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The Accuracy of Trade Classification Rules: Evidence from Nasdaq

Posted: 19 Feb 2001  

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management

Katrina Ellis

Government of the Commonwealth of Australia - Australian Prudential Regulation Authority (APRA)

Roni Michaely

Johnson@Cornell Tech, Cornell University

Abstract

Researchers are increasingly using data from the Nasdaq market to examine pricing behavior, market design, and other microstructure phenomena. The validity of any study that classifies trades as buys or sells depends on the accuracy of the classification method. Using a Nasdaq proprietary data set that identifies trade direction, we examine the validity of several trade classification algorithms. We find that the quote rule, the tick rule, and the Lee and Ready (1991) rule correctly classify 76.4%, 77.66%, and 81.05% of the trades, respectively. However, all classification rules have only a very limited success in classifying trades executed inside the quotes, introducing a bias in the accuracy of classifying large trades, trades during high volume periods, and ECN trades. We also find that extant algorithms do a mediocre job when used for calculating effective spreads. For Nasdaq trades, we propose a new and simple classification algorithm that improves over extant algorithms.

Suggested Citation

O'Hara, Maureen and Ellis, Katrina and Michaely, Roni, The Accuracy of Trade Classification Rules: Evidence from Nasdaq. Journal of Financial and Quantitative Analysis, December 2000. Available at SSRN: https://ssrn.com/abstract=245795

Maureen O'Hara (Contact Author)

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

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

Katrina Ellis

Government of the Commonwealth of Australia - Australian Prudential Regulation Authority (APRA) ( email )

400 George Street
Sydney, NSW 2000
Australia

Roni Michaely

Johnson@Cornell Tech, Cornell University ( email )

111 8th Avenue #302
New York, NY 10011
United States

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