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Discerning Information from Trade Data

56 Pages Posted: 23 Jan 2012 Last revised: 16 May 2016

David Easley

Cornell University - Department of Economics

Marcos Lopez de Prado

Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: March 1, 2015

Abstract

How best to discern trading intentions from market data? We examine the accuracy of three methods for classifying trade data: bulk volume classification (BVC), Tick Rule and Aggregated Tick Rule. We develop a Bayesian model of inferring information from trade executions, and show the conditions in which tick rules or bulk volume classification will predominate.

Empirically, we find that Tick rule approaches and BVC are relatively good classifiers of the aggressor side of trading, but bulk volume classifications are better linked to proxies of information-based trading. Thus, BVC would appear to be a useful tool for discerning trading intentions from market data.

Keywords: Trade Classification, Bulk Volume Classification, flow toxicity, volume imbalance, market microstructure

JEL Classification: C02, D52, D53, G14

Suggested Citation

Easley, David and Lopez de Prado, Marcos and O'Hara, Maureen, Discerning Information from Trade Data (March 1, 2015). Journal of Financial Economics, 120(2), pp. 269-286. May 2016; Johnson School Research Paper Series No. 8-2012. Available at SSRN: https://ssrn.com/abstract=1989555 or http://dx.doi.org/10.2139/ssrn.1989555

David Easley (Contact Author)

Cornell University - Department of Economics ( email )

414 Uris Hall
Ithaca, NY 14853-7601
United States
607-255-6283 (Phone)
607-255-2818 (Fax)

Marcos Lopez de Prado

Guggenheim Partners, LLC ( email )

330 Madison Avenue
New York, NY 10017
United States

HOME PAGE: http://www.QuantResearch.org

Lawrence Berkeley National Laboratory ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

HOME PAGE: http://www.lbl.gov

Harvard University - RCC ( email )

26 Trowbridge Street
Cambridge, MA 02138
United States

HOME PAGE: http://www.rcc.harvard.edu

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States
607-255-3645 (Phone)
607-255-5993 (Fax)

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