The idea that seemingly cheap securities, according to measures of fundamental and intrinsic value, outperform seemingly expensive securities has been scrutinized by academics for more than 30 years, yet the value strategy is still widely misunderstood. Recent research that updated the extensively cited Fama–French three-factor model introduced two new factors that claim to make the value factor redundant. The authors identify a number of facts and fictions about value investing that need clarification.
How Is This Research Useful to Practitioners?
Learning is the acquisition of knowledge, but much of what practitioners already know has never been very accurate in the first place. By pointing out fictions as well as facts, the authors help the reader let go of misconceptions—that is, the unlearning curve. They discuss enough characteristics of the value approach to convert the casual reader into a “factor wonk,” but the most salient discoveries are those that relate to applying the latest research.
Fama and French’s introduction of the three-factor model in 1993 is the most widely cited article in financial economics, but their new five-factor model (working paper 2014) potentially rearranges the value landscape. Fama and French added a profitability factor (RMW) and an investment factor (CMA) to their original construct, potentially making the original value factor redundant. Given the recent popularity of factor-based models, such as smart beta and fundamental indexing, the relative importance of the original value metric and the impact of more up-to-date calculations of high minus low is exceptionally fertile ground for research.
According to the new model, the highest expected returns can be expected from small value companies that are profitable and not embarking on major growth initiatives. At first glance, the new factors appear to be at odds with the original ones because profitable companies tend to be larger and sell at higher book-to-market multiples than traditional value stocks. The authors attempt to integrate new research on value with preexisting practitioner perspectives.
How Did the Authors Conduct This Research?
The authors critically examine several pros, cons, and ancillary beliefs surrounding value investing by pointing out four fictions and five facts. They use a mix of counterexamples, analogies, and references to existing research to make their points. These new multifactor approaches and concepts, such as fundamental indexing, are largely empirical models.
The authors present an anthology of recent research and insight from practitioner journals on the value approach, so portions of the article are more theoretical in nature, such as exploring whether the returns represent compensation for risk, a behavioral anomaly, or a little of both. They cite their own research to make the point that the principle of buying assets at a historically low price-to-value ratio can be profitably applied to other asset classes, such as currencies or bonds. They do not challenge the methodology of existing research.
Efficiencies in integrating multiple engines of added value are more interesting than debates about whether rules-based strategies are, by definition, passive. Berkshire Hathaway’s vice chairman, Charlie Munger, is widely credited with expanding Buffett’s value-only investment opportunity set to include a quality pillar, and he probably did not require math to make his point. The notion is simple: The quality screen means you do not buy the cheap value stocks that are cheap because they are bad, and the value screen means you do not overpay for the quality stocks that are expensive.
Value investing as a solo strategy may soon be a relic of investment history. New product development purporting that value investing works most effectively with other strategies is supported by backtesting and academic research, but there should be a distinct rationale for why a factor works. Strategies that passively capture risk premiums need to be able to answer the question, “Why higher returns?” One viewpoint often advocated by Fischer Black of Black–Scholes fame is that factor strategies, lacking support in asset-pricing theory, may be the result of little more than data mining. The number of papers and equity factor “discoveries” in recent years is enormous and may reflect the increasing sophistication of data mining rather than enduring anomalies.
Financial economist Robert Novy-Marx is credited with publishing a number of insights that address the importance of controlling for quality in value portfolios, but the notion of using value in combination with other factors that serve as a proxy for profitability is not new. In fact, it is as old as the original value theory itself: In the late 1920s, Benjamin Graham and David Dodd advocated a form of value investing that involved buying profitable but undervalued assets. In fact, Graham wrote as much about firm quality as he did about stock price, but he is identified purely with traditional value strategies.