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On the Economic Significance of Stock Return Predictability

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  • Scott Cederburg
  • Travis L Johnson
  • Michael S O’Doherty

Abstract

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.

Suggested Citation

  • Scott Cederburg & Travis L Johnson & Michael S O’Doherty, 2023. "On the Economic Significance of Stock Return Predictability," Review of Finance, European Finance Association, vol. 27(2), pages 619-657.
  • Handle: RePEc:oup:revfin:v:27:y:2023:i:2:p:619-657.
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    File URL: http://hdl.handle.net/10.1093/rof/rfac035
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    More about this item

    Keywords

    Stock market return predictability; Time-varying volatility; Economic significance;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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