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Learning specialists and market resilience

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  • Contreras, Alfredo

Abstract

In this paper, I address the stochastic behavior of asset prices set by an imperfectly informed specialist who uses learning technology to refine her knowledge of the order flow. The specialist’s endogenous choice of an information structure is analyzed which fully characterizes her responses to large orders in the market. Specifically, large orders can either be of structural origin, i.e., a disturbance in the asset’s payoff, or an exogenous one associated with noise trading. A specialist with a large learning capacity optimally chooses a pricing function where structural shocks display high persistence, whereas exogenous shocks disappear rapidly. This market structure provides a natural setup to address market resilience, in the sense of the recovery speed of prices.

Suggested Citation

  • Contreras, Alfredo, 2023. "Learning specialists and market resilience," Finance Research Letters, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:finlet:v:52:y:2023:i:c:s1544612322006924
    DOI: 10.1016/j.frl.2022.103516
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    References listed on IDEAS

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    1. Peng, Lin, 2005. "Learning with Information Capacity Constraints," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(2), pages 307-329, June.
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    More about this item

    Keywords

    Market microstructure; Market resilience; Insider trading; Dynamic rational inattention; Entropy learning;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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