Banks adjust loan spreads after observing natural disasters linked to climate change. We isolate this updating process by identifying loans to borrowers at risk of, but not directly affected by, such disasters. Loan spreads for these borrowers spike in both primary and secondary markets, while no such updating occurs for non–climate‐related disasters. Evidence suggests a heightened perceived credit risk, which nonetheless cannot fully explain the increase in rates. Taken altogether, increased spreads are explained primarily by salience bias, as they are short‐lived and amplified by media attention. This salience impacts financial decisions at bank‐dependent firms.

We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application.