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

Daniel Mantilla-García () and Vijay Vaidyanathan ()
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Daniel Mantilla-García: Optimal Asset Management
Vijay Vaidyanathan: Optimal Asset Management
Authors registered in the RePEc Author Service: Daniel Mantilla Garcia ()

Financial Markets and Portfolio Management, 2017, vol. 31, issue 3, No 4, 357-391

Abstract: 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.

Keywords: Bayesian methods; Dividend-price ratio; Return predictability; Statistical shrinkage (search for similar items in EconPapers)
JEL-codes: C11 C58 G17 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11408-017-0290-3

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