![Estimating the probability of informed trading-does trade misclassification matter? [An article from: Journal of Financial Markets]](https://www.ebooknetworking.net/books/B00/0PD/bigB000PDSRQQ.jpg)
Estimating the probability of informed trading-does trade misclassification matter? [An article from: Journal of Financial Markets]
Price not available for France
You can still browse on Amazon. Try another country above.
Book Details
Author(s)
E. Boehmer, J. Grammig, E. Theissen
Publisher
Elsevier
ISBN / ASIN
B000PDSRQQ
ISBN-13
978B000PDSRQ2
Marketplace
France 🇫🇷
Ratings & Reviews
No reviews yet — be the first!
No reviews yet.
Description
This digital document is a journal article from Journal of Financial Markets, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Easley et al. [1996. Journal of Finance 51, 1405-1436] 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.
Description:
Easley et al. [1996. Journal of Finance 51, 1405-1436] 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.
