The Market Inside the Market: Odd-Lot Quotes

60 Pages Posted: 8 Feb 2022 Last revised: 11 Feb 2022

See all articles by Robert P. Bartlett

Robert P. Bartlett

Stanford Law School

Justin McCrary

Columbia University - Law School; National Bureau of Economic Research (NBER)

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: February 1, 2022

Abstract

We show how current market practices relating to odd lot quotes result in a large “inside” market where for many stocks better prices routinely exist relative to the National Best Bid or Offer (NBBO). We provide strong evidence that being able to see these odd lot quotes provides valuable information to traders with access to proprietary data feeds. We develop a XGBoost machine learning prediction algorithm that uses odd lot data to predict future prices, and demonstrate a simple and profitable trading strategy using odd lot data. We show that the SEC’s new approach of changing the definition of a round lot reduces, but does not ameliorate, the high incidence of superior odd lot quotes within the NBBO.

Keywords: Odd-lot quotes, inside market, NBBO, round lots, Reg NMS II, machine learning

JEL Classification: G14, G21, G23, G24

Suggested Citation

Bartlett, Robert P. and McCrary, Justin and O'Hara, Maureen, The Market Inside the Market: Odd-Lot Quotes (February 1, 2022). Available at SSRN: https://ssrn.com/abstract=4027099 or http://dx.doi.org/10.2139/ssrn.4027099

Robert P. Bartlett (Contact Author)

Stanford Law School

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States

Justin McCrary

Columbia University - Law School ( email )

435 West 116th Street
New York, NY 10025
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States
607-255-3645 (Phone)
607-255-5993 (Fax)

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