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

Daniel Mantilla-García, Vijay Vaidyanathan

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)357-391
Number of pages35
JournalFinancial Markets and Portfolio Management
Volume31
Issue number3
DOIs
StatePublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Bayesian methods
  • Dividend-price ratio
  • Return predictability
  • Statistical shrinkage

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