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Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading


Author Info

  • Anthony Tay

    (School of Economics, Singapore Management University)

  • Christopher Ting

    (Lee Kong Chian School of Business, Singapore Management University)

  • Yiu Kuen Tse

    (School of Economics, Singapore Management University)

  • Mitch Warachka

    (Lee Kong Chian School of Business, Singapore Management University)


This paper implements the Asymmetric Autoregressive Conditional Duration (AACD) model of Bauwens and Giot (2003) to analyze irregularly spaced transaction data of trade direction, namely buy versus sell orders. We examine the influence of lagged transaction duration, lagged volume and lagged trade direction on transaction duration and direction. Our results are applied to estimate the probability of informed trading (PIN) based on the Easley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley- Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sell orders, the AACD model makes full use of transaction data and allows for interactions between buy and sell orders.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 13-2007.

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Length: 22 pages
Date of creation: Jan 2007
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:13-2007

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Related research

Keywords: Autoregressive Conditional Duration; Market Microstructure; Probability of informed Trading; Transaction Data; Weibull Distribution;

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