Forecasting Stock Returns with Model Uncertainty and Parameter Instability, a paper co-authored by our school’s Associate Professor Jiang Fuwei, Professor He Qiang, and PhD student Zhang Hongwei of CUFE’s Tilburg Program, was officially accepted for publication by the Journal of Applied Econometrics, a world-renowned journal on applied economics. In this paper, Professor Jiang and his co-authors put forward a brand-new portfolio forecasting method which can significantly raise the forecasting accuracy for equity risk premium or expected stock returns. It has hence made important academic contributions to researches on empirical asset pricing, financial machine learning and quantitative investment, among others.

First of all, the paper comprehensively looks into the forecasting capability of multiple model averaging methods (including KS, BMA, MMA, JMA, WALS, LASSO and ENet) as well as machine learning algorithms for equity risk premium, and comes to an unexpected conclusion that the out-of-sample forecasting accuracy of most models is very low. Next, from the economic theory perspective, it suggests that this is because these classic models have all neglected the inherent characteristics of financial markets such as dynamism, evolution and frequent structural changes; but financial markets feature model uncertainty and parameter instability, so these models which are built on the assumption of stable data generation have all lost forecasting accuracy.

Then it comes up with a brand-new portfolio forecasting method with model uncertainty, parameter instability and shrinkage all taken into consideration, and applies it to model averaging (such as KS and LASSO) and classic machine learning algorithms. The new method can substantially boost the out-of-sample forecasting capability of these classic model averaging and machine learning methods for equity risk premium. Moreover, it can also accurately forecast such macroeconomic indicators as GDP, economic sentiment and probability of recession. From both the statistical theory perspective of bias-variance tradeoff, and the economic theory perspective of time-varying macroeconomic risks and time-varying risk aversion, the paper explains why the new portfolio forecasting method can greatly enhance forecasting accuracy.