Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text
从《华尔街日报》新闻文本中提取叙事因子，构建定价模型，发现其比传统特征因子有更高的样本外夏普比率和更小的定价误差，并能预测未来投资机会。
Abstract We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online