Environmental sustainability and stock returns

We apply machine learning methods to granular environmental variables and test if there is a strong positive relation between environmental sustainability and future stock returns. A long-short portfolio that longs stocks with high forecasted returns and sells stocks with low forecasted returns earns large abnormal returns, and it performs better when climate concerns in the media are more intense. Further diagnosis shows that various dimensions of environmental sustainability help return predictions. High forecasted returns are associated primarily with strong environmental operational performance. The return prediction based on a customized transformer model is similar. • With Random Forest, granular environmental variables help stock return prediction. • Other machine learning methods, such as elastic net, provide supportive evidence. • Firms with strong environmental operational performance earn high returns, especially when climate risks are scrutinized.