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.

The market for U.S. equity indexes presently comprises floor‐traded index futures contracts, exchange‐traded funds (ETFs), electronically traded, small‐denomination futures contracts (E‐minis), and sector ETFs that decompose the S&P 500 index into component industry portfolios. This paper empirically investigates price discovery in this environment. For the S&P 500 and Nasdaq‐100 indexes, most of the price discovery occurs in the E‐mini market. For the S&P 400 MidCap index, price discovery is shared between the regular futures contract and the ETF. The S&P 500 ETF contributes markedly to price discovery in the sector ETFs, but there are only minor effects in the reverse direction.

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 (NYSE) (a median 92.7 percent information share).

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).

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 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.

The primary purpose of this paper is the use of survey expectations data to study the empirical relationships between stock returns, inflation, and economic activity. In the course of this analysis and as a secondary purpose, the paper discusses general considerations involving the use of expectations proxies and makes recommendations for econometric techniques. The main empirical findings are: (1) Hypothesized relationships between expected economic activity and expected inflation do not in practice appear to be important in explaining the negative relationship between expected inflation and stock returns. (2) Nevertheless, the survey data do lend some support to the hypothesis of a quantity theory relationship between expected inflation and expected economic activity, holding constant monetary growth. (3) The cross‐forecaster dispersion of economic activity forecasts, a proxy for real uncertainty, appears to be a significant determinant of stock returns. Inclusion of this variable eliminates the negative impact of expected inflation.

The primary purpose of this paper is the use of survey expectations data to study the empirical relationships between stock returns, inflation, and economic activity. In the course of this analysis and as a secondary purpose, the paper discusses general considerations involving the use of expectations proxies and makes recommendations for econometric techniques. The main empirical findings are: (1) Hypothesized relationships between expected economic activity and expected inflation do not in practice appear to be important in explaining the negative relationship between expected inflation and stock returns. (2) Nevertheless, the survey data do lend some support to the hypothesis of a quantity theory relationship between expected inflation and expected economic activity, holding constant monetary growth. (3) The cross-forecaster dispersion of economic activity forecasts, a proxy for real uncertainty, appears to be a significant determinant of stock returns. Inclusion of this variable eliminates the negative impact of expected inflation.