Nonparametric predictive regression for stock return prediction

Tingting Cheng, Jiti Gao, Oliver Linton and Yayi Yan

Econometric Reviews, 2025, vol. 44, issue 10, 1462-1493

Abstract: We propose a multi-step nonparametric predictive regression model, which allows for general locally stationary predictors and time-varying/nonlinear return predictability. We propose a kernel estimation method and establish the large sample properties in both short and long horizons. We apply our method to stock return prediction using a number of standard predictors such as dividend yield. The empirical results show that our proposed model can outperform the historical mean benchmark, linear predictive regression model, and several machine learning methods for some cases in terms of out-of-sample forecasting performance. We also compare our method with the historical mean benchmark using an economic metric. In particular, we show how our methods could be used to deliver a trading strategy that beats the buy-and-hold strategy over our sample period.

Date: 2025
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Working Paper: Nonparametric Predictive Regressions for Stock Return Prediction (2019)
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DOI: 10.1080/07474938.2025.2519389

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