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Adaptive Traders and the Design of Financial Markets

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  • SEBASTIEN POUGET

Abstract

This paper studies a financial market populated by adaptive traders. Learning is modeled following Camerer and Ho (1999). A call market and a Walrasian tatonnement are compared in an environment in which both institutions have the same Nash and competitive equilibrium outcomes. When traders learn via a belief‐based model, equilibrium is discovered in both types of markets. In contrast, when traders learn via a reinforcement‐based model, convergence to equilibrium is achieved in the Walrasian tatonnement but not in the call market. This paper suggests that market mechanisms can be designed to foster traders' learning of equilibrium strategies.

Suggested Citation

  • Sebastien Pouget, 2007. "Adaptive Traders and the Design of Financial Markets," Journal of Finance, American Finance Association, vol. 62(6), pages 2835-2863, December.
    • Handle: RePEc:bla:jfinan:v:62:y:2007:i:6:p:2835-2863
    DOI: 10.1111/j.1540-6261.2007.01294.x
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    Cited by:

    1. Albert Banal-Estañol & Augusto Rupérez Micola, 2010. "Are Agent-based Simulations Robust? The Wholesale Electricity Trading Case," Working Papers 443, Barcelona School of Economics.
    2. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance," Journal of Finance, American Finance Association, vol. 73(3), pages 1113-1137, June.
    3. Eaves, James & Williams, Jeffrey & Power, Gabriel J., 2016. "Do traders strategically time their pledges during real-world Walrasian auctions?," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 109-118.
    4. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    5. Pouget, Sebastien, 2007. "Financial market design and bounded rationality: An experiment," Journal of Financial Markets, Elsevier, vol. 10(3), pages 287-317, August.
    6. He, Zhongzhi (Lawrence), 2023. "A Gradient-based reinforcement learning model of market equilibration," Journal of Economic Dynamics and Control, Elsevier, vol. 152(C).
    7. Marco Mantovani & Antonio Filippin, 2024. "When do prediction markets return average beliefs? Experimental evidence," Working Papers 532, University of Milano-Bicocca, Department of Economics.
    8. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    9. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    10. Banal-Estañol, Albert & Rupérez Micola, Augusto, 2011. "Behavioural simulations in spot electricity markets," European Journal of Operational Research, Elsevier, vol. 214(1), pages 147-159, October.
    11. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    12. Barroso, Ricardo Vieira & Lima, Joaquim Ignacio Alves Vasconcellos & Lucchetti, Alexandre Henrique & Cajueiro, Daniel Oliveira, 2016. "Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning," MPRA Paper 73308, University Library of Munich, Germany.

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