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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
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 36 (2012)
    Issue (Month): 2 ()
    Pages: 454-467

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    Handle: RePEc:eee:jbfina:v:36:y:2012:i:2:p:454-467

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Boundary solution; Probability of informed trading; PIN;

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    References

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    1. Bamber, Linda Smith & Barron, Orie E. & Stober, Thomas L., 1999. "Differential Interpretations and Trading Volume," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(03), pages 369-386, September.
    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. Vega, Clara, 2006. "Stock price reaction to public and private information," Journal of Financial Economics, Elsevier, vol. 82(1), pages 103-133, October.
    4. Duarte, Jefferson & Young, Lance, 2009. "Why is PIN priced?," Journal of Financial Economics, Elsevier, vol. 91(2), pages 119-138, February.
    5. Brown, Stephen & Hillegeist, Stephen A. & Lo, Kin, 2004. "Conference calls and information asymmetry," Journal of Accounting and Economics, Elsevier, vol. 37(3), pages 343-366, September.
    6. Duarte, Jefferson & Han, Xi & Harford, Jarrad & Young, Lance, 2008. "Information asymmetry, information dissemination and the effect of regulation FD on the cost of capital," Journal of Financial Economics, Elsevier, vol. 87(1), pages 24-44, January.
    7. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-46, June.
    8. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    9. Joachim Grammig & Erik Theissen, 2003. "Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?," University of St. Gallen Department of Economics working paper series 2003 2003-01, Department of Economics, University of St. Gallen.
    10. Xin Zhao & Kee H. Chung, 2006. "Decimal Pricing and Information-Based Trading: Tick Size and Informational Efficiency of Asset Price," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(5-6), pages 753-766.
    11. Easley, David & O'Hara, Maureen & Saar, Gideon, 2001. "How Stock Splits Affect Trading: A Microstructure Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(01), pages 25-51, March.
    12. Brockman, Paul & Yan, Xuemin (Sterling), 2009. "Block ownership and firm-specific information," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 308-316, February.
    13. Kalok Chan & Albert J. Menkveld & Zhishu Yang, 2008. "Information Asymmetry and Asset Prices: Evidence from the China Foreign Share Discount," Journal of Finance, American Finance Association, vol. 63(1), pages 159-196, 02.
    14. Easley, David, et al, 1996. " Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-36, September.
    15. Easley, David & Hvidkjaer, Soeren & O’Hara, Maureen, 2010. "Factoring Information into Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(02), pages 293-309, April.
    16. 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.
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    Cited by:
    1. 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.
    2. Jackson, David, 2013. "Estimating PIN for firms with high levels of trading," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 116-120.
    3. 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.
    4. 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.
    5. Chen, Yifan & Zhao, Huainan, 2012. "Informed trading, information uncertainty, and price momentum," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2095-2109.

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