This paper considers the problem of modeling a firm’s expected return as a nonlinear function of its observable characteristics. We investigate whether theoretically-motivated monotonicity constraints on characteristics and nonstationarity of the conditional expectation function provide statistical and economic benefit. We present an interpretable model that has similar out-of-sample performance to black-box machine learning methods. With this model, the data provide support for monotonicity and time variability of the conditional expectation function. Additionally, we develop an approach for characteristic selection using loss functions to summarize the posterior distribution. Standard unexplained volume, short-term reversal, size, and variants of momentum are found to be significant characteristics, and there is evidence this set changes over time.
"Monotonic effects of characteristics on returns." Ann. Appl. Stat. 14 (4) 1622 - 1650, December 2020. https://doi.org/10.1214/20-AOAS1351