Using an improved measure of a common stock's annualized dividend yield, we document that risk‐adjusted NYSE stock returns increase in dividend yield during the period from 1963 to 1994. This relation between return and yield is robust to various specifications of multifactor asset pricing models that incorporate the Fama–French factors. The magnitude of the yield effect is too large to be explained by a “tax penalty” on dividend income and is not explained by previously documented anomalies. Interestingly, the effect is primarily driven by smaller market capitalization stocks and zero‐yield stocks.

This paper addresses the issue of the optimal trading system for less actively traded (i.e., ‘thinly-traded’) stocks. We compare the performance of a pure order driven market with limit order book (POD) with that of a hybrid order driven market with specialist and limit order book (HOD). We find that the HOD system offers superior performance along several dimensions of market quality. In particular, the specialist-based system offers lower execution costs, greater depth, a significant increase in the depth-to-spread ratio, and lower adverse selection costs. Very ‘thinly-traded’ stocks benefit more than less inactive stocks from the adoption of a hybrid trading system both in terms of greater liquidity and in terms of lower adverse selection costs.

In this paper, we present a simple model which relates security returns to three components: an expected return, a bid-ask error, and white noise. The relative importance of the various components is empirically assessed, and the model's ability to explain the various time-series properties of individual security and portfolio returns is tested. Time-varying expected returns and bid-ask errors are found to explain substantial proportions (up to 24%) of the variance of security returns. We also reconcile the typically negative autocorrelation in security returns with the strong positive autocorrelation in portfolio returns.

Abstract We show that time variation in expected returns and/or partial price adjustments lead to a downward bias in previous estimators of both the spread and its components. We introduce a new approach that provides unbiased and efficient estimators of the components of the spread. We find that between 77 and 97 percent of the downward bias in previous spread estimates is caused by time variation in expected returns. More importantly, the adverse-selection component, though significant, accounts for a much smaller proportion (8 to 13 percent) of the quoted spread, at least for small trades, than the proportion (over 40 percent) previously reported in the literature. Order processing costs are the predominant component of quoted spreads.

We assess the market microstructure properties of U.S. banking firms’ equity, to determine whether they exhibit more or less evidence of asset opaqueness than similar-sized nonbanking firms. The evidence indicates that large bank holding companies (BHC), traded on the NYSE, have very similar trading properties to their matched nonfinancial firms. In contrast, smaller BHCs, traded on NASDAQ, trade much less frequently despite having very similar spreads. Analysis of IBES earnings forecasts indicates that banking assets are not unusually opaque; they are simply boring. The implications for regulatory policy and future market microstructure research are discussed.

Underwriters using bookbuilding can allocate shares of initial public offerings (IPOs) on the basis of, among other things, commissions paid by investors. In testing the hypothesis that investors trade liquid stocks in order to affect their IPO allocations, we find that money left on the table by IPOs is related to the trading volume of the 50 most liquid stocks near the offer date. For an IPO that leaves $1 billion on the table, there is abnormal volume of 2.7% to 4.1% in the 50 most liquid stocks over the six days ending on the day that trading commences in that IPO, although only during the internet bubble period is this volume increase statistically significant.

Prior work with competitive rational expectations equilibrium models indicates that there should be a positive relation between trading volume and differences in beliefs or information among traders. We show that this result is sensitive to whether and how transaction costs are modeled. In a specialist market with endogenous transaction costs we show that trading volume can be negatively related to the degree of informational asymmetry in the market. Our analysis highlights the dependence of volume on market structure, and our results suggest that the “volume effects” of corporate or macroeconomic events reflect a decrease, rather than an increase, in heterogeneity of beliefs or asymmetry of information.

ABSTRACT Prior work with competitive rational expectations equilibrium models indicates that there should be a positive relation between trading volume and differences in beliefs or information among traders. We show that this result is sensitive to whether and how transaction costs are modeled. In a specialist market with endogenous transaction costs we show that trading volume can be negatively related to the degree of informational asymmetry in the market. Our analysis highlights the dependence of volume on market structure, and our results suggest that the “volume effects” of corporate or macroeconomic events reflect a decrease, rather than an increase, in heterogeneity of beliefs or asymmetry of information.