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Predicting stock returns in the presence of uncertain structural changes and sample noise

Author

Listed:
  • Daniel Mantilla-García

    (Optimal Asset Management
    Edhec-Risk Institute)

  • Vijay Vaidyanathan

    (Optimal Asset Management
    Edhec-Risk Institute)

Abstract

The predictive power of the dividend-price ratio has been the subject of intense scrutiny. Most studies on return predictability assume that predictor variables follow stationary processes with constant long-run means. Following recent evidence on the role of structural breaks in the dividend-price ratio mean, we propose an estimation method that explicitly incorporates uncertainty about the location and magnitude of structural breaks in the predictor that extracts the regime mean component of the dividend-price ratio. Adjusting for structural changes in the ratio’s mean and estimation error significantly improves predictive power of the dividend-price ratio as well as other standard predictors in sample and out of sample.

Suggested Citation

  • Daniel Mantilla-García & Vijay Vaidyanathan, 2017. "Predicting stock returns in the presence of uncertain structural changes and sample noise," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 357-391, August.
    • Handle: RePEc:kap:fmktpm:v:31:y:2017:i:3:d:10.1007_s11408-017-0290-3
    DOI: 10.1007/s11408-017-0290-3
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    More about this item

    Keywords

    Bayesian methods; Dividend-price ratio; Return predictability; Statistical shrinkage;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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