[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.]

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.