New Methods for the Cross-Section of Returns
这篇编辑导言总结了《金融研究评论》特刊中关于从股票收益率横截面和时间序列中提取随机贴现因子信息的最新方法，包括处理复制问题、假发现以及应用机器学习识别资产定价因子，对从事实证资产定价和金融大数据研究的学者有参考价值。
Abstract 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.