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Are Networks Priced? Network Topology and Order Trading Strategies in High Liquidity Markets

Author

Listed:
  • Ethan Cohen-Cole

    (University of Maryland - College Park)

  • Andrei Kirilenko

    (Commodity Futures Trading Commission)

  • Eleonora Patacchini

    (University of Rome "La Sapienza")

Abstract

Network spillovers explain as much as 90% of the individual variation in returns in a fully electronic market. We study two fully electronic, highly liquid markets, the Dow an S&P 500 e-mini futures markets. Within these markets, we use a unique dataset of realized trades that includes the precise topology of transactions; this topology allows us to identify precisely both the relevance of network structure as well as endogenous network spillovers. Within these markets, we will show that network positioning on the part of trader leads to remarkable spillovers in return. Empirically, we estimate that the implied average multiplier, the ratio of a individual level shock to the total network one, is as large as 20. A gain of $1 for a trader leads to an average of $20 in gains for all traders and much more for connected ones. In a zero-sum market, such as the one in this study, this suggests a very large re-allocation of returns according to network structure.

Suggested Citation

  • Ethan Cohen-Cole & Andrei Kirilenko & Eleonora Patacchini, 2010. "Are Networks Priced? Network Topology and Order Trading Strategies in High Liquidity Markets," EIEF Working Papers Series 1011, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2010.
    • Handle: RePEc:eie:wpaper:1011
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    References listed on IDEAS

    as
    1. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    2. Antoni Calvó-Armengol & Eleonora Patacchini & Yves Zenou, 2009. "Peer Effects and Social Networks in Education," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1239-1267.
    3. Lada Adamic & Celso Brunetti & Jeffrey H. Harris & Andrei Kirilenko, 2017. "Trading networks," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 126-149, October.
    4. Allen, Franklin & Babus, Ana & Carletti, Elena, 2010. "Financial Connections and Systemic Risk," Working Papers 10-20, University of Pennsylvania, Wharton School, Weiss Center.
    5. de Paula, Áureo, 2009. "Inference in a synchronization game with social interactions," Journal of Econometrics, Elsevier, vol. 148(1), pages 56-71, January.
    6. Ethan Cohen‐Cole & Giulio Zanella, 2008. "Unpacking Social Interactions," Economic Inquiry, Western Economic Association International, vol. 46(1), pages 19-24, January.
    7. Cohen-Cole, Ethan, 2006. "Multiple groups identification in the linear-in-means model," Economics Letters, Elsevier, vol. 92(2), pages 157-162, August.
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    Cited by:

    1. Henry, Jérôme & Zimmermann, Maik & Leber, Miha & Kolb, Markus & Grodzicki, Maciej & Amzallag, Adrien & Vouldis, Angelos & Hałaj, Grzegorz & Pancaro, Cosimo & Gross, Marco & Baudino, Patrizia & Sydow, , 2013. "A macro stress testing framework for assessing systemic risks in the banking sector," Occasional Paper Series 152, European Central Bank.
    2. Zenou, Yves & Patacchini, Eleonora & Cohen-Cole, Ethan, 2011. "Systemic Risk and Network Formation in the Interbank Market," CEPR Discussion Papers 8332, C.E.P.R. Discussion Papers.
    3. Silvia Gabrieli, 2011. "The microstructure of the money market before and after the financial crisis: a network perspective," CEIS Research Paper 181, Tor Vergata University, CEIS, revised 19 Jan 2011.
    4. Gabrieli, S., 2012. "Too-connected versus too-big-to-fail: banks’ network centrality and overnight interest rates," Working papers 398, Banque de France.

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