MONOTONIC EFFECTS OF CHARACTERISTICS ON RETURNS

• 成果类型: Article
• 署名作者: Fisher, Jared D.; Puelz, David W.; Carvalho, Carlos M.
• 署名单位: University of California System; University of California Berkeley; University of Chicago; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1351
• 发表日期: 2020
• 页码: 1622-1650
• 关键词: cross-section; variable selection; momentum; prices; RISK; stocks

摘要

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 non-stationarity 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.