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

ABSTRACT We propose a conditional factor model for corporate bond returns with five factors and time‐varying factor loadings. We have three main empirical findings. First, our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin. Second, our model recommends a systematic bond investment portfolio whose high out‐of‐sample Sharpe ratio suggests that the credit risk premium is notably larger than previously estimated. Third, we find closer integration between debt and equity markets than found in prior literature.

ABSTRACT Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions. The majority of asset pricing factors (i) can be replicated; (ii) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio; (iii) work out‐of‐sample in a new large data set covering 93 countries; and (iv) have evidence that is strengthened (not weakened) by the large number of observed factors.

ABSTRACT We reconsider trend‐based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock‐level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short‐term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.

ABSTRACT We propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own‐signal predictability, assuming equal strength across securities, our framework includes cross‐predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out‐of‐sample alphas to standard factors in several data sets.