Financial Markets and Portfolio Management

, Volume 31, Issue 3, pp 357–391 | Cite as

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

  • Daniel Mantilla-GarcíaEmail author
  • Vijay Vaidyanathan


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.


Bayesian methods Dividend-price ratio Return predictability Statistical shrinkage 

JEL Classification

C58 G17 C11 



We thank an anonymous referee whose comments greatly improved the paper, Ravi Bansal, Ren’e Garcia, Abraham Lioui, Lionel Martellini, Gideon Ozik, and Raman Uppal for their valuable feedback and comments, Khanh-Linh Loth for her excellent research assistance, conference participants at the 3rd International Conference on Computational and Financial Econometrics (CFE’09), Limassol, Cyprus, and seminar participants at EDHEC-France for useful discussions and comments. A former version of this article is part of the authors’ Ph.D. Thesis at Edhec Business School.


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Copyright information

© Swiss Society for Financial Market Research 2017

Authors and Affiliations

  1. 1.Optimal Asset ManagementLos AltosUSA
  2. 2.Edhec-Risk InstituteNiceFrance

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