Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?
Singapore Management University - Lee Kong Chian School of Business
Eberhard Karls Universitaet Tübingen; Centre for Financial Research (CfR); Center for Financial Studies (CfS)
University of Mannheim - Finance Area
February 25, 2006
Easley et al. (1996) have proposed an empirical methodology to estimate the probability of informed trading (PIN). This approach has been employed in a wide range of applications in market microstructure, corporate finance, and asset pricing. To estimate the model, a researcher only needs the number of buyer- and seller-initiated trades. This information, however, is generally unobservable and has to be inferred from trade-classification algorithms, which are known to be inaccurate. In this paper, we show analytically that inaccurate trade classification leads to downward biased PIN estimates and that the magnitude of the bias is related to a security's trading intensity. Simulation results and empirical evidence based on order and transaction data from the New York Stock Exchange are consistent with this argument. We propose a data-based adjustment procedure that substantially reduces the misclassification bias.
Note: A previous version of this paper can be found at: http://ssrn.com/abstract=367041.
Number of Pages in PDF File: 39
Keywords: Informed trading, market microstructure, trade classification
JEL Classification: G12, G14, C52
Date posted: March 3, 2006
© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollobot1 in 0.266 seconds