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Learning from Experience in the Stock Market

Author

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  • Nakov, Anton
  • Nuño, Galo
Abstract
New evidence suggests that individuals "learn from experience," meaning they learn from events occurring during their own lifetimes as opposed to the entire history of events. Moreover, they weigh more heavily the more recent events compared to events occurring in the more distant past. This paper analyzes the implications of such learning for stock pricing in a model with finitely-lived agents. Individuals learn about the rate of change of the stock price and of dividends using a weighted decreasing-gain algorithm. Information is dispersed across age cohorts with older agents having larger information sets than younger ones. In the model, the stock price exhibits stochastic fluctuations around the rational expectations equilibrium due to successive waves of optimism and pessimism. We demonstrate how this heterogeneous-beliefs model can be approximated by an economy with a representative agent who updates his beliefs following a constant-gain learning scheme. The aggregate gain parameter of the approximation is a nonlinear function of the survival rate and of the individual gain parameters.

Suggested Citation

  • Nakov, Anton & Nuño, Galo, 2014. "Learning from Experience in the Stock Market," CEPR Discussion Papers 9845, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:9845
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    Citations

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    Cited by:

    1. Stefan Nagel & Zhengyang Xu, 2022. "Asset Pricing with Fading Memory," The Review of Financial Studies, Society for Financial Studies, vol. 35(5), pages 2190-2245.
    2. Mayer, Alexander, 2023. "Two-step estimation in linear regressions with adaptive learning," Statistics & Probability Letters, Elsevier, vol. 195(C).
    3. Nakov, Anton & Nuño, Galo, 2015. "Learning from experience in the stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 224-239.
    4. Gandré, Pauline, 2015. "Asset prices and information disclosure under recency-biased learning," CEPREMAP Working Papers (Docweb) 1515, CEPREMAP.
    5. Bansal, Avijit & Jacob, Joshy, 2022. "Impact of Price Path on Disposition Bias," Journal of Banking & Finance, Elsevier, vol. 143(C).
    6. Bonam, Dennis & Goy, Gavin, 2019. "Home biased expectations and macroeconomic imbalances in a monetary union," Journal of Economic Dynamics and Control, Elsevier, vol. 103(C), pages 25-42.
    7. Lei, Xiaowen, 2019. "Information and Inequality," Journal of Economic Theory, Elsevier, vol. 184(C).
    8. Duffy, John & Shin, Michael, 2024. "Heterogeneous experience and constant-gain learning," Journal of Economic Dynamics and Control, Elsevier, vol. 164(C).
    9. Adem Atmaz & Suleyman Basak, 2018. "Belief Dispersion in the Stock Market," Journal of Finance, American Finance Association, vol. 73(3), pages 1225-1279, June.
    10. Ampudia, Miguel & Ehrmann, Michael, 2017. "Macroeconomic experiences and risk taking of euro area households," European Economic Review, Elsevier, vol. 91(C), pages 146-156.
    11. Gandré, Pauline, 2020. "US stock prices and recency-biased learning in the run-up to the Global Financial Crisis and its aftermath," Journal of International Money and Finance, Elsevier, vol. 104(C).

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    More about this item

    Keywords

    Asset pricing; Constant-gain learning; Dispersed beliefs; Heterogeneous beliefs; Olg;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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