A Portfolio Perspective on the Multitude of Firm Characteristics

Hundreds of variables have been proposed to predict the cross-section of stock returns; see, for instance, Harvey, Liu, and Zhu (2015), McLean and Pontiff (2016), and Hou, Xue, and Zhang (2016). Our goal is to investigate which firm-specific characteristics matter jointly from a portfolio perspective; that is, from the perspective of an investor who cares not only about average returns but also about portfolio risk, transaction costs, and out-of-sample performance. To achieve our goal, we consider a dataset with 100 firm-specific characteristics and focus on three research questions. First, which characteristics are jointly significant from a portfolio perspective and why? Second, how does the answer to this question change with transaction costs? Third, can an investor identify ex-ante combinations of characteristics that result in good out-of-sample performance? We find only a small number of characteristics—six—are significant without transaction costs. With transaction costs, the number of significant characteristics increases to 15 because the trades in the underlying stocks required to rebalance different characteristics often net out. We show how investors can identify combinations of characteristics with abnormal out-of-sample returns net of transaction costs that are not fully explained by the Fama and French (2015) and Hou, Xue, and Zhang (2014) factors.