Short sales and trade classification algorithms
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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- 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.
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- 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.
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- 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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- 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.
- 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.
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