39 Pages Posted: 3 Mar 2006
Date Written: 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.
Notes: A previous version of this paper can be found at: http://ssrn.com/abstract=367041.
Keywords: Informed trading, market microstructure, trade classification
JEL Classification: G12, G14, C52
Suggested Citation: Suggested Citation
Boehmer, Ekkehart and Grammig, Joachim and Theissen, Erik, Estimating the Probability of Informed Trading - Does Trade Misclassification Matter? (February 25, 2006). Available at SSRN: https://ssrn.com/abstract=887221 or http://dx.doi.org/10.2139/ssrn.887221