Short Sales and Trade Classification Algorithms

32 Pages Posted: 16 Jul 2008 Last revised: 9 Sep 2010

See all articles by Paul Asquith

Paul Asquith

Massachusetts Institute of Technology (MIT) - Economics, Finance, Accounting (EFA); National Bureau of Economic Research (NBER)

Rebecca Oman

Massachusetts Institute of Technology (MIT)

Christopher Safaya

Massachusetts Institute of Technology (MIT)

Multiple version iconThere are 2 versions of this paper

Date Written: July 2008

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. 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.

Suggested Citation

Asquith, Paul and Oman, Rebecca and Safaya, Christopher, Short Sales and Trade Classification Algorithms (July 2008). NBER Working Paper No. w14158. Available at SSRN: https://ssrn.com/abstract=1161041

Paul Asquith (Contact Author)

Massachusetts Institute of Technology (MIT) - Economics, Finance, Accounting (EFA) ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Rebecca Oman

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Christopher Safaya

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

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