Swedroe: Digging Into ‘Deep Value’

A review of a book by Tobias Carlisle that looks at the argument for deep value investing.

Wes Gray and Tobias Carlisle’s “Quantitative Value” is an exceptionally well-written and voluminously researched book that anyone interested in value investing should read, 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, Gray and Carlisle offer insights from legendary value investors such as Warren Buffett, Seth Klarman and Joel Greenblatt (creator of Magic Formula investing). You can find my review here.

Deep Value

Carlisle followed up “Quantitative Value” with his own book, “Deep Value.” He begins by noting that deep value is simply “the methodological application of timeless principles proven over 80 years of research and practice.” These principles were laid out by Benjamin Graham, the father of value investing and Warren Buffett’s mentor, in his seminal work, “Security Analysis.”

Carlisle explains: “Through his genius and experience Graham understood intuitively what other researchers would demonstrate empirically over the eight decades since his book was first published: That stocks appear most attractive on a fundamental basis at the peak of their business cycle when they represent the worst risk-reward ratio, and least attractive at the bottom of the cycle when the opportunity is best.” That is why value investors are often referred to as contrarians.

With clear and crisp writing, Carlisle provides a detailed account of the evolution of the various theories of intrinsic value and activist investing from Graham to Buffett to Carl Icahn and beyond. It’s filled with intriguing anecdotes and excellent research on the principles and strategies of deep value investing.

Carlisle begins his tale by noting that “the problem for investors is not only that high growth and unusual profitability don’t persist. Exacerbating the problem in many cases is that the market overestimates the business’s potential, bidding the price of its stock too high relative to its potential. The stock of high quality companies is driven so high that long-term returns are impaired even assuming the high rates of growth and profitability persist. The corollary is true. A company with an apparently poor business will generate an excellent return if the market price underestimates its fair value even assuming the low growth or profitability persist.”

Carlisle explains that investors fail to fully recognize this. He writes: “High growth and high returns invite new entrants who compete away profitability, leading to stagnation, while losses and poor returns cause competitors to exit, leading to a period of high growth and profitability for those business that remain.”

Supporting Evidence

In fact, there are many studies demonstrating the truth of Carlisle’s statement. For example, in the 1997 study “Forecasting Profitability and Earnings,” Eugene Fama and Kenneth French tested whether the theory that profitability reverts to the mean stood up to historical data. They examined the profits of an average of 2,304 firms a year for the period 1964 through 1995. They concluded:

• There is a strong tendency for profits to revert to the mean.
• Reversion to the mean is strongest when profits are highest (the incentive for competition to enter is greatest) and when they are lowest (the incentive to leave an industry and reallocate assets is greatest, thereby reducing competition and restoring profits).
• Abnormally low earnings tend to revert faster than abnormally high profits.
• Reversion to the mean occurs at a rate of about 40% per year.
• Real-world forecasts tend to underestimate the speed at which reversion to the mean in profitability occurs.

Fama and French offered a possible behavioral-oriented explanation for abnormally low earnings reverting faster than abnormally high earnings. They hypothesized that when reporting bad news, companies become very conservative and try to get all the bad news out of the way at one time (often blaming previous management). On the other hand, they tend to spread good news out over time.

If, in fact, reversion to the mean occurs faster than the market anticipates, it would help explain why growth stocks underperform value stocks. Specifically, the market may simply be overestimating the amount of time growth companies can generate abnormal profits. Ultimately, earnings expectations are not met, and this gets reflected in lower equity returns.

The reverse is true of value companies. The market appears to overestimate the time it takes for abnormally low profits to revert to the mean. Ultimately, earnings expectations are exceeded, and this is reflected in higher returns. Several other studies, including “Returns to E/P Strategies, Higgledy-Piggledy Growth, Analysts’ Forecast Errors, and Omitted Risk Factors,” published in the Winter 1993 issue of the Journal of Portfolio Management, have reached the same conclusion.

The body of evidence led Carlisle to conclude: “It is this unusual contrast between the statistical likelihood of mean reversion—it’s the probable outcome—and the fact that the market prices securities as if it’s a remote possibility, that keeps deep value investing so profitable and interesting.”

Valuation Trumps Growth
Through a thorough analysis of the historical data, Carlisle presents an impressive case demonstrating that valuation is more important than growth in constructing portfolios.

He shows that “cheap, low-growth portfolios systematically outperform expensive, high-growth portfolios, and by wide margins.”

Carlisle also presents another counterintuitive finding: “Even in the value portfolio, high growth leads to underperformance and low or no growth leads to outperformance.”

He notes: “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 the uglier the stock, the better the return, even when valuations are comparable.”

For example, Carlisle showed that, over the period 1951 through 2013, a “glamour value” portfolio held stocks with an enterprise multiple of 8.2 while “deep value” portfolios held stocks with an enterprise multiple less than half that at just 3.9. The result was that deep value stocks returned 15.7% versus 8.8% for the glamour value portfolio.

He believes that “the enterprise multiple identifies many undervalued companies with so-called ‘lazy’ balance sheets and hidden or unfulfilled potential.” Carlisle writes that activist investors target these undervalued, cash-rich companies, seeking to improve the intrinsic value and close the market price discount by increasing payout ratios.

Best Approach?

He also examined the question of which approach to value investing in the battle of “human versus machine” is superior. Carlisle observes that “resistance to the application of statistical prediction rules in value investment runs deep.” And he goes on to explain there is evidence that “experts make better decisions when they are provided with the results of statistical prediction.”

However, the research also finds that “experts predict less reliably than they would have if they had just used the statistical prediction rule. The statistical prediction rule tends to be the ceiling from which the expert detracts, rather than a floor to which the expert adds.”

Carlisle explains: “Our resistance to the statistical-prediction rule findings is due to our tendency to be overconfident in our ability to reason subjectively and hence the reliability of our predictions. It is self-reflective: Our confidence in our reasoning abilities reinforces our confidence in our judgments, and our overconfident judgments encourage our belief in the reliability of our faculties.”

Along with “The Physics of Wall Street,” “Successful Investing Is a Process,” “Expected Returns” and “Quantitative Value,” Carlisle’s 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.