When homogeneous or closely-linked securities trade in multiple markets, it is often of interest to determine where price discovery (the incorporation of new information) occurs. This article suggests an econometric approach based on an implicit unobservable efficient price common to all markets. The information share associated with a particular market is defined as the proportional contribution of that market's innovations to the innovation in the common efficient price. Applied to quotes for the thirty Dow stocks, the technique suggests that the preponderance of the price discovery takes place at the New York Stock Exchange (NYSE) (a median 92.7 percent information share).ALTHOUGH MOST OF THE CLASSIC paradigms in market microstructure concern a security that trades in a single centralized market, such situations are becoming increasingly rare in practice. Fragmentation, the dispersal of trading in a security to multiple sites, has emerged as a dominant institutional trend. This process is of concern to financial economists and regulators because price information and price discovery (the impounding of new information into the security price), arguably the most important products of a security market, have many attributes of public goods. As the process of fragmentation accelerates, it may be important to determine where the price information and price discovery are being produced.1 This paper suggests a practical econometric approach to this problem.The technique is illustrated with an application to U.S. equity markets. Many stocks listed on the New York Stock Exchange (NYSE) trade concurrently on the regional exchanges and the National Association of Securities Dealers' National Market System (NASD NMS). Since a share of IBM stock is the same security whether purchased on the Midwest or Pacific Exchange, this is a particularly clear instance of multiple markets. Nevertheless, the expres-* Stern School of Business, New York University. For comments on an earlier draft, I am indebted to the editor, the anonymous referees,

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.CENTRAL TO 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 (1987Glosten ( , 1989, Admati and Pfleiderer (1988), and Foster and Viswanathan (1987). 179The Journal of Finance that the asymmetry is positively related to the spread (between the bid and ask quotes), and second, that the asymmetry is positively related to the price impact of a trade. The first effect has led empirical researchers seeking measurable proxies for information asymm...

When homogeneous or closely‐linked securities trade in multiple markets, it is often of interest to determine where price discovery (the incorporation of new information) occurs. This article suggests an econometric approach based on an implicit unobservable efficient price common to all markets. The information share associated with a particular market is defined as the proportional contribution of that market's innovations to the innovation in the common efficient price. Applied to quotes for the thirty Dow stocks, the technique suggests that the preponderance of the price discovery takes place at the New York Stock Exchange (NYSE) (a median 92.7 percent information share).

The effective cost of trading is usually estimated from transaction-level data. This study proposes a Gibbs estimate that is based on daily closing prices. In a validation sample, the daily Gibbs estimate achieves a correlation of 0.965 with the transactionlevel estimate. When the Gibbs estimates are incorporated into asset pricing specifications over a long historical sample (1926 to 2006), the results suggest that effective cost (as a characteristic) is positively related to stock returns. The relation is strongest in January, but it appears to be distinct from size effects.INVESTIGATIONS INTO THE ROLE of liquidity and transaction costs in asset pricing must generally confront the fact that while many asset pricing tests make use of U.S. equity returns from 1926 onward, the high-frequency data used to estimate trading costs are usually not available prior to 1983. Accordingly, most studies either limit the sample to the post-1983 period of common coverage or use the longer historical sample with liquidity proxies estimated from daily data. This paper falls into the latter group. Specifically, I propose a new approach to estimating the effective cost of trading and the common variation in this cost. These estimates are then used in conventional asset pricing specifications with a view to ascertaining the role of trading costs as a characteristic in explaining expected returns. 1 * Hasbrouck is with the Stern School of Business, New York University. For comments and suggestions I am grateful to the editor, the referee, Yakov Amihud,Lubos Pástor, Bill Schwert, Jay Shanken, Kumar Venkataraman, Sunil Wahal, and seminar participants at the University of Rochester, the NBER Microstructure Research Group, the Federal Reserve Bank of New York, Yale University, the University of Maryland, the University of Utah, Emory University, and Southern Methodist University. All errors are my own responsibility. Earlier versions of this paper and an SAS data set containing the long-run Gibbs sampler estimates are available on my web site at www.stern.nyu.edu/∼jhasbrou.1 Recent asset pricing analyses based on samples in which high-frequency data are available include Brennan and Subrahmanyam (1996), Easley, Hvidkjaer, and O'Hara (2002), Sadka (2004), and Korajczyk and Sadka (2008). Analyses that use proxies based on daily data include Amihud (2002), Pástor and Stambaugh (2003), Acharya and Pedersen (2005), and Spiegel and Wang (2005). Closing daily or annual bid-ask quotes are sometimes available over samples longer than those of the high-frequency data. Studies that use closing spreads include Stoll and Whalley (1983), Amihud and Mendelson (1986), Eleswarapu and Reinganum (1993), Reinganum (1990), andChalmers andKadlec (1998). provide a broad survey of the links between asset pricing and microstructure. 1446The Journal of Finance R For a buy order executed in a single trade, the effective cost is the execution price less the midpoint of the prevailing bid and ask quotes (and vice versa for a sale). In the simplest setting...