Practical Applications of Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing

Practical Applications Summary

In Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing, which appeared in the April 2019 issue of The Journal of Portfolio Management, Rob Arnott of Research Affiliates, LLC, Campbell R. Harvey of the Fuqua School of Business at Duke University, Vitali Kalesnik of Research Affiliates Global Advisors (Europe), Limited, and Juhani Linnainmaa of the USC Marshall School of Business discuss three common mistakes in factor investing: unrealistically high expectations of factor-based returns, drawdowns that far exceed expectations, and less benefit from diversification than most observers might expect. The authors explore each of these problems and discuss how investors can address them in factor-based portfolios.

First, factor returns are likely to fall short of expectations for a variety of reasons, including data mining and backtest overfitting during factor identification, and crowding following publication of a factor. Data mining (searching the data for ideas that historically work without starting with a prior economic rationale for those ideas) paired with selection bias (throwing out anything that did not work) is a near-guarantee of implausible backtest results. Second, factor performance not normally distributed, exhibiting excessive kurtosis and negative skewness. Investors should expect occasional large, even extreme, factor drawdowns. Third, diversification of a portfolio cannot always mitigate the risk of extreme drawdowns due to cross-correlation of factors. Portfolios that combine factors (whether six of the most popular or eight second-tier factors) show drawdowns that are roughly as severe—measured in standard deviations away from the mean—as the individual factors.