Forecasting stock returns: A predictor-constrained approach

Zhiyuan Pan, Davide Pettenuzzo () and Yudong Wang

Journal of Empirical Finance, 2020, vol. 55, issue C, 200-217

Abstract: We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls within the variable’s past 24-month high and low. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads to significantly larger forecast gains. We also show how a simple equal-weighted combination of our constrained forecasts leads to further improvements in forecast accuracy, generating forecasts that are more accurate than those obtained using current constrained methods. Further analysis confirms that these findings are robust to the presence of model instabilities and structural breaks.

Keywords: Equity premium; Predictive regressions; Predictor constraints; 24-month high and low; Model combinations (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (18)

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Related works:
Working Paper: Forecasting Stock Returns: A Predictor-Constrained Approach (2018)
Working Paper: Forecasting Stock Returns: A Predictor-Constrained Approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:55:y:2020:i:c:p:200-217

DOI: 10.1016/j.jempfin.2019.11.008

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