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Forecasting stock returns: A predictor-constrained approach


  • Pan, Zhiyuan
  • Pettenuzzo, Davide
  • Wang, Yudong


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.

Suggested Citation

  • Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
  • Handle: RePEc:eee:empfin:v:55:y:2020:i:c:p:200-217
    DOI: 10.1016/j.jempfin.2019.11.008

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    More about this item


    Equity premium; Predictive regressions; Predictor constraints; 24-month high and low; Model combinations;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates


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