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Prices as Aggregators of Private Information: Evidence from S&P 500 Futures Data

Published online by Cambridge University Press:  06 April 2009

Jin-Wan Cho  and
Murugappa Krishnan
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Abstract

This paper assesses the importance of the role of prices as aggregators of private information in the S&P 500 future market. We estimate primitive parameters of the Hellwig (1980) noisy rational expectations model, when both prices and terminal values are observable. The variance-covariance parameters governing futuers prices and terminal values can be inverted to obtain estimate of the primitive parmeters, including the precision of private infromation and the variance of liquidity-motivated trades. We also estimate coefficients in the linear price conjecture, weights that agents place on different sources of information, and the informativeness of prices. We find that the variance of the error term in agents' private signals is several orders of magnitude larger than the variance of liquidity-motivated trades. But, in a large market, prices are still so informative that the market as a whole appears to weight them more than prior beliefs.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 35 , Issue 1 , March 2000 , pp. 111 - 126
Copyright
Copyright © School of Business Administration, University of Washington 2000

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Footnotes

*

Dupree College of Management, Georgia Institue of Technology, Atlanta, GA 30332 and Rutgers University, 96 New England Avenue, #18, Summit, NJ 07901, respectively. This paper has benefited form earlier work by Krishnan with Uday Chandra. The authors are grateful for the comments, suggestions, criticism, and encouragement of Celal Aksu, Bruno Biais, Jordi Caballé, Rick Green, Mark Grinblatt, Guy Laroque, Fallaw Sowell, and participants at the 1996 European summer Symposium in Financial Market at Gerzenzee, Swizerland, where a previous version of this paper was presented. This study began when Krishnan was at the University of Minnesota, and the computer work has benefited in part from facilites provided by the Minnesota supercomputing Institue. The authors also thank Peter Bossaerts (a referee) and Guofu Zhou (associate editor and a referee).

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