IDEAS home Printed from
   My bibliography  Save this article

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


  • Anthony Tay
  • Christopher Ting
  • Yiu Kuen Tse
  • Mitch Warachka


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. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:, Oxford University Press.

Suggested Citation

  • Anthony Tay & Christopher Ting & Yiu Kuen Tse & Mitch Warachka, 2009. "Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(3), pages 288-311, Summer.
  • Handle: RePEc:oup:jfinec:v:7:y:2009:i:3:p:288-311

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Petchey, James & Wee, Marvin & Yang, Joey, 2016. "Pinning down an effective measure for probability of informed trading," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 456-475.
    2. Moonsoo Kang & Kiseok Nam, 2015. "Informed trade and idiosyncratic return variation," Review of Quantitative Finance and Accounting, Springer, vol. 44(3), pages 551-572, April.
    3. Agudelo, Diego A. & Giraldo, Santiago & Villarraga, Edwin, 2015. "Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 149-161.
    4. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    5. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    6. Hahn, TeWhan & Ligon, James A. & Rhodes, Heather, 2013. "Liquidity and initial public offering underpricing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4973-4988.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:7:y:2009:i:3:p:288-311. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.