Measuring the Information Content of Stock Trades

This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE issues suggest: a trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and, information asymmetries are more significant for smaller firms. CENTRALTO THE ANALYSIS of market microstructure is the notion that in a market with asymmetrically informed agents, trades convey information and therefore cause a persistent impact on the security price. The magnitude of the price effect for a given trade size is generally held to be a positive function of the proportion of potentially informed traders in the population, the probability that such a trader is in fact informed (i.e., the probability that a private information signal has in fact been observed), and the precision of the private information. The close dependence of the price impact on these factors, which may be referred to as the extent of the information asymmetry, provides a strong motivation for the empirical determination of this impact. This paper strives to achieve such a determination in a framework that is robust to deviations from the assumptions of the formal models. In the process, the framework establishes a rich characterization of the dynamics by which trades and quotes interact. The market considered here is a specialist market in which a designated market-maker exposes bid and ask quotes to the trading public. An extensive theory has evolved that analyzes the market-maker's exposure to traders with superior information.' Concerning the extent of the information asymmetry, this body of theory yields two important empirical predictions: first, *Department of Finance, Leonard N. Stern School of Business, New York University. For comments on an earlier draft I am indebted to Larry Harris, Robert Wood, and seminar participants at Columbia University, Duke University, Pennsylvania State University, Southern Methodist University, and the Securities and Exchange Commission. I am especially indebted to the referee Larry Glosten for the illustrative microstructure model used in Section I1 and for his help in framing the argument of Section 111. All errors are my own responsibility. 'See Bagehot (1971), Copeland and Galai (1983), Glosten and Milgrom (1985), Kyle (1985), Easley and O'Hara (1987), Glosten (1987, 1989), Admati and Pfleiderer (1988), and Foster and Viswanathan (1987).