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An improved estimation method and empirical properties of the probability of informed trading

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  • Yan, Yuxing
  • Zhang, Shaojun

Abstract

We report evidence that boundary solutions can cause a bias in the estimate of the probability of informed trading (PIN). We develop an algorithm to overcome this bias and use it to estimate PIN for nearly 80,000 stock-quarters between 1993 and 2004. We obtain two sets of PIN estimates by using the factorized likelihood functions in both Easley, Hvidkjaer, and O’Hara (EHO, 2010) and Lin and Ke (LK, 2011), respectively. We find that the estimate based on the EHO factorization is systematically smaller than the estimate based on the LK factorization, meaning that there is a downward bias associated with the EHO factorization. In addition, we find that boundary solutions appear with a very high frequency when the LK factorization is used. Thus it is necessary to use the LK factorization together with the algorithm in this paper. At last, we document several interesting empirical properties of PIN.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbfina:v:36:y:2012:i:2:p:454-467 DOI: 10.1016/j.jbankfin.2011.08.003
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    References listed on IDEAS

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    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. Sankaraguruswamy, Srinivasan & Shen, Jianfeng & Yamada, Takeshi, 2013. "The relationship between the frequency of news release and the information asymmetry: The role of uninformed trading," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4134-4143.
    4. Carl Chiarella & Xue-Zhong He & Lijian Wei, 2013. "Learning and Evolution of Trading Strategies in Limit Order Markets," Research Paper Series 335, Quantitative Finance Research Centre, University of Technology, Sydney.
    5. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    6. Ormos, Mihály & Timotity, Dusán, 2016. "Market microstructure during financial crisis: Dynamics of informed and heuristic-driven trading," Finance Research Letters, Elsevier, vol. 19(C), pages 60-66.
    7. Jackson, David, 2013. "Estimating PIN for firms with high levels of trading," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 116-120.
    8. repec:bla:acctfi:v:57:y:2017:i:2:p:565-595 is not listed on IDEAS
    9. 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.
    10. 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.
    11. Chen, Yifan & Zhao, Huainan, 2012. "Informed trading, information uncertainty, and price momentum," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2095-2109.
    12. Kim, Sangwan & Lim, Steve C., 2017. "Earnings comparability and informed trading," Finance Research Letters, Elsevier, vol. 20(C), pages 130-136.
    13. Malinova, Katya & Park, Andreas, 2014. "The impact of competition and information on intraday trading," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 55-71.
    14. Yan, Yuxing & Zhang, Shaojun, 2014. "Quality of PIN estimates and the PIN-return relationship," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 137-149.
    15. 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.
    16. 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.
    17. Huh, Sahn-Wook & Lin, Hao & Mello, Antonio S., 2015. "Options market makers׳ hedging and informed trading: Theory and evidence," Journal of Financial Markets, Elsevier, vol. 23(C), pages 26-58.

    More about this item

    Keywords

    Boundary solution; Probability of informed trading; PIN;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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