Certainty of uncertainty for asset pricing

Uncertainty is known to be crucial in asset pricing, yet evidence from a comprehensive analysis of various uncertainty measures remains sparse. By machine learning, we construct a novel uncertainty index derived from a heterogeneous range of uncertainty measures and investigate the predictability of stock returns based on economic fundamental uncertainty. Our composite uncertainty index exhibits robust in- and out-of-sample predictability of stock market returns over the one- to 12-month horizon. The predictive power stems from the volatility-orthogonal components of individual uncertainty measures and becomes more pronounced during high uncertainty and high sentiment periods. The predictability of our uncertainty index aligns with theoretical frameworks linking uncertainty to future investment, cash flows, and market expectations.