Trade classification accuracy for the BIST

Osman Ulas Aktas and Lawrence Kryzanowski

Journal of International Financial Markets, Institutions and Money, 2014, vol. 33, issue C, 259-282

Abstract: The accuracy of five algorithms for classifying trades as buyer- or seller-initiated is assessed for BIST-30 index constituents over a period including the Lehman collapse. The highest classification accuracy rate (over 95%) is for the one-second lagged Lee & Ready (LR) algorithm. The LR's classification accuracy is highest (lowest) for trades representing mixed agency and principal (pure principal) relations between clients and executing brokers. Unlike for U.S. markets, almost all trades are classifiable with accuracy rates of 90-plus percent for both long and short trades. As for U.S. markets, higher misclassification rates occur for trades in the first versus last 30min of the trading day, as the time between consecutive trades decreases, and for decreasing trade sizes.

Keywords: Trade classification algorithms; Market microstructure; Developing stock market; Short sales; Agency relations (search for similar items in EconPapers)
JEL-codes: C52 G10 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:33:y:2014:i:c:p:259-282

DOI: 10.1016/j.intfin.2014.08.003

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