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Journal of Banking & Finance Vol. 31 No. 12 2007

Trade classification algorithms for electronic communications network trades

Bidisha Chakrabarty1,2,3; Bingguang Li4; Vanthuan Nguyen5; Robert A. Van Ness

1 UCLouvain Saint-Louis Brussels · 2 Linde (United States) · 3 Saint Louis University · 4 Shenandoah University · 5 Morgan State University

Abstract

Ellis et al. [Ellis, K., Michaely, R., O’Hara, M., 2000. The accuracy of trade classification rules: Evidence from Nasdaq. Journal of Financial and Quantitative Analysis 35 (4), 529–551] find that trade classification rules have limited success in classifying trades which execute inside the quotes. We reconfirm this result and propose an alternative algorithm to improve the classification accuracy for trades inside the quotes. This alternative algorithm improves the overall success rate for classifying trades, especially for trades that occur inside the quotes. Additionally, we show that the Lee and Ready [Lee, C., Ready, M., 1991. Inferring trade direction from intraday data. Journal of Finance 46, 733–747] and Ellis et al. (2000) trade classification algorithms provide biased estimates of the actual effective spreads and price impacts, while our algorithm provides statistically unbiased estimates of actual effective spreads and price impacts.

DOI
10.1016/j.jbankfin.2007.03.003
Volume
31
Issue
12
Pages
3806-3821
Language
en
Sources
crossref openalex bibtex:phds-export.bib

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