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Stock Return Predictability: Evaluation based on interval forecasts

Author

Listed:
  • Amélie Charles

    (Audencia Business School)

  • Olivier Darné

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université)

  • Jae Kim

    (La Trobe University [Melbourne])

Abstract

This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are of limited value since they carry no information about intrinsic predictive uncertainty. We compare the empirical performance of alternative interval forecasts for stock return generated from a naïve model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using U.S. data from 1926. It is found that neither univariate nor multivariate interval forecasts outperform naïve forecasts. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form.

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  • Amélie Charles & Olivier Darné & Jae Kim, 2022. "Stock Return Predictability: Evaluation based on interval forecasts," Post-Print hal-03656310, HAL.
  • Handle: RePEc:hal:journl:hal-03656310
    DOI: 10.1111/boer.12298
    Note: View the original document on HAL open archive server: https://hal.science/hal-03656310
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    Keywords

    Autoregressive Model; Bootstrapping; Financial Ratios; Forecasting; Interval Score; Market Efficiency;
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