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Short Sales and Trade Classification Algorithms

  • Paul Asquith
  • Rebecca Oman
  • Christopher Safaya
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    This paper demonstrates that short sales are often misclassified as buyer-initiated by the Lee-Ready and other commonly used trade classification algorithms. This result is due in part to regulations which require short sales be executed on an uptick or zero-uptick. In addition, while the literature considers "immediacy premiums" in determining trade direction, it ignores the often larger borrowing premiums which short sellers must pay. Since short sales constitute approximately 30% of all trade volume on U.S. exchanges, these results are important to the empirical market microstructure literature as well as to measures that rely upon trade classification, such as the probability of informed trading (PIN) metric.

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    Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 14158.

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    Date of creation: Jul 2008
    Date of revision:
    Publication status: published as Asquith, Paul & Oman, Rebecca & Safaya, Christopher, 2010. "Short sales and trade classification algorithms," Journal of Financial Markets, Elsevier, vol. 13(1), pages 157-173, February.
    Handle: RePEc:nbr:nberwo:14158
    Note: AP
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    1. Alexander, Gordon J. & Peterson, Mark A., 2008. "The effect of price tests on trader behavior and market quality: An analysis of Reg SHO," Journal of Financial Markets, Elsevier, vol. 11(1), pages 84-111, February.
    2. Aitken, Michael & Frino, Alex, 1996. "The accuracy of the tick test: Evidence from the Australian stock exchange," Journal of Banking & Finance, Elsevier, vol. 20(10), pages 1715-1729, December.
    3. 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.
    4. Ellis, Katrina & Michaely, Roni & O'Hara, Maureen, 2000. "The Accuracy of Trade Classification Rules: Evidence from Nasdaq," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(04), pages 529-551, December.
    5. Asquith, Paul & Pathak, Parag A. & Ritter, Jay R., 2005. "Short interest, institutional ownership, and stock returns," Journal of Financial Economics, Elsevier, vol. 78(2), pages 243-276, November.
    6. Odders-White, Elizabeth R., 2000. "On the occurrence and consequences of inaccurate trade classification," Journal of Financial Markets, Elsevier, vol. 3(3), pages 259-286, August.
    7. Joachim Grammig & Erik Theissen, 2002. "Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?," Bonn Econ Discussion Papers bgse37_2002, University of Bonn, Germany.
    8. Bessembinder, Hendrik, 2003. "Trade Execution Costs and Market Quality after Decimalization," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(04), pages 747-777, December.
    9. 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.
    10. Olivier Vergote, 2005. "How to Match Trades and Quotes for Nyse Stocks?," Center for Economic Studies - Discussion papers ces0510, Katholieke Universiteit Leuven, Centrum voor Economische Studiƫn.
    11. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    12. 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.
    13. 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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