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Short Sales, Long Sales, and the Lee-Ready Trade Classification Algorithm Revisited
Articles and Chapters
  • Bidisha Chakrabarty, St. Louis University
  • Pamela Moulton, Cornell University School of Hotel Administration
  • Andriy Shkilko, Wilfrid Laurier University
Publication Date
1-12-2012
Abstract
Asquith, Oman, and Safaya (2010) conclude that short sales are often misclassified by the Lee-Ready algorithm. The algorithm identifies most short sales as buyer-initiated, whereas the authors posit that short sales should be overwhelmingly seller-initiated. Using order data to identify true trade initiator, we document that short sales are, in fact, predominantly buyer-initiated and that the Lee-Ready algorithm correctly classifies most of them. Misclassification rates for short and long sales are near zero at the daily level. At the trade level, misclassification rates are 31% using contemporaneous quotes and trades and decline to 21% when quotes are lagged one second.
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Required Publisher Statement
© Elsevier. Final version published as: Chakrabarty, B., Moulton, P. C, & Shkilko, A. (2012). Short sales, long sales, and the Lee-Ready trade classification algorithm revisited. Journal of Financial Markets, 15(4), 467-491. Reprinted with permission. All rights reserved.

Citation Information

Chakrabarty, B., Moulton, P. C, & Shkilko, A. (2012). Short sales, long sales, and the Lee-Ready trade classification algorithm revisited [Electronic version]. Retrieved [insert date], from Cornell University, School of Hospitality Administration site: http://scholarship.sha.cornell.edu/articles/2/