Stock return predictability and model uncertainty

Doron Avramov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

301 Scopus citations

Abstract

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.

Original languageAmerican English
Pages (from-to)423-458
Number of pages36
JournalJournal of Financial Economics
Volume64
Issue number3
DOIs
StatePublished - 2002
Externally publishedYes

Keywords

  • Bayesian model averaging
  • Model uncertainty
  • Portfolio selection
  • Stock return predictability
  • Variance decomposition

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