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Informed Trading and Portfolio Returns

42 Pages Posted: 11 Sep 2009 Last revised: 14 Jan 2010

Alex Boulatov

HSE Moscow; 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

Date Written: 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.

Keywords: informed trading, portfolio autocorrelations, adverse selection

Suggested Citation

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

Alex Boulatov

HSE Moscow ( email )

26 Shabolovka
Moscow
Russia

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, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

Dmitry Livdan

University of California, Berkeley ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States
(510) 642-4733 (Phone)

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