Stock return prediction: Stacking a variety of models

Albert Bo Zhao and Tingting Cheng

Journal of Empirical Finance, 2022, vol. 67, issue C, 288-317

Abstract: 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.

Keywords: Stock return; Out-of-sample performance; Combination forecast; Machine learning; Stacking (search for similar items in EconPapers)
JEL-codes: C58 G12 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:67:y:2022:i:c:p:288-317

DOI: 10.1016/j.jempfin.2022.04.001

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