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Forecasting Stock Returns: A Predictor-Constrained Approach

Author

Listed:
  • Davide Pettenuzzo

    () (Brandeis University)

  • Zhiyuan Pan

    () (Southwestern University of Finance and Economics, Institute of Chinese Financial Studies)

  • Yudong Wang

    () (School of Economics and Management, Nanjing University of Science, Technology, and Economics)

Abstract

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it at zero whenever its value falls below the variable's past 12-month high. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads tosignificantly larger forecasting gains, both in statistical and economic terms. We also show how a simple equal-weighted combination of the constrained forecasts leads to further improvements in forecast accuracy, with predictions that are more precise than those obtained either using the Campbell and Thompson (2008) or Pettenuzzo, Timmermann, and Valkanov (2014) methods. Subsample analysis and a large battery of robustness checks confirm that these findings are robust to the presence of model instabilities and structural breaks.

Suggested Citation

  • Davide Pettenuzzo & Zhiyuan Pan & Yudong Wang, 2017. "Forecasting Stock Returns: A Predictor-Constrained Approach," Working Papers 116R, Brandeis University, Department of Economics and International Businesss School, revised Feb 2018.
  • Handle: RePEc:brd:wpaper:116r
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    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP116R.pdf
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    References listed on IDEAS

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    Cited by:

    1. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.

    More about this item

    Keywords

    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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