Precision Factor Investing: Avoiding Factor Traps by Predicting Heterogeneous Effects of Firm Characteristics
We apply ideas from causal inference and machine learning to estimate the sensitivity of future stock returns to observable characteristics like size, value, and momentum. By analogy with the informal notion of a "value trap," we distinguish "characteristic traps" (stocks with weak sensitivity) from "characteristic responders" (those with strong sensitivity). We classify stocks by interpreting these distinctions as heterogeneous treatment effects (HTE), with characteristics interpreted as treatments and future returns interpreted as responses. The classification exploits a large set of stock features and recent work applying machine learning to HTE. Long-short strategies based on sorting stocks on characteristics perform significantly better when applied to characteristic responders than traps. A strategy based on the difference between these long-short returns profits from the predictability of HTE rather than from factors associated with the characteristics themselves. This is joint work with Pu He.
Paul Glasserman
He is also chair of the Financial and Business Analytics Center within Columbia University’s Data Science Institute.
His recent research applies novel empirical signals for trading and risk management, including information from text analysis, volatility roughness, and firm characteristics. His past research recognitions include the INFORMS Lanchester Prize for his book Monte Carlo Methods in Financial Engineering, the Erlang Prize in applied probability, and Risk magazine’s Quant of the Year award.