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Journal of Financial Econometrics Advance Access published online on February 26, 2008
Journal of Financial Econometrics, doi:10.1093/jjfinec/nbn003
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© The Author 2008. Published by Oxford University Press. All rights reserved. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org

Time-Varying Arrival Rates of Informed and Uninformed Trades

David Easley
     Cornell University

Robert F. Engle
     New York University

Maureen O'Hara
     Cornell University

Liuren Wu
     Baruch College, CUNY

Address correspondence to Robert F. Engle, Stern School of Business, New York University, 44 West 4th Street, Suite 9-62, NY 10012-1126, or e-mail: rengle{at}stern.nyu.edu.

JEL Classification: C51, C53, G10, G12, G14


  Abstract

We propose a dynamic econometric microstructure model of trading, and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity, market depth, and order flow. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for 16 actively traded stocks over 15 years of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades, which we use to construct forecasts of the probability of information-based trade (PIN). These forecasts are used in turn to forecast market liquidity as measured by bid-ask spreads and the price impact of orders. We observe that PINs vary across assets and over time, and most importantly that they are correlated across assets. Our analysis shows that one principal component explains much of the daily variation in PINs and that this systemic liquidity factor may be important for asset pricing. We also find that PINs tend to rise before earnings announcement days and decline afterwards.

KEYWORDS: Arrival rates, informed trades, uninformed trades, autoregressive process, market depth, liquidity

We thank Mark Ready, Schmuel Baruch, and seminar participants at New York University and the 2002 AFA meetings for helpful comments.

Received October 24, 2006; revised January 7, 2008; accepted January 10, 2008


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