Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?

Joachim Grammig () and Erik Theissen

University of St. Gallen Department of Economics working paper series 2003 from Department of Economics, University of St. Gallen

Abstract: Easley / Kiefer / O'Hara / Paperman (1996) (EKOP) have proposed an empirical methodology that allows to estimate the probability of informed trading and that has subsequently been used to address a wide range of issues in market microstructure. The data needed for estimation is the number of buyer- and seller-initiated trades. This information often has to be inferred by applying trade classification algorithms like the one proposed by Lee / Ready (1991). These algorithms are known to be inaccurate. In this paper we perform extensive simulations to show that inaccurate trade classification leads to biased estimation of the probability of informed trading when applying the EKOP methodology. The estimate is biased downward and the magnitude of the bias is related to the trading intensity of the stock in question. Scrutinizing prior empirical studies using the EKOP methodology, we conclude that the bias may severely affect the results of empirical microstructure studies.

Keywords: Informed trading; market microstructure; trade classification (search for similar items in EconPapers)
JEL-codes: C52 G10 G14 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2003-01
New Economics Papers: this item is included in nep-ecm and nep-fin
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Estimating the probability of informed trading--does trade misclassification matter? (2007)
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