Stock return prediction: Stacking a variety of models

We employ an ensemble learning approach, stacking, to refine and combine a variety of linear and nonlinear individual stock return prediction models. In an application of forecasting U.S. market excess return, stacking with a simple structure can outperform the traditional historical mean benchmark, Mallows model averaging, simple combination forecast, complete subset regression, combination elastic net forecast, and several other models in terms of both in- and out-of-sample performance measures on a consistent basis. More importantly, we find that the out-of-sample gains of stacking are especially evident during extreme downside market movements. Overall, stacking can generate substantive improvements in market excess return predictability.

Automated summary

In this study, we utilize the ensemble learning approach of stacking to refine and combine various linear and nonlinear models for stock return prediction. We find that stacking with a simple structure outperforms traditional benchmarks and other models, both in terms of in- and out-of-sample performance measures, particularly during extreme market downturns.