[I discuss a new method for measuring the deviations between actual transaction prices and implicit efficient prices. The approach decomposes security transaction prices into random-walk and stationary components. The random-walk component may be identified with the efficient price. The stationary component, the difference between the efficient price and the actual transaction price, is termed the pricing error. Its dispersion is a natural measure of market quality. I describe practical strategies for estimating these quantities. For a sample of NYSE stocks, the average pricing error standard deviation estimate is roughly 0.33 percent of the stock price. If the pricing error is normally distributed and if it is always a positive cost incurred by the transaction initiators, the corresponding average transaction cost for these traders is 0.26 percent of the stock price. The dispersion of the pricing error is also found to be elevated at the beginning and end of the trading session.]

[In a security market with asymmetrically informed participants, trades are signals of private information. In this article, new measures of trade informativeness are proposed based on a decomposition of the variance of changes in the efficient price into trade-correlated and -uncorrelated components. The trade-correlated component has a natural interpretation as an absolute measure of trade informativeness. The ratio of this component to the total variance is a relative measure (i.e., a proportion normalized with respect to the total public information). For a sample of NYSE-listed companies, trades are found to be more informative for small firms in both absolute and relative senses. From an analysis of intraday patterns, it appears that trades are in absolute terms more informative at the beginning of trading, but slightly less informative in relative terms.]

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 transaction-level 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.

This paper presents an empirical microstructure model of bid and ask quotes that features discreteness, random costs of market making, and ARCH volatility effects. Applied to intraday quotes at 15‐minute intervals for Alcoa (a randomly chosen Dow stock), the results show that quote exposure costs contain stochastic components that are persistent and large relative to the deterministic intraday “U” components. Analysis of the filtered estimates of the system suggest that bid and ask costs contain common components, and that these costs reflect risk as proxied by ARCH variance forecasts.

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 (a median 92.7 percent information share).

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.

[This article investigates the behavior of intraday trades and quotes for individual stocks on the Tokyo Stock Exchange (TSE). We examine the transaction and quote record for three firms for the first 3 months of 1990. Our findings suggest that the immediacy available (at least for small trades) in the market is high, despite the reliance on public limit orders to supply liquidity. When orders that would otherwise walk through the limit order book are converted into limit orders, execution is delayed, but some orders execute (at least in part) at more favourable prices.]

This paper proposes a dynamic model of bid and ask quotes that incorporates a stochastic cost of market-making, discreteness (restriction of quotes to a fixed grid) and clustering (the tendency of quotes to lie on `natural' multiples of the tick size). The Gibbs sampler provides a convenient vehicle for estimation. The model is estimated for daily and intradaily US Dollar/Deutschemark Reuters quotes.