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Perpetual learning and stock return predictability

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  • Zhu, Xiaoneng

Abstract

The stock market is evolving, and investors are learning. This paper investigates the role of perpetual learning in excess return forecasts. We find that perpetual learning usually delivers statistically and economically significant out-of-sample gains relative to the historical average.

Suggested Citation

  • Zhu, Xiaoneng, 2013. "Perpetual learning and stock return predictability," Economics Letters, Elsevier, vol. 121(1), pages 19-22.
    • Handle: RePEc:eee:ecolet:v:121:y:2013:i:1:p:19-22
    DOI: 10.1016/j.econlet.2013.06.035
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    References listed on IDEAS

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    Cited by:

    1. Boriss Siliverstovs, 2017. "International stock return predictability: on the role of the United States in bad and good times," Applied Economics Letters, Taylor & Francis Journals, vol. 24(11), pages 771-773, June.
    2. Zhu, Yanjian & Zhu, Xiaoneng, 2014. "European business cycles and stock return predictability," Finance Research Letters, Elsevier, vol. 11(4), pages 446-453.
    3. Zhang, Yaojie & Zeng, Qing & Ma, Feng & Shi, Benshan, 2019. "Forecasting stock returns: Do less powerful predictors help?," Economic Modelling, Elsevier, vol. 78(C), pages 32-39.

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    More about this item

    Keywords

    Excess return; Learning; Forecasts; Stock returns;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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