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Journal of Financial Econometrics Advance Access originally published online on May 1, 2009
Journal of Financial Econometrics 2009 7(3):288-311; doi:10.1093/jjfinec/nbp005
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org.

Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading

Anthony Tay
     Singapore Management University

Christopher Ting
     Singapore Management University

Yiu Kuen Tse
     Singapore Management University

Mitch Warachka
     Singapore Management University

Address correspondence to Yiu Kuen Tse, School of Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, or e-mail: yktse{at}smu.edu.sg.

JEL Classification: C410, G120


  Abstract

This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley–Hvidkjaer–O'Hara framework by allowing the probabilities of good news and bad news to vary each day. Our PIN estimates can be computed daily as well as over intraday intervals.

KEYWORDS: autoregressive conditional duration, market microstructure, probability of informed trading, transaction data, Weibull distribution

The authors are indebted to the associate editor and two anonymous referees for their many helpful comments and suggestions. Any remaining errors are the responsibility of the authors. Tao Yang provided excellent research assistance. Yiu Kuen Tse gratefully acknowledges research support from MOE Tier 2 research grant T206B4301-RS.

Received February 12, 2008; revised March 4, 2009; accepted March 20, 2009


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