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Estimating PIN for firms with high levels of trading

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  • Jackson, David

Abstract

For models of the probability of informed trading (PIN), estimation can fail for firms with high levels of trading due to computer over/under-flow. Since active firms tend to have large market capitalizations, studies that use PIN have excluded as much as 40% of total market capitalization from their sample. Similarly, since trading tends to be more intense around important events, studies that use PIN may lose observations exactly during periods that are the focus of study. A simple procedure, using scaled trade counts, allows PIN to be estimated for actively-traded firms, avoiding the possible biases or false generalizations that may occur when data from large firms or important events is ignored.

Suggested Citation

  • Jackson, David, 2013. "Estimating PIN for firms with high levels of trading," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 116-120.
    • Handle: RePEc:eee:empfin:v:24:y:2013:i:c:p:116-120
    DOI: 10.1016/j.jempfin.2013.10.001
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    References listed on IDEAS

    as
    1. Duarte, Jefferson & Young, Lance, 2009. "Why is PIN priced?," Journal of Financial Economics, Elsevier, vol. 91(2), pages 119-138, February.
    2. David Easley & Soeren Hvidkjaer & Maureen O'Hara, 2002. "Is Information Risk a Determinant of Asset Returns?," Journal of Finance, American Finance Association, vol. 57(5), pages 2185-2221, October.
    3. Yan, Yuxing & Zhang, Shaojun, 2012. "An improved estimation method and empirical properties of the probability of informed trading," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 454-467.
    4. Boehmer, Ekkehart & Grammig, Joachim & Theissen, Erik, 2007. "Estimating the probability of informed trading--does trade misclassification matter?," Journal of Financial Markets, Elsevier, vol. 10(1), pages 26-47, February.
    5. William Lin, Hsiou-Wei & Ke, Wen-Chyan, 2011. "A computing bias in estimating the probability of informed trading," Journal of Financial Markets, Elsevier, vol. 14(4), pages 625-640, November.
    6. Kee H. Chung & Mingsheng Li, 2003. "Adverse‐Selection Costs and the Probability of Information‐Based Trading," The Financial Review, Eastern Finance Association, vol. 38(2), pages 257-272, May.
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    9. Heidle, Hans G. & Huang, Roger D., 2002. "Information-Based Trading in Dealer and Auction Markets: An Analysis of Exchange Listings," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(3), pages 391-424, September.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Kitamura, Yoshihiro, 2016. "The probability of informed trading measured with price impact, price reversal, and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 77-90.
    2. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    3. Griffin, Jim & Oberoi, Jaideep & Oduro, Samuel D., 2021. "Estimating the probability of informed trading: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 125(C).
    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. Ke, Wen-Chyan & Chen, Hueiling & Lin, Hsiou-Wei William, 2019. "A note of techniques that mitigate floating-point errors in PIN estimation," Finance Research Letters, Elsevier, vol. 31(C).
    6. Hua, Renhai & Liu, Qingfu & Tse, Yiuman, 2016. "Extended trading in Chinese index markets: Informed or uninformed?," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 112-122.

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    More about this item

    Keywords

    Asymmetric information; PIN; Event studies; Maximum likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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