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Information Content in Small and Large Trades


Malay K. Dey


Cornell University

Hal S. Stern


University of California, Irvine - Department of Statistics

Zhang Hongmei


affiliation not provided to SSRN

April 22, 2011

Economic Notes, Forthcoming

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.

Number of Pages in PDF File: 45

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

JEL Classification: G19

Accepted Paper Series


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Date posted: April 24, 2011  

Suggested Citation

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

Contact Information

Malay K. Dey (Contact Author)
Cornell University ( email )
Ithaca, NY 14853
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 )
Feedback to SSRN (Beta)


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