Oddly, the nullification of the small-cap anomaly has received scant notice. At the same time, between academia and the investment industry, the mining of new equity anomalies has yielded a great many new return factors that offer exotic and diversifying premia. Today, quantitative managers and smart beta solution providers peddle breathtaking Sharpe ratios from back-tested equity portfolios, which optimally hold uncorrelated “pure” factors. As providers rush to outdo each other, the number of shiny new factors and the resulting portfolio Sharpe ratios have both grown improbably large.
We can completely understand the providers’ incentive to “factor-proliferate.” Equally, we can sympathize with the investors’ desire to believe that there might really be 81 unique factors which can be combined to provide an equity portfolio with a Sharpe ratio of 2. We have seen this very same phenomenon in the “alpha” space. Managers, consultants, and investors alike dream that lush dream of combining diversifying alpha portfolios to create an equity core with an information ratio of 2. For those of us who are skeptical of “alphas,” this is our chance to dream that same dream in the smart beta space. Except, of course, we get to invoke the arbitrage pricing theory (APT) framework and throw around big words like multi-factors and optimization, which gives our version an added air of intellectual rigor and authority.
Now, in fairness to our academic colleagues and professors, we were warned. Recognizing the dangerous combination of cheap computing power and overzealous young finance Ph.D. students, our professors explained that if one runs 10,000 back-tests, one is bound to discover a few incredible factors which generate huge Sharpe ratios. These “data-mined” factors will unfortunately offer no future premia. Recognizing further that Ph.D. students tend to believe everything they read in finance journals, our professors quickly added that with 10,000 academics globally, each running one honest back-test a year, we would similarly end up with “data-snooped” factors, reported in top journals, which would have no greater probability of delivering future premia.4 The first lesson is generally well understood in our industry; the second lesson is less widely understood. Now, given the proliferation in factors, academia is increasingly turning up the volume to warn against “factor” proliferation.
A Zoo Of Factors
Perhaps the proliferation of new factors has hit the tipping point. McLean and Pontiff (2013) re-examined 82 factors published in tier-one academic journals. They were only able to replicate the reported results for 72 of them; at least 10 out of 82 factors were artifacts of reporting mistakes in the databases, which have now been corrected. Levi and Welch (2014) took the kitchen-sink approach and examined 600 factors from both the academic and practitioner literature. They found that 49% of the factors produced zero to negative premia out-of-sample, suggesting that investing based on the identified factors is only ever so slightly better than tossing coins. Of course, net of transaction and management fees, tragically, you will likely do worse than a monkey throwing darts.