Enhancing stock market return predictability by using a novel autoencoder-based aggregate EPU index

We propose a novel aggregate economic policy uncertainty (EPU) index, which is constructed using an autoencoder to extract the relevant component from eight news-based EPU proxies, for examining the impact of EPU on the stock market returns. We find that the autoencoder-based aggregate EPU index (i) exhibits the strong in-sample and out-of-sample forecasting power, and outperforms the existing EPU measures as well as well-known macroeconomic variables; (ii) generates the considerable economic value for the mean-variance investors in terms of portfolio optimization; (iii) derives its predictive ability primarily from the cash flow channel; and (iv) displays the asymmetric return predictability, with heightened performance in the low-sentiment periods.