This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firm-level size and book-to-market as well as with macroeconomic variables. With constant beta, none of the models examined capture any of the market anomalies. When beta is allowed to vary, the size and value effects are often explained, but the explanatory power of past return remains robust. The past return effect is captured by model mispricing that varies with macroeconomic variables.

This article proposes a trading-based explanation for the asymmetric effect in daily volatility of individual stock returns. Previous studies propose two major hypotheses for this phenomenon: leverage effect and time-varying expected returns. However, leverage has no impact on asymmetric volatility at the daily frequency and, moreover, we observe asymmetric volatility for stocks with no leverage. Also, expected returns may vary with the business cycle, that is, at a lower than daily frequency. Trading activity of contrarian and herding investors has a robust effect on the relationship between daily volatility and lagged return. Consistent with the predictions of the rational expectation models, the non-informational liquidity-driven (herding) trades increase volatility following stock price declines, and the informed (contrarian) trades reduce volatility following stock price increases. The results are robust to different measures of volatility and trading activity.

This article explores cross-market liquidity dynamics by estimating a vector autoregressive model for liquidity (bid-ask spread and depth, returns, volatility, and order flow in the stock and Treasury bond markets). Innovations to stock and bond market liquidity and volatility are significantly correlated, implying that common factors drive liquidity and volatility in these markets. Volatility shocks are informative in predicting shifts in liquidity. During crisis periods, monetary expansions are associated with increased liquidity. Moreover, money flows to government bond funds forecast bond market liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity.

[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.]

This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firm-level size and book-to-market as well as with macroeconomic variables. With constant beta, none of the models examined capture any of the market anomalies. When beta is allowed to vary, the size and value effects are often explained, but the explanatory power of past return remains robust. The past return effect is captured by model mispricing that varies with macroeconomic variables.

We use information from over 2 million trading strategies randomly generated using real data and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute t-statistic thresholds that control for multiple hypothesis testing, when searching for anomalies, at 3.8 and 3.4 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of false rejections that researchers would produce if they failed to account for multiple hypothesis testing to be about 45%.

This article proposes a trading-based explanation for the asymmetric effect in daily volatility of individual stock returns. Previous studies propose two major hypotheses for this phenomenon: leverage effect and time-varying expected returns. However, leverage has no impact on asymmetric volatility at the daily frequency and, moreover, we observe asymmetric volatility for stocks with no leverage. Also, expected returns may vary with the business cycle, that is, at a lower than daily frequency. Trading activity of contrarian and herding investors has a robust effect on the relationship between daily volatility and lagged return. Consistent with the predictions of the rational expectation models, the non-informational liquidity-driven (herding) trades increase volatility following stock price declines, and the informed (contrarian) trades reduce volatility following stock price increases. The results are robust to different measures of volatility and trading activity.

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.