2010
DOI: 10.1016/j.finmar.2009.09.005
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Short sales and trade classification algorithms

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 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.… Show more

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Cited by 43 publications
(26 citation statements)
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“…Like these authors, we find that long sellers appear to consume liquidity more often than they provide it while short sellers appear to provide liquidity more often than they consume it. Like these studies, we find that short sales are predominantly buyer-initiated which is consistent with the expectation advanced by Chakrabarty et al (2012) but not Asquith et al (2010) whose prior is that short sales should be predominantly seller-initiated. This interpretation depends on the somewhat strong implicit assumption that any differential immediacy costs associated with the short-side of a trade are not material.…”
Section: Algorithms Definitionsupporting
confidence: 88%
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“…Like these authors, we find that long sellers appear to consume liquidity more often than they provide it while short sellers appear to provide liquidity more often than they consume it. Like these studies, we find that short sales are predominantly buyer-initiated which is consistent with the expectation advanced by Chakrabarty et al (2012) but not Asquith et al (2010) whose prior is that short sales should be predominantly seller-initiated. This interpretation depends on the somewhat strong implicit assumption that any differential immediacy costs associated with the short-side of a trade are not material.…”
Section: Algorithms Definitionsupporting
confidence: 88%
“…We find significant differences in the accuracy rates of the trade classification algorithms that generally are higher for long versus short trades. Unlike Asquith et al (2010) and Chakrabarty et al (2012) who examine US data for June and December 2005 provided under the SEC's Reg SHO initiative, 5 we find accuracy rates of at least 90% using one-second lagged quotes for both long and short trades for the quote, at-the-quote and LR algorithms. Like these authors, we find that long sellers appear to consume liquidity more often than they provide it while short sellers appear to provide liquidity more often than they consume it.…”
Section: Algorithms Definitioncontrasting
confidence: 59%
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