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Learning Whether to Be Informed in an Agent-Based Evolutionary Market Model

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  • Paolo Pellizzari

    (Department of Economics, Ca’ Foscari University of Venice)

Abstract

Can traders in a financial market learn whether to be informed and which information to use in their demand for risky assets? We describe in this paper an agent-based model where heterogeneous traders seek short-term profits and differ in their choices to use or discard some signals. In the model, a vector of fresh news/signals is available at every period and some (but not all) the signals affect the stochastic payoff of the stock. Under an evolutionary dynamics favouring higher myopic returns we find that, in equilibrium, traders mostly end up in either discarding all signals or being (perfectly) informed using all the relevant signals (paying the related costs). Moreover, the rate of use of information strongly depends on the "complexity" of the market: an excessively large abundance of signals to be screened or a high volatility of the market, result in large shares of passive agents who overestimate the market's risk; conversely, low market complexity is associated with a more intense use of information and aggressiveness of informed traders. Evolutionary models and Agent-based models and Information in financial markets

Suggested Citation

  • Paolo Pellizzari, 2024. "Learning Whether to Be Informed in an Agent-Based Evolutionary Market Model," Working Papers 2024: 03, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2024:03
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    References listed on IDEAS

    as
    1. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    2. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Klaus Schredelseker, 2014. "Pascal's Wager and Information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 455-470, September.
    3. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    4. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    5. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
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    More about this item

    Keywords

    financial markets w information; agent-based models; evolutionary game theory; equity premium puzzle;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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