(Image: Zen Buddha Silence by Marilyn Barbone.)
March 5, 2017
Quantitative value investor Tobias Carlisle has written an excellent book entitled Deep Value: Why Activists and Other Contrarians Battle for Control of LOSING Corporations (Wiley, 2014).
The book has many important and counterintuitive lessons for quantitative value investors. This blog post is a brief summary of some chief lessons.
UGLY VALUE STOCKS OUTPERFORM
Companies with low growth or no growth that are trading at cheap valuations significantly outperform companies with high growth. In other words, as a group, companies that have been doing terribly and that are trading at cheap prices – seemingly justifiably – do much better than companies that have been doing well and growing fast.
It’s important to note that these findings apply to groups of stocks, not individual stocks. Cheap value stocks, as a group (and as a portfolio), significantly outperform companies that have been doing well and whose stocks have been doing well. Moreover, in states of the world including bear markets and recessions, value stocks do better than growth stocks. So value stocks are less risky as a group than growth stocks.
On an individual level, a value stock is riskier than an average stock. Whereas an average stock has a 50% chance of underperforming the market, a value stock – if it is just chosen based on cheapness alone, without additional criteria – has a greater than 50% chance of underperforming the market. But as a group (and as a portfolio), value stocks – when compared to either growth stocks or average stocks – are less risky and perform better over time.
If you are following a deep value approach, there are additional criteria that you can apply in the stock selection process to reduce the percentage of deep value stocks that underperform the market. One example is the Piotroski F-Score, which identifies companies that show improving fundamentals (e.g., increased cash flows or reduced debt levels). Joseph Piotroski came up with the F-Score because he discovered that while value stocks outperform as a group, there are many individual value stocks dragging down the overall performance of the value portfolio.
In sum, a portfolio of ugly value stocks far outperforms the market over time. Remarkably, this already significant outperformance can be noticeably improved by using the Piotroski F-Score to cut off the left tail of the return distribution. See: http://boolefund.com/joseph-piotroski-value-investing/
STAGNANT VALUE OUTPERFORMS GROWING VALUE
If you only look at value stocks, which as a group outperform, doesn’t it make sense to focus on the cheap stocks where the companies have been doing well – in terms of growth – rather than the cheap stocks where the companies have been doing terribly? No. Carlisle comments:
This is a fascinating finding. Intuitively, we are attracted to high growth and would assume that high-growth value stocks are high-quality stocks available at a bargain price. The data show, however, that the low- or no-growth value stocks are the better bet. It seems that the uglier the stock, the better the return, even when the valuations are comparable. (page 133)
This same logic also applies to excellent, A+ companies versus unexcellent, D companies. Carlisle again:
Buying well-run companies with good businesses seems to make so much sense. Buying well-run companies with good businesses at bargain prices seems to make even more sense. The research shows, however, that the better investment – rather than the better company – is the value stock, the scorned, the unexcellent, the Ds, the loss-making net nets. And the better value stock, according to Lakonishok, Shleifer, and Vishny’s research, is the low- or no-growth value stock, what they describe as ‘contrarian value,’ and what I regard as deep value; the ugliest of the ugly. (page 140)
Link to the famous 1994 paper by Josef Lakonishok, Andrei Schleifer, and Robert Vishny: http://scholar.harvard.edu/files/shleifer/files/contrarianinvestment.pdf?m=1360042367
QUANTITATIVE DEEP VALUE
Investing in general is difficult to do effectively, which is why Warren Buffett advises most people to invest in low-cost index funds.
Deep value investing can be even more difficult because it requires consistently buying what everyone else hates – the ugliest, the worst, the cheapest stocks available. Deep value stocks are almost always facing enormous business problems, and quite often the industries in which deep value stocks are found are doing horribly (for example, oil-related stocks with the oil price down from over $100 a barrel to below $30 or $40 a barrel).
Thus, the best way for most investors to benefit from deep value stocks is to use a quantitative (statistical) approach. Carlisle explains:
Most deeply undervalued, fundamentally weak stocks are that way because their futures appear uncertain – they are losing money or marginally profitable – and, on an individual basis, don’t appear to be good candidates for purchase. We know, however, that in aggregate they provide excellent returns, outperforming the market in the long run and suffering fewer down years than the market. This is an area where our native intuition fails us. As we have seen, no matter how well trained we are, humans tend to have difficulty with probabilistic, uncertain, and random processes… Since the 1950s, social scientists have been comparing the predictive abilities of traditional experts and what are known are statistical prediction rules. The studies have found almost uniformly that statistical prediction rules are more consistently accurate than the very best experts. (page 141)
I wrote about this here: http://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/
The conclusion is that, for a surprisingly wide range of prediction problems – including investing – statistical prediction rules are more reliable than human experts. Many people have objected that experts could do better than simple statistical prediction rules if they had the ability to override the rule in specific cases. But this turns out not to be true. The statistical prediction rules are a ceiling from which the expert detracts rather than a floor to which the expert adds.
As Daniel Kahneman explains so well in his book Thinking, Fast and Slow – see especially Part III – we humans are generally very overconfident about our ability to predict the future. Philip Tetlock did a landmark, 20-year study of experts making political and economic predictions. What Tetlock found based on more than 27,000 predictions over the course of two decades is that the experts were little better than chance. See Tetlock’s Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, 2005).
People, especially experts, are simply way overconfident about their ability to predict many future events. Even Kahneman himself, after spending most of his life studying overconfidence, admits that he is “wildly overconfident” by nature, just like most people. Overconfidence is related to many cognitive biases that people have, especially hindsight bias: http://boolefund.com/cognitive-biases/
Statistical Prediction Rules Applied to Deep Value Investing
If you don’t understand value investing – or if trailing the market for a couple of years would make you abandon a value strategy – then your best long-term investment is a low-cost broad market index fund. Such an index fund will allow you to beat 85-90% of all investors over the course of several decades. And it takes very little time to implement and maintain this approach.
If you understand value investing, then you should consider a quantitative value fund. A quantitative value fund – like the Boole Microcap Fund – is a fund that systematically picks cheap stocks. Typically, systematic stock selection is fully automated, thereby maximizing long-term results by minimizing human error.
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: http://boolefund.com/best-performers-microcap-stocks/
This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.
There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.
The 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: firstname.lastname@example.org
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.