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Short sales and trade classification algorithms

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  • Asquith, Paul
  • Oman, Rebecca
  • Safaya, Christopher
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    Abstract

    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 that 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 that 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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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Financial Markets.

    Volume (Year): 13 (2010)
    Issue (Month): 1 (February)
    Pages: 157-173

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    Handle: RePEc:eee:finmar:v:13:y:2010:i:1:p:157-173

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

    Related research

    Keywords: Market microstructure Trade classification Short sales;

    References

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    1. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, Elsevier, vol. 6(3), pages 233-257, May.
    2. 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.
    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. Theissen, Erik, 2001. "A test of the accuracy of the Lee/Ready trade classification algorithm," Journal of International Financial Markets, Institutions and Money, Elsevier, Elsevier, vol. 11(2), pages 147-165, June.
    5. 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.
    6. Ekkehart Boehmer & Charles M. Jones & Xiaoyan Zhang, 2008. "Which Shorts Are Informed?," Journal of Finance, American Finance Association, vol. 63(2), pages 491-527, 04.
    7. Asquith, Paul & Pathak, Parag A. & Ritter, Jay R., 2005. "Short interest, institutional ownership, and stock returns," Journal of Financial Economics, Elsevier, Elsevier, vol. 78(2), pages 243-276, November.
    8. 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.
    9. Odders-White, Elizabeth R., 2000. "On the occurrence and consequences of inaccurate trade classification," Journal of Financial Markets, Elsevier, Elsevier, vol. 3(3), pages 259-286, August.
    10. 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, Elsevier, vol. 11(1), pages 84-111, February.
    11. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, Elsevier, vol. 3(2), pages 83-111, May.
    12. 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.
    13. 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.
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    Citations

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    Cited by:
    1. Torben G. Andersen & Oleg Bondarenko, 2013. "Assessing Measures of Order Flow Toxicity via Perfect Trade Classification," CREATES Research Papers 2013-43, School of Economics and Management, University of Aarhus.
    2. Duarte-Silva, Tiago, 2010. "The market for certification by external parties: Evidence from underwriting and banking relationships," Journal of Financial Economics, Elsevier, Elsevier, vol. 98(3), pages 568-582, December.
    3. Blau, Benjamin M. & Brough, Tyler J., 2012. "Short sales, stealth trading, and the suspension of the uptick rule," The Quarterly Review of Economics and Finance, Elsevier, Elsevier, vol. 52(1), pages 38-48.
    4. Dimitrios Karyampas & Paola Paiardini, 2011. "Probability of Informed Trading and Volatility for an ETF," Birkbeck Working Papers in Economics and Finance, Birkbeck, Department of Economics, Mathematics & Statistics 1101, Birkbeck, Department of Economics, Mathematics & Statistics.
    5. Maraachlian, Hilda & Rourke, Thomas, 2014. "Delta and vega exposure trading in stock and option markets," Journal of Financial Markets, Elsevier, Elsevier, vol. 18(C), pages 96-125.
    6. Asquith, Paul & Au, Andrea S. & Covert, Thomas & Pathak, Parag A., 2013. "The market for borrowing corporate bonds," Journal of Financial Economics, Elsevier, Elsevier, vol. 107(1), pages 155-182.
    7. Nimalendran, Mahendrarajah & Ray, Sugata, 2014. "Informational linkages between dark and lit trading venues," Journal of Financial Markets, Elsevier, Elsevier, vol. 17(C), pages 230-261.

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