At subsecond horizons, bids and offers in U.S. equity markets are more volatile than what would be implied by long-term fundamentals. To assess costs and consequences, this paper suggests that traders’ random delays (latencies) interact with quote volatility to generate execution price risk and relative latency costs. Analysis of the behavior of quote setters suggests that this volatility is more likely to arise from recurrent cycles of undercutting similar to the Edgeworth cycles found in product markets rather than mixed strategies of limit-order placement.

Motivated by economic models of sequential trade, empirical analyses of market dynamics frequently estimate liquidity as the coefficient of signed order flow in a price change regression. This paper implements such an analysis for futures transaction data from pit trading. To deal with the absence of timely bid and ask quotes (which are used to sign trades in most equity market studies), this paper proposes new techniques based on Markov chain Monte Carlo estimation. The model is estimated for four representative Chicago Mercantile Exchange contracts. The highest liquidity (lowest order flow coefficient) is found for the S&P 500 index. Liquidity for the Euro and U.K. £ contracts is somewhat lower. The pork belly contract exhibits the least liquidity.

In deciding among these estimators for a particular application, gains in efficiency must be weighed against additional computational effort. On these grounds, there would seem to be good reason in most circumstances for preferring the wrap-around overlap estimator to the simple unbiased one. The gain in efficiency is achieved at a small cost in additional computation, and, in addition, no data need be discarded if the sample size (T) does not happen to be an integral multiple of N. In going beyond the wrap-around estimator to the combinatorial one, however, the decision is less clear. The gain in efficiency is relatively small and the number of return relatives to be computed may become quite large. For example, with T=30 and N=10, the last parameter set for which computations are made in Table 1, evaluation of the combinatorial estimator requires computation of (= approximately 3×108) ten-period return relatives. In many applications, this additional effort is probably not worthwhile. Significantly, the ordinary overlap estimator seems to fare worst, probably due to its asymmetric use of the data, and, therefore, should not be used.It also should be noted that when relatively large amounts of data are available, the simple unbiased estimator will in most cases perform quite adequately. A common application in financial research involves computing annual stock returns from monthly data. Representative relative efficiency computations using μ = 1.01, σ = .05, and N = 12 are given in panel B of Table 1 for T = 60, 120, and 360 months. The incremental improvement of the wrap-around estimator relative to the simple one is quite small. Figures for the combinatorial estimator are not presented because the number of permutations would be prohibitively large.

This paper is an econometric analysis of the information content of automated orders arriving at the NYSE. The model captures the joint behavior of automated orders and also the return on the stock index future and the futures-spot basis. The results indicate that orders contain information useful in predicting stock returns beyond the information contained in the reported trades. Furthermore, program and index-arbitrage orders contain information beyond that available from the futures return and basis, suggesting that these orders are not merely passive conveyors of common-factor information. Nonprogram, program, and index-arbitrage orders have roughly similar price impacts.

This empirical examination of the relation between trades and quote revisions for New York Stock Exchange-listed stocks is designed to ascertain asymmetric-information and inventory-control effects. This study finds that negative autocorrelation in trades consistent with inventory-control behavior characterizes low-volume stocks, but not high-volume stocks. The evidence of inventory control in the impact of trades on quote revisions is inconclusive. The information content of trades, on the other hand, is found to be substantial. There is also strong evidence that large trades convey more information than small trades.

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 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. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

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 NYSE-listed companies, trade 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. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.