We show that the noninformation component of trading costs is priced in the cross-section of stock returns using intraday data for NYSE/AMEX stocks. More importantly, we show that the noninformation component is much larger and more strongly related to stock returns than is the adverse-selection component, indicating that the noninformation component plays a more important role in asset pricing than does the adverse-section component. We conduct a variety of robustness tests and show that our main results hold for different estimation methods, measures of the adverse-selection cost, subsample periods, and control variables. We offer plausible explanations for these results. Received December 27, 2014; accepted January 11, 2016 by Editor Maureen O’Hara.

This paper analyzes the Amihud (2002) measure of illiquidity and its role in asset pricing. It is shown first that the effect of illiquidity on asset pricing is clarified by using the turnover version of the Amihud measure and including firm size as a separate variable. When we decompose the Amihud measure into elements that correspond to positive (up) and negative (down) return days, we find that in general, only the down-day element commands a return premium. Further analysis of the up- and down-day elements using order flows shows that a sidedness variable, which captures the tendency for orders to cluster on the sell side on down days, is associated with a more significant return premium than the other components of the Amihud measure. (JEL G12)

Many proxies of illiquidity have been used in the literature that relates illiquidity to asset prices. These proxies have been motivated from an empirical standpoint. In this study, we approach liquidity estimation from a theoretical perspective. Our method explicitly recognizes the analytic dependence of illiquidity on more primitive drivers such as trading activity and information asymmetry. More specifically, we estimate illiquidity using structural formulae in line with Kyle's (1985) lambda for a comprehensive sample of stocks. The empirical results provide evidence that theory-based estimates of illiquidity are priced in the cross-section of expected stock returns, even after accounting for risk factors, firm characteristics known to influence returns, and other illiquidity proxies prevalent in the literature.

This article studies cross-sectional variations in trading activity for a comprehensive sample of NYSE/AMEX and Nasdaq stocks over a period of about 40 years. We test whether trading activity depends upon the degree of liquidity trading, the mass of informed traders, and the extent of uncertainty and dispersion of opinion about fundamental values. We hypothesize that liquidity (or noise) trading depends both on a stock’s visibility and on portfolio rebalancing needs triggered by past price performance. We use firm size, age, price, and the book-to-market ratio as proxies for a firm’s visibility. The mass of informed agents is proxied by the number of analysts whereas forecast dispersion and firm leverage proxy for differences of opinion. Earning volatility and absolute earning surprises proxy for uncertainty about fundamental values. Overall, the results provide support for theories of trading based on stock visibility, portfolio rebalancing needs, differences of opinion, and uncertainty about fundamental values.

We explore the dynamics of informed trading around corporate announcements of merger bids, dividend initiations, SEOs, and quarterly earnings by calculating daily posterior probabilities of informed buying and selling. We find evidence of informed trading before the announcements and a significant part of the news in announcements is impounded in stock prices before the announcements by pre-event informed trading. We also find evidence of informed trading after the announcements. Most strikingly, the probability of informed trading after merger bids predicts the probability of the bid being withdrawn or met with a competing bid. For other announcements, post-announcement informed-trading probabilities predict subsequent returns. Received September 26, 2016; editorial decision December 17, 2017 by Editor Andrew Karolyi.

This study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and conditional versions of the Fama and French (1993) 3-factor model. In particular, the DFPM can explain and better forecast the momentum portfolio returns, which are mostly missed by alternative models.