发表期刊：Journal of Empirical Finance
发表时间：Sep 2024
作者及单位：Fuwei Jiang (Department of Finance at School of Economics, Wang Yanan Institute for Studies in Economics, Xiamen University), Jie Kang*, Lingchao Meng
摘要：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 economic uncertainty index derived from a heterogeneous range of uncertainty measures and investigate its predictability of stock returns. 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 economic uncertainty index aligns with theoretical frameworks linking uncertainty to future investment, cash flows, and market expectations.
关键词：Economic uncertainty; Asset pricing; Return predictability; Machine learning; Market expectation
