I investigate cross-sectional variation in stock returns over the trading day and overnight to shed light on what drives asset pricing anomalies. Margin requirements are higher overnight, and lending fees are typically charged only on positions held overnight. Such institutional constraints and overnight risk incentivize arbitrageurs who trade on mispricing to reduce their positions before the end of the day. Consistent with this intuition, a mispricing factor earns positive returns throughout the day but performs poorly at the end of the day. This pattern strengthens in the second half of the sample and is shared by several well-known anomalies.

A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross‐section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross‐sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross‐section of stock returns, which can help explain available empirical evidence.

We investigate the impact of an exogenous trading glitch at a high-frequency market-making firm on standard measures of stock liquidity (spreads, price impact, turnover, and depth) and institutional trading costs (implementation shortfall and volume-weighted average price slippage). Stocks in which the firm accumulates large long (short) positions increase (decrease) by about 4% during the glitch and become substantially more illiquid. It takes one day for prices and spread-based liquidity measures to revert. Institutional trading costs, however, remain significantly higher for more than one week. Both liquidity measures are also weakly correlated outside the glitch period, suggesting they capture different aspects of liquidity.

We examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post‐decimalization period. Motivated by a continuous‐time inventory model, we compute a high‐frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high‐frequency order imbalance volatility is an important driver of liquidity and explains the often positive time‐series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross‐section of stock returns.

We study how intraday and overnight components of past returns predict future stock returns from 1926 to 2019. Portfolios formed on past intraday returns display momentum without long-term reversal, whereas portfolios formed on past overnight returns display no momentum. We link this asymmetric day-night pattern to the fact that most trading occurs intraday, which has remained stable over time. Evidence from international stock markets, intraday intervals, and analyst expectations suggests that investors underreact to private information revealed through trading. This underreaction mechanism is most consistent with Hong and Stein’s (1999) theory of momentum.

We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.