New Methods for the Cross-Section of Returns

• 成果类型: Article
• 署名作者: Karolyi, G. Andrew; Van Nieuwerburgh, Stijn
• 署名单位: Cornell University; Columbia University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa019
• 发表日期: 2020
• 页码: 1879
• 关键词: PRESIDENTIAL-ADDRESS; market value; RISK; anomalies

摘要

The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors-many more than seem plausible-to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.