Trading networks


  • Lada Adamic,

  • Celso Brunetti,

  • Jeffrey H. Harris,

  • Andrei Kirilenko

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    • Adamic is with the University of Michigan, Brunetti is with the Board of Governors at the Federal Reserve Bank, Harris is with American University, and Kirilenko is with Imperial College. We are grateful to Paul Tsyhura for invaluable assistance with the retrieval, organization, and processing of transaction-level data. We thank Kirsten Anderson, Ana Babus, Matt Elliott, Pat Fishe, Ben Golub, Matt Harding, Matt Jackson, Pete Kyle, Shawn Mankad, Antonio Mele, Pam Moulton, Anna Obizhaeva, Han Ozsoylev, Kester Tong, Kumar Venkataraman and presentation participants at the American University, Cambridge University, the Chicago Mercantile Exchange, the Commodities Futures Trading Commission, 2009 Complexity Conference at Northwestern University, 2009 Econometric Society Summer Meetings in Barcelona, the Federal Reserve Board of Governors, George Washington University, NASDAQ, 2009 NBER-NSF Time Series Conference, Oxford, the Securities and Exchange Commission, Southern Methodist University, Stanford University, Syracuse University, the University of Maryland, the University of Michigan, the University of Missouri—Columbia, the University of Tennessee, the University of South Florida, Villanova University, and 2009 Workshop on Information in Networks at New York University for very helpful comments and suggestions. Some earlier drafts of the paper were circulated under the title ”On the Informational Properties of Trading Networks.“ The views expressed in this paper are our own and do not constitute an official position of the Commodity Futures Trading Commission, its Commissioners or staff.

  • This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/ectj.12090


In this paper we analyze the time series of 12,000+ networks of traders in the e-mini S&P 500 stock index futures contract and empirically link network variables with financial variables more commonly used to describe market conditions. We show that network variables lead trading volume, intertrade duration, effective spreads, trade imbalances and other market liquidity measures. Network variables reflect information, information asymmetry and market liquidity and significantly presage future market conditions prior to volume or liquidity measures. We also find two-way Granger-causality between network variables and both returns and volatility, highlighting strong feedback between market conditions and trading behavior.

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