Certainty of Uncertainty for Asset Pricing

Uncertainty is known to be crucial in asset pricing, yet evidence from comprehensive analysis of various uncertainty measures remains sparse. This paper investigates the predictability of stock returns based on economic fundamentals uncertainty by constructing a novel uncertainty index derived from a heterogeneous range of uncertainty measures utilizing a machine learning method. 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 individual uncertainty measures significantly correlated with market volatility, and becomes more pronounced during high uncertainty and recession periods. The predictability of our uncertainty index aligns with theoretical frameworks linking uncertainty to future investment, cash flows, and market expectations