We’ll gladly bet a simple blend of market, value, low beta and momentum exposures against anyone’s optimized 81-factor portfolio.
Factors are becoming so numerous and exotic that John Cochrane referred to the collection as a zoo.1 While the concept is entertaining, the proliferation of factors is deeply troubling. The sheer number of factors suggests that it’s better to have more factors than less, but how can investors determine how to use factors in their equity portfolios? The options are endless, particularly given the smart beta movement under way today. We believe one cannot make intelligent choices regarding smart betas without first understanding factors and their role in investment portfolios. Luckily for investors, most so-called factors can be ignored.
When we were in the Ph.D. program at UCLA, we were taught the four-factor model in our asset pricing class. The world was simple; there were the market risk factor and the value, small-cap, and momentum return factors.2 The three non-market factors carried juicy return premia that could be had by investors willing to diversify into non-market exposures and exploit retail investors’ behavioral biases.
Fifteen years later, we are shocked to learn that some quant shops now use an 81-factor model to build equity portfolios. This inflation in factors has certainly made us feel inadequate and has potentially eroded the real value of our paper diploma. Understandably, we are concerned with the relentless onslaught of shiny, exciting, and sexy new factors introduced by bright-eyed, bushy-tailed young financial engineers.3
Frankly, we expected the number of “accepted” factors to decrease rather than explode over time. We expected that at least one of the three documented anomalies would be revealed as a fluke—a data artifact that would disappear with better quality international data and with additional decades of out-of-sample data following the original discovery.
Indeed, that is what we have seen. The small-cap anomaly has not been observed in the United States since the early 1980s and does not exist outside the U.S. dataset (Table 1). This lack of “robustness” out-of-sample led Tyler Shumway and Vincent Warther to re-examine the small-cap anomaly; they concluded that it was likely driven by a mistake in how researchers treated missing data for delisted stocks. Apparently, missing returns for delisted stocks in the CRSP database created a systematic bias in the computed returns for small stocks, which are more likely to face delisting. When this bias is adjusted for, the small-cap anomaly is no longer observed (Shumway and Warther, 1999).