Swedroe: The Problems With Formulaic Value Investing

May 24, 2017

U-Wen Kok, Jason Ribando and Richard Sloan are the authors of the paper “Facts About Formulaic Value Investing,” which will appear in an upcoming issue of the Financial Analysts Journal.

The authors begin by noting: “The term ‘value investing’ is increasingly being adopted by quantitative investment strategies that use ratios of common fundamental metrics (e.g., book value, earnings) to market price. A hallmark of such strategies is that they do not involve a comprehensive effort to determine the intrinsic value of the underlying securities.”

Kok, Ribando and Sloan find the following:

  • The book-to-market ratio systematically identifies securities with overstated book values that are subsequently written down.
  • The trailing earnings-to-price ratio systematically identifies securities with temporarily high earnings that subsequently decline.
  • The forward earnings-to-price ratio systematically identifies securities for which sell-side analysts offer relatively more optimistic forecasts of future earnings.       

The authors conclude that quantitative investment strategies based on such ratios are not good substitutes for value-investing strategies that use a comprehensive approach in identifying underpriced securities.

They cite the Dimensional Fund Advisors (DFA) US Large Cap Value Portfolio (DFLVX) as an early example of such simple formulaic quant (quantitative analysis) strategies: “Begun in 1993, the fund described its investment strategy as follows: The portfolio seeks to capture return premiums associated with high book-to-market ratios by investing in the US Large Cap Value Series of the DFA Investment Trust Company, which in turn invests on a market cap weighted basis in companies that are approximately $500 million or larger in market cap and have book-to-market ratios in the upper 30% of publicly traded companies.”

Clarifying Formulaic Value Strategies

Essentially, the authors are making the case that formulaic value strategies as employed by firms such as DFA should not be confused with value strategies that employ a comprehensive approach to determine the intrinsic value of the underlying securities—an approach used by the legendary value investors Benjamin Graham and David Dodd.

Wes Gray, author of what I consider to be two must-read books, “Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors” and “Quantitative Momentum: A Practitioner's Guide to Building a Momentum-Based Stock Selection System,” provides an in-depth analysis and critique of Kok, Ribando and Sloan’s paper.

Among the issues raised by Gray is that “Facts About Formulaic Value Investing” doesn’t address the real question of whether man (fundamental analysis based on human judgments) or machine (formulaic quantitative strategies) is the superior approach. It only raises the question of whether there may be better approaches than a simple BtM screen.


Channeling Graham And Dodd

Kok, Ribando and Sloan state: “A capable analyst, however, should be able to significantly enhance quantitative approaches with Graham and Dodd-style security analysis.”

In fact, the authors note that including momentum screens can improve the performance of a BtM-only formula: “By conditioning on both a high fundamental-to-price ratio and positive momentum, we can weed out some of the stocks whose fundamentals are temporarily inflated because of a delayed accounting response to deteriorating business conditions.”

On this important issue of whether you can improve on a simple BtM formula approach to value investing, Gray and I agree with the authors. And so does the investment firm Dimensional Fund Advisors. (Full disclosure: My firm, Buckingham Strategic Wealth, recommends DFA funds in constructing client portfolios.)

For example, since 2003, DFA has included screens for negative momentum in all their equity strategies, not just value strategies. And in 2013, they added a screen for profitability. In addition, they have for a long time included screens for securities that the research has found have poor return characteristics (are anomalies).

For example, DFA excludes extreme small growth stocks (those with high investment and low profitability), IPOs, penny stocks and stocks in bankruptcy. In other words, formulaic value strategies can be more complex than a simple BtM strategy.

Human Insight Is Key

Returning to the question of man versus machine, in a recent Business Insider article, U-Wen Kok (one of the authors of the study) states the following: “This paper emphasizes the importance of not just going with a quant screen or simple model … You need human insight.”

Before providing evidence on the man-versus-machine debate, here’s a simple test of that hypothesis. If man were superior to a well-designed formulaic approach, we should see such formulaic approaches (like those used by two of the leading providers of formulaic value funds, the index funds of Vanguard and the structured portfolios of Dimensional Fund Advisors (specifically mentioned by the authors)), underperforming.

To test this hypothesis, we’ll look at the most recent 15-year period (2002 through 2016). The table below shows the Morningstar percentile rankings (1 being the highest ranking and 100 being the lowest) adjusted for survivorship bias:

The average ranking for the five DFA funds is 2.2. Their simple formulaic strategies that do not rely on the humans that Kok, Ribando and Sloan believe are needed to produce superior results outperformed 97.8% of their competitor funds run by those humans. That’s before taxes are considered.

And since taxes are typically the largest expense of actively managed funds, the figure would almost certainly be even higher on an after-tax basis. The average ranking for the two Vanguard index funds was 20.5, outperforming almost 80% of actively managed value funds using superior human judgment.

We’ll now take a further look at the issue of man versus machine.


Man Vs. Machine

Fortunately, the question of whether human experts can reliably beat algorithms has already been addressed. We’ll begin with a study by Lewis Goldberg, a psychology professor who, in 1968, analyzed the Minnesota Multiphasic Personality Inventory (MMPI) test responses of more than 1,000 patients and their final diagnoses as neurotic or psychotic.

Goldberg used this 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 the model’s predictions. Goldberg was shocked that, while their performance did improve, they still underperformed the model, even armed with the benefit of its predictions.

Performance Ceiling

The conclusion one might draw is that the results of quantitative models may be a performance ceiling from which humans are more likely to subtract (due to our behavioral biases, such as overconfidence) than exceed.

We also have further evidence from the investment world. Campbell Harvey, Sandy Rattray, Andrew Sinclair and Otto Van Hemert provide evidence on the subject with their December 2016 paper “Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance.”

They analyzed and contrasted the performance of systematic hedge funds, which use rules-based strategies involving little or no daily intervention by humans, with the performance of discretionary hedge funds, which rely on human skills to interpret new information and make the day-to-day investment decisions.

The study covered the period 1996 through 2014 and included data on more than 9,000 macro and equity hedge funds. To adjust returns for exposure to common factors, they used stock factors (beta, size, value and momentum) and bond factors (term and credit) as well as FX (foreign exchange) carry and volatility.

Discretionary Funds Preferred

Investors have a clear preference for discretionary funds, given that they make up about 70% of the hedge fund universe and control approximately 75% of the assets under management.

However, the authors found no evidence to support such a preference. For equity hedge funds, they found both that, after adjusting for exposure to well-known risk factors, risk-adjusted performances were similar and that for discretionary funds (in aggregate), more of the average return and volatility of returns can be explained by risk factors.


In addition, when looking at what they called the “appraisal ratio” (the ratio of the average risk-adjusted return to its volatility), the authors found that systematic funds outperformed discretionary funds 0.35 to 0.25.

For macro funds, they found systematic funds outperformed discretionary funds both on an unadjusted and on a risk-adjusted basis. The appraisal ratios were 0.44 for systematic funds and just 0.31 for discretionary funds. They concluded “the lack of confidence in systematic funds is not justified.”

In their aforementioned book, “Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors,” Wes Gray and Tobias Carlisle provide further support for the power found in systematic, quantitative investing.

Protected Against Our Biases

They write that the objectiveness of the approach 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. To make this point, they presented the following 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.

In his own book, “The Little Book That Beats the Market,” Greenblatt describes an experiment he conducted in 2002. Greenblatt wanted to know if Warren Buffett’s investment strategy could be quantified.

He studied Buffett’s annual shareholder letters and developed his “Magic Formula,” which he 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 that he cannot outperform the Magic Formula.”



Advances in academic research since the Fama-French paper “The Cross-Section of Expected Returns” was published in 1992 have led to improvements in quantitative value strategies beyond the simple screening for stocks with low prices to book value.

Today each of the companies my firm uses to implement value strategies—DFA, AQR Capital and Bridgeway Capital Management—all use more complex construction rules. Depending on the particular fund, they may include not only screens for momentum and profitability but various other value metrics that they believe have been shown to add value (such as price-to-cash flow and price-to-enterprise value).

There are other benefits from a formulaic approach as taken by such firms as DFA and Vanguard. Their portfolios are designed to be broadly diversified. In their attempts to try to improve returns, which have unknowable outcomes, individual stock-picking limits the ability to achieve broad diversification. Thus, it tends to lead to more concentrated portfolios, which in turn leads to more extreme outcomes.

As you have seen, the combination of broad diversification, a consistent focus on current price information and efficient implementation with thoughtful trading and turnover is the strategy that is far more likely to allow you to achieve your financial and life goals.

Approaches Unequal

Before closing, I offer these words of caution: Not all rules-based approaches are created equal—you saw that in the differences in rankings of the similar DFA and Vanguard funds. Many quants have underperformed or blown themselves up throughout history. It takes human insight and expertise to decide what rules make sense. Thus, it’s important to perform thorough due diligence on a fund sponsor before investing.

And finally, as the 15-year rankings of DFA’s and Vanguard’s value funds demonstrated, when it comes to the debate about whether man or machine is superior, the evidence seems overwhelming to me. In other words, if it were a prize fight, we’d have a TKO by now.

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


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