We estimate investors’ disagreement regarding firm news and assess its ability to predict stock returns. Specifically, we quantify firm-level investor disagreement through the volume-volatility elasticity surrounding firm news; higher elasticity is associated with less investor disagreement. Intuitively, disagreement introduces additional trading motives that are not driven by price changes, weakening the connection between volume and volatility. Our findings indicate that investor disagreement on news negatively predicts cross-sectional returns. We also present empirical evidence aligned with the theoretical predictions of a recently developed model by Atmaz and Basak (Journal of Finance 73 (3): 1225–1279 2018) that the negative disagreement-stock relation strengthens when the optimism effect dominates the uncertainty effect. Importantly, this predictive relationship remains robust after controlling for news heterogeneity, other volume- and volatility-based measures, and alternative channels.

Based on intraday data for a large cross section of individual stocks and newly developed econometric procedures, we decompose the realized variation for each of the stocks into separate so-called realized up and down semi-variance measures, or “good” and “bad” volatilities, associated with positive and negative high-frequency price increments, respectively. Sorting the individual stocks into portfolios based on their normalized good minus bad volatilities results in economically large and highly statistically significant differences in the subsequent portfolio returns. These differences remain significant after controlling for other firm characteristics and explanatory variables previously associated with the cross section of expected stock returns.

We propose a novel high-frequency decomposition of daily stock returns into news- and non-news-driven components, and uncover evidence of pervasive stock market underreaction to firm news. Prices tend to drift in the same direction as the initial market response for several days after the news arrival without reversals. A trading strategy exploiting the return drift generates high abnormal returns and remains profitable after transaction costs. To understand the economic mechanism, we find that the return drift is stronger when investors are distracted. Analysts’ slow adjustments of market expectations following firm news also contribute to the market underreaction.

We investigate how individual equity prices respond to continuous and jumpy market price moves and how these different market price risks, or betas, are priced in the cross section of expected stock returns. Based on a novel high-frequency data set of almost 1,000 stocks over two decades, we find that the two rough betas associated with intraday discontinuous and overnight returns entail significant risk premiums, while the intraday continuous beta does not. These higher risk premiums for the discontinuous and overnight market betas remain significant after controlling for a long list of other firm characteristics and explanatory variables.

This paper analyzes how trading after shareholder meetings changes the composition of the shareholder base. Analyzing daily trades, we find that mutual funds reduce their holdings if their votes are opposed to the voting outcome. Trading volume is high even when stock prices do not change, peaks on the meeting date, and remains high up to four weeks after shareholder meetings. The results support models based on differences of opinion that predict that shareholders’ beliefs may diverge more after observing voting outcomes. Hence, trading after meetings creates a more homogeneous shareholder base, which has important implications for corporate governance.

Based on high frequency S & P 500 exchange-traded fund (ETF) data from 1993–2013, we show an intraday momentum pattern: the first half-hour return on the market as measured from the previous day’s market close predicts the last half-hour return. This predictability, which is both statistically and economically significant, is stronger on more volatile days, on higher volume days, on recession days, and on major macroeconomic news release days. Intraday momentum also exists for ten other most actively traded domestic and international ETFs. Theoretically, the intraday momentum is consistent not only with Bogousslavsky’s (2016) model of infrequent portfolio rebalancing but also with a model of late-informed trading near the market close.

We identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.