, Piotroski invented the following three simple measures:

• ΔLEVER captures change in the firm’s long-term debt level.
• ΔLIQUID measures change in the firm’s current ratio.
• EQ_OFFER indicates whether the firm has issued common equity.

If ΔLEVER < 0–if the long-term debt level fell–then the indicator variable F_ΔLEVER = 1, otherwise F_ΔLEVER = 0.

If ΔLIQUID > 0–if the current ratio (current assets divided by current liabilities) improved–then the indicator variable F_ΔLIQUID = 1, otherwise F_ΔLIQUID = 0.

If the firm did not issue common equity, then the indicator variable EQ_OFFER = 1, otherwise EQ_OFFER = 0.

If a distressed firm does not have to raise external capital via an increase in long-term debt, that is a positive signal about its ability to generate sufficient cash internally. Also, an improvement in liquidity bodes well for the firm’s ability to service current debt obligations. If a distressed firm has to issue new equity to raise cash, that’s not a good signal, especially if the new equity is cheaply priced.

 

OPERATING EFFICIENCY

Piotroski looked at two key constructs in a decomposition of return on assets: gross margin ratio and asset turnover. Both are signals of the efficiency of the firm’s operations.

• ΔMARGIN measures the firm’s current gross margin ratio relative to the previous year.
• ΔTURN measures the firm’s current asset turnover relative to the previous year.

If ΔMARGIN > 0–if the gross margin ratio has improved–then the indicator variable F_ΔMARGIN = 1, otherwise F_ΔMARGIN = 0.

If ΔTURN > 0–if asset turnover increased–then the indicator variable F_ΔTURN = 1, otherwise F_ΔTURN = 0.

An improvement in margins means a reduction in costs or an increase in the price of the firm’s product. An increase in asset turnover–greater productivity of the asset base–can result from more efficient operations (greater sales/assets) or an increase in sales. Increased sales may signify improved market conditions for the firm’s product.

 

MUCH IMPROVED PERFORMANCE

The overall F_SCORE is the sum of the nine individual binary signals:

F_SCORE = F_ROA + F_ΔROA + F_CFO + F_ACCRUAL + F_ΔMARGIN + F_ΔTURN + F_ΔLEVER + F_ΔLIQUIDITY + EQ_OFFER.

 

From the low P/B quintile, Piotroski isolated all the companies that had F_SCORES of 8 or 9. This low P/B, high F_SCORE portfolio outperformed the low P/B quintile by 7.5% per year between 1976 and 1996. The biggest improvements in performance were concentrated incheap micro caps with no analyst coverage.

 

BOOLE MICROCAP FUND

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

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

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

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees. The Boole Fund has low fees.

 

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

My e-mail: [email protected]

 

 

 

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

(Image: Zen Buddha Silence by Marilyn Barbone.)

December 11, 2016

CLINICAL VS. MECHANICAL PREDICTION: A META-ANALYSIS

William Grove, David Zald, Boyd Lebow, Beth Snitz, and Chad Nelson did a meta-analysis (a study of studies) of 136 different studies of human experts vs. simple quant models. Here is a link to the paper by Grove et al. (2000):http://datacolada.org/wp-content/uploads/2014/01/Grove-et-al.-2000.pdf

Here is what Grove et al. discovered about 136 different studies:

• 64 clearly favored the model
• 64 showed approximately the same result between the model and human judgment
• 8 found in favor of human judgment

(NOTE: All eight cases where human judgment prevailed had one thing in common: the humans had more information than the quant models.)

Across all 136 studies that Grove et al. examined, experts were correct in 66.5% of the cases, while the quant models did significantly better with an average hit ratio of 73.2%.

Paul Meehl, one of the founding fathers of the importance of quant models versus human judgments, had this to say:

There is no controversy in social science which shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one… predicting everything from the outcomes of football games to the diagnosis of liver disease. And when you can hardly come up with a half a dozen studies showing even a weak tendency in favor of the clinician, it is time to draw a practical conclusion.

 

BOOLE MICROCAP FUND

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

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

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

The goal of the Boole Microcap Fund is to outperform the Russell Microcap Index over time, net of fees. The Boole Fund has low fees.

 

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

My e-mail: [email protected]

 

 

 

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