Information Content in Small and Large Trades

Economic Notes, Forthcoming

45 Pages Posted: 24 Apr 2011

See all articles by Malay K. Dey

Malay K. Dey

Cornell University; Southern Illinois University at Edwardsville

Hal S. Stern

University of California, Irvine - Department of Statistics

Zhang Hongmei

affiliation not provided to SSRN

Date Written: April 22, 2011

Abstract

We estimate the probabilities of informed trading (PIN) for small and large trades and then investigate their determinants. We model a competitive dealership market for equities with two order sizes using a Poisson process mixture model and use TORQ data to estimate the parameters for the model via the method of maximum likelihood. The probabilities of informed trading (PIN) for small and large trades are functions of the resulting parameter estimates. In our empirical tests, we find that although for the majority of securities information contents in small and large trades are similar, the average PIN for small trades is significantly higher than that in large trades. We also find that trading volume and institutional trading are the primary determinants of information content in small and large trades respectively but not of both. A further investigation of the securities with the largest differences in terms of PINs for small and large trades reveals that trade size alone distinguishes those firms from the rest- all eight firms reside in the lowest quartile in terms of average trade size.

Keywords: PIN, Trade Size, Institutional trading, Mixture model

JEL Classification: G19

Suggested Citation

Dey, Malay K. and Stern, Hal S. and Hongmei, Zhang, Information Content in Small and Large Trades (April 22, 2011). Economic Notes, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1819242

Malay K. Dey (Contact Author)

Cornell University ( email )

United States

Southern Illinois University at Edwardsville ( email )

1 Hairpin Drive
Edwardsville, IL 62026-1102
United States

Hal S. Stern

University of California, Irvine - Department of Statistics ( email )

Campus Drive
Irvine, CA 62697-3125
United States

Zhang Hongmei

affiliation not provided to SSRN ( email )

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