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Learning in Crowded Markets

Author

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  • Adam Zawadowski

    (Central European University)

  • Peter Kondor

    (London School of Economics)

Abstract

We develop a model of capital reallocation to analyze whether the presence of more arbitrageurs improves capital allocation and welfare. While trades can become crowded due to imperfect information and externalities, arbitrageurs can devote resources to flexibly learn about the number of earlier entrants. Above a threshold, increasing the number of arbitrageurs does not affect capital allocation: whether there is eventually too little or too much capital allocated to the trade is solely determined by the parameters of the market. The flexibility in the learning technology is key to this insight. However, the presence of more arbitrageurs decreases welfare, as they use more aggregate resources to learn about each others' position. When both sophisticated and unsophisticated arbitrageurs are present, increasing the share of sophisticated arbitrageurs might be welfare reducing.

Suggested Citation

  • Adam Zawadowski & Peter Kondor, 2016. "Learning in Crowded Markets," 2016 Meeting Papers 338, Society for Economic Dynamics.
    • Handle: RePEc:red:sed016:338
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    Cited by:

    1. Kondor, Péter & Zawadowski, Adam, 2019. "Learning in crowded markets," Journal of Economic Theory, Elsevier, vol. 184(C).
    2. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    3. Yang Hao, 2023. "Financial Market with Learning from Price under Knightian Uncertainty," Working Papers hal-03686748, HAL.
    4. Stepan Gorban & Anna A. Obizhaeva & Yajun Wang, 2020. "Trading in Crowded Markets," Working Papers w0275, New Economic School (NES).
    5. Fardeau, Vincent, 2023. "Sequential entry in illiquid markets," Journal of Financial Markets, Elsevier, vol. 64(C).

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    JEL classification:

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

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