Swedroe: Bias Can Override Knowledge

The introduction to the new book I co-authored with Andrew Berkin, “Your Complete Guide to Factor-Based Investing,” begins: “If a poll was taken asking investors to name the greatest investor of all time, it is safe to say that the vast majority would likely respond, ‘Warren Buffett.’

Thus, we could say that a major goal of investors the world over is to find Buffett’s ‘secret sauce.’ If we could identify it, we could invest like him—assuming we also had his ability to ignore the noise of the market and avoid the panicked selling that causes so many investors to incur the higher risk of stocks while ending up with lower, bond-like returns. This book is in part about the academic community’s search for that secret sauce—specifically the characteristics of stocks and other securities that both explain performance and provide premiums (above market returns). Such characteristics can also be called factors, which are simply properties or a set of properties common across a broad set of securities. Thus, a factor is a quantitative way of expressing a qualitative theme.”

Indeed, quantitative approaches allow investors to express qualitative themes in a systematic way that avoids the all-too-human behavioral errors to which investors are prone. Wes Gray and Tobias Carlisle’s book, “Quantitative Value,” is all about finding the aforementioned secret sauce and implementing it in a systematic way.

It’s an exceptionally well-written and voluminously researched book that should be a must-read for anyone interested in value investing, whether you believe the source of the value premium is risk-based or behavioral-based. In addition to presenting a wealth of academic research on value investing, the authors offer insights from legendary value investors such as Warren Buffett, Seth Klarman and Joel Greenblatt (creator of Magic Formula Investing).

More Thought Doesn’t Always Mean More Returns

In the opening chapter, Gray and Carlisle present Buffett’s thinking on the issue of value investing: “Most institutional investors in the early 1970s … regarded business value as of only minor relevance when they were deciding the prices at which they would buy or sell. This now seems hard to believe. However, these institutions were then under the spell of academics at prestigious business schools who were preaching a newly-fashioned theory: the stock market was totally efficient, and therefore calculations of business value—and even thought, itself—were of no importance in investment activities. (We are enormously indebted to those academics: what could be more advantageous in an intellectual contest—whether it bridge, chess or stock selection than to have opponents who have been taught that thinking is a waste of energy.)”

But before being swayed by the idea that “good management that is thinking must be better than nonthinking management,” Gray and Carlisle present evidence that even experts have a very difficult time outperforming systematic approaches that rely on having identified the key metrics that matter in decision-making.

For example, they present the case of Lewis Goldberg, a psychology professor, who in 1968 analyzed more than 1,000 patients’ Minnesota Multiphasic Personality Inventory (MMPI) test responses and their final diagnoses as neurotic or psychotic. He used the data to develop a simple model to predict the final diagnosis based on the MMPI test results.

He found that, out-of-sample, his model had a 70% accuracy rate. He then gave the MMPI scores to both experienced and inexperienced clinical psychologists and asked them to diagnose the patient. Goldberg found that his simple model outperformed even the most experienced psychologists.

Taking it one step further, Goldberg ran the test again, this time providing the clinical psychologists with his simple model’s predictions. Goldberg was shocked to find that, while their performance did improve, they still underperformed the model even with the benefit of its predictions. The conclusion an observer might draw is that the results of quantitative models might be a performance ceiling from which humans are more likely to subtract (due to behavioral biases, such as overconfidence) than exceed.

Investors Cannot Resist Temptation

Before you take exception to this conclusion, Gray and Carlisle offer the following from legendary value investor Seth Klarman: “So if the entire country became security analysts, memorized Benjamin Graham’s [book, “The Intelligent Investor,”] and regularly attended Warren Buffett’s annual shareholder meetings, most people would, nevertheless, find themselves irresistibly drawn to hot initial public offerings, momentum strategies, and investment fads. People would still find it tempting to day-trade and perform technical analysis of stock charts. A country of security analysts would still overreact. In short, even the best-trained investors would make the same mistakes that investors have been making forever, and for the same immutable reason—they cannot help it.”

Gray and Carlisle conclude that “tricking ourselves into doing the right thing works better than simply trying to do the right thing.” The reason is that “even once we are aware of our biases, we must recognize that knowledge does not equal behavior. The solution lies in designing and adopting an investment process that is at least partially robust to behavioral decision-making errors.”

They go on to note that the research shows we, as humans and investors, find it hard to overcome cognitive biases, such as overconfidence. Gray and Carlisle quote James Montier, a noted behavioral expert, who stated: “We think we know better than simple models, which have a known error rate, but prefer our own judgment, which has an unknown error rate.” In other words, we tend to overweight our own opinions and experiences even in the face of statistical evidence to the contrary.

Quantitative investing’s objectiveness acts as a shield, protecting us against our own biases, while also acting as a sword, allowing us to exploit the cognitive biases of others. Providing further support for the power of this approach, Gray and Carlisle present this example from Joel Greenblatt. Greenblatt’s firm, Gotham Capital, had compounded at a phenomenal rate of 40% annually, before fees, for the 10 years from Gotham’s formation in 1985 to its return of outside capital to investors in 1995.

A Magic Formula

In his book, “The Little Book That Beats the Market,” Greenblatt describes an experiment he conducted in 2002. Greenblatt wanted to know if Buffett’s investment strategy could be quantified. So he studied Buffett’s annual shareholder letters and developed his “Magic Formula,” which he also published.

Gray and Carlisle show that study after study has found “the model is the ceiling of performance from which the expert detracts, rather than the floor to which the expert adds. Even Greenblatt has said he cannot outperform the Magic Formula.”

The rest of Gray and Carlisle’s “Quantitative Value” is a detailed and methodological look at various value metrics tested in the literature, including, among others, price-to-cash flow, book value, earnings and earnings before interest and taxes (and also before depreciation). They also address quality measures, such as return on capital, return on assets and gross profitability, as well as financial strength measures, such as the Ohlson O-score and Piotroski’s F-score.

In other words, they have provided readers with a behind-the-curtain look into the black boxes of the best minds in finance, both academics and practitioners. One might consider it the ultimate guide to Graham-and-Doddsville.

Along with “The Physics of Wall Street,” “Successful Investing Is a Process” and “Expected Returns,” this book is a must-read for serious investors, and there shouldn’t be any other kind.

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.