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Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?

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  • Joachim Grammig

    ()

  • Erik Theissen

    ()

Abstract

Easley / Kiefer / O'Hara / Paperman (1996) (EKOP) have proposed an empirical methodology that allows to estimate the probability of informed trading and that has subsequently been used to address a wide range of issues in market microstructure. The data needed for estimation is the number of buyer- and seller-initiated trades. This information often has to be inferred by applying trade classification algorithms like the one proposed by Lee / Ready (1991). These algorithms are known to be inaccurate. In this paper we perform extensive simulations to show that inaccurate trade classification leads to biased estimation of the probability of informed trading when applying the EKOP methodology. The estimate is biased downward and the magnitude of the bias is related to the trading intensity of the stock in question. Scrutinizing prior empirical studies using the EKOP methodology, we conclude that the bias may severely affect the results of empirical microstructure studies.

Suggested Citation

  • 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.
  • Handle: RePEc:usg:dp2003:2003-01
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    References listed on IDEAS

    as
    1. 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.
    2. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    3. Savickas, Robert & Wilson, Arthur J., 2003. "On Inferring the Direction of Option Trades," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(04), pages 881-902, December.
    4. Easley, David & O'Hara, Maureen & Paperman, Joseph, 1998. "Financial analysts and information-based trade," Journal of Financial Markets, Elsevier, vol. 1(2), pages 175-201, August.
    5. Theissen, Erik, 2001. "A test of the accuracy of the Lee/Ready trade classification algorithm," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 11(2), pages 147-165, June.
    6. Finucane, Thomas J., 2000. "A Direct Test of Methods for Inferring Trade Direction from Intra-Day Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(04), pages 553-576, December.
    7. 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.
    8. David Easley & Robert F. Engle & Maureen O'Hara & Liuren Wu, 2008. "Time-Varying Arrival Rates of Informed and Uninformed Trades," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(2), pages 171-207, Spring.
    9. Grammig, Joachim & Schiereck, Dirk & Theissen, Erik, 2001. "Knowing me, knowing you: : Trader anonymity and informed trading in parallel markets," Journal of Financial Markets, Elsevier, vol. 4(4), pages 385-412, October.
    10. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    11. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1996. " Cream-Skimming or Profit-Sharing? The Curious Role of Purchased Order Flow," Journal of Finance, American Finance Association, vol. 51(3), pages 811-833, July.
    12. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
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    15. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
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    More about this item

    Keywords

    Informed trading; market microstructure; trade classification;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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