Short Sales and Trade Classification Algorithms

32 Pages Posted: 13 Dec 2006 Last revised: 30 Jun 2008

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: June 16, 2008

Abstract

This paper demonstrates that the Lee-Ready and other commonly used trade classification algorithms classify short sales as buyer-initiated significantly more than 50% of the time. 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, they ignore 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.

Keywords: Lee-Ready algorithm, market microstructure, short sales

JEL Classification: G10

Suggested Citation

Asquith, Paul and Oman, Rebecca and Safaya, Christopher, Short Sales and Trade Classification Algorithms (June 16, 2008). Available at SSRN: https://ssrn.com/abstract=951420 or http://dx.doi.org/10.2139/ssrn.951420

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

Register to save articles to
your library

Register

Paper statistics

Downloads
749
Abstract Views
3,251
rank
30,874
PlumX Metrics