Firm Characteristics and Empirical Factor Models: A Model Mining Experiment

• 成果类型: Article
• 署名作者: Tian, Mary
• 署名单位: Federal Reserve System - USA; Federal Reserve System Board of Governors
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa126
• 发表日期: 2021
• 页码: 6087
• 关键词: cross-section; INVESTMENT; anomalies; return; stocks; tests; RISK; INFORMATION; performance; GROWTH

摘要

In a novel model mining experiment, we data mine hundreds of randomly constructed three-factor models and find that many outperform well-known models from the literature, including those with four and five factors. The results provide compelling evidence that the threshold of factor model success needs to be raised. Confidence intervals for model rankings, derived from a bootstrap simulation, offer new insights into the consistency of a model's pricing ability. Rankings for some well-known models are unusually volatile, which have wider confidence intervals than that of most of the random factor models.