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Trading networks and liquidity provision

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  • Cohen-Cole, Ethan
  • Kirilenko, Andrei
  • Patacchini, Eleonora

Abstract

We study the profitability of traders in two fully electronic and highly liquid markets: the Dow and Standard & Poor׳s 500 e-mini futures markets. Using unique information that identify counterparties to a transaction, we show and seek to explain the fact that the network pattern of trades captures the relations between behavior in the market and returns. Our approach includes a simple representation of how much a shock is amplified by the network and how widely it is transmitted. This representation provides a possible shorthand for understanding the consequences of a fat-finger trade, a withdrawing of liquidity, or other market shock.

Suggested Citation

  • Cohen-Cole, Ethan & Kirilenko, Andrei & Patacchini, Eleonora, 2014. "Trading networks and liquidity provision," Journal of Financial Economics, Elsevier, vol. 113(2), pages 235-251.
  • Handle: RePEc:eee:jfinec:v:113:y:2014:i:2:p:235-251
    DOI: 10.1016/j.jfineco.2014.04.007
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Marc van Kralingen & Diego Garlaschelli & Karolina Scholtus & Iman van Lelyveld, 2020. "Crowded trades, market clustering, and price instability," Tinbergen Institute Discussion Papers 20-007/II, Tinbergen Institute.
    2. Han, Rui-Qi & Li, Ming-Xia & Chen, Wei & Zhou, Wei-Xing & Stanley, H. Eugene, 2019. "Structural properties of statistically validated empirical information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 747-756.
    3. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    4. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 115-168, Elsevier.
    5. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Ushchev, Philip & Zenou, Yves, 2018. "Price competition in product variety networks," Games and Economic Behavior, Elsevier, vol. 110(C), pages 226-247.
    7. Kromidha, Endrit & Li, Matthew C., 2019. "Determinants of leadership in online social trading: A signaling theory perspective," Journal of Business Research, Elsevier, vol. 97(C), pages 184-197.
    8. Cohen-Cole, Ethan & Patacchini, Eleonora & Zenou, Yves, 2015. "Static and dynamic networks in interbank markets," Network Science, Cambridge University Press, vol. 3(1), pages 98-123, March.
    9. Rainone, Edoardo, 2020. "The network nature of over-the-counter interest rates," Journal of Financial Markets, Elsevier, vol. 47(C).
    10. Ushchev, Philip & Zenou, Yves, 2018. "Price competition in product variety networks," Games and Economic Behavior, Elsevier, vol. 110(C), pages 226-247.
    11. Zhang, Weiping & Zhuang, Xintian & Lu, Yang, 2020. "Spatial spillover effects and risk contagion around G20 stock markets based on volatility network," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    12. Panzica, Roberto Calogero, 2018. "Idiosyncratic volatility puzzle: The role of assets' interconnections," SAFE Working Paper Series 228, Leibniz Institute for Financial Research SAFE.
    13. Shan Lu & Jichang Zhao & Huiwen Wang, 2019. "The emergence of critical stocks in market crash," Papers 1908.07244, arXiv.org.

    More about this item

    Keywords

    Financial interconnections; Contagion; Spatial autoregressive models; Network centrality; Trading limits;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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