A trend among investment managers is to focus on “evidence-based” investing, which boils down to adopting strategies that have strong empirical support and ideally a strong theoretical foundation to avoid spurious results. Unfortunately, it is quite common for investment strategy results to offer compelling performance when studied “in-sample” (data or time-period of the original research), but that perform poorly out-of-sample (data or time-period not of the original research). Researchers Campbell Harvey, Yan Liu and Hequing Zhu (HLZ) 2016 argue that so many variables get tested to explain stock returns that by sheer luck many will have convincing empirical evidence “in-sample.” They go on to say that the cleanest way to evaluate research is through out-of-sample testing. They admit that unfortunately, few investment studies deliver on such standards.
No research or researcher is immune from being held accountable to in/out-of-sample effects. Some events are much larger in hindsight than they originally seem. The Fama/French 1992 3-Factor Model, and the introduction of the “value factor” was such an event. In an investment management context, it ranked somewhere between Elvis Presley and The Beatles appearing on The Ed Sullivan Show. Essentially this work birthed the Quantitative Value Investing field, which has given rise to numerous investment management firms utilizing this research and its derivative insights. The premise to this work is simple: identify companies trading at a high ratio of book value to market value (i.e., cheap based on book value), hoping that enough of these cheap companies are also undervalued so that overall, the portfolio rises faster than the market in general.
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