On the Economic Significance of Stock Return Predictability

Scott Cederburg, Travis L. Johnson, Michael S. O'Doherty

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We study the effects of time-varying volatility and investment horizon on the economic significance of stock market return predictability from the perspective of Bayesian investors. Using a vector autoregression framework with stochastic volatility (SV) in market returns and predictor variables, we assess a broad set of twenty-six predictors with both in-sample and out-of-sample designs. Volatility and horizon are critically important for assessing return predictors, as these factors affect how an investor learns about predictability and how she chooses to invest based on return forecasts. We find that statistically strong predictors can be economically unimportant if they tend to take extreme values in high volatility periods, have low persistence, or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains. We also demonstrate that incorporating SV leads to substantial utility gains in real-time forecasting.

Original languageEnglish (US)
Pages (from-to)619-657
Number of pages39
JournalReview of Finance
Issue number2
StatePublished - Mar 2023
Externally publishedYes


  • Economic significance
  • Stock market return predictability
  • Time-varying volatility

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics


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