Abstract

http://ssrn.com/abstract=1472033
 
 

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Informed Trading and Portfolio Returns


Alex Boulatov


University of Houston - C.T. Bauer College of Business

Terrence Hendershott


University of California, Berkeley - Haas School of Business

Dmitry Livdan


University of California, Berkeley

January 11, 2010


Abstract:     
We solve a multi-period model of strategic trading with long-lived information in multiple assets with correlated innovations in fundamental values. Market makers in each asset can only condition their price functions on trading in the that asset (but not on trading in the other asset). Using daily non-public data from the New York Stock Exchange we test the model's predictions on the conditional and unconditional lead-lag relations of institutional order flows and returns within portfolios. We find support for the model prediction of positive autocorrelations in portfolio returns as well as the predictions for how informed order flow positively predicts future returns and future informed order flow. As the model predicts we find these relations strengthen for portfolios formed from assets with higher correlation of fundamental values.

Number of Pages in PDF File: 42

Keywords: informed trading, portfolio autocorrelations, adverse selection

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Date posted: September 11, 2009 ; Last revised: January 14, 2010

Suggested Citation

Boulatov, Alex and Hendershott, Terrence and Livdan, Dmitry, Informed Trading and Portfolio Returns (January 11, 2010). Available at SSRN: http://ssrn.com/abstract=1472033 or http://dx.doi.org/10.2139/ssrn.1472033

Contact Information

Alex Boulatov
University of Houston - C.T. Bauer College of Business ( email )
Houston, TX 77204-6021
United States
713-743-4618 (Phone)
Terrence J. Hendershott (Contact Author)
University of California, Berkeley - Haas School of Business ( email )
545 Student Services Building
Berkeley, CA 94720
United States
Dmitry Livdan
University of California, Berkeley ( email )
545 Student Services Building
Berkeley, CA 94720
United States
(510) 642-4733 (Phone)
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