This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

Despite positive and significant earnings announcement premia, we find that institutional investors reduce their exposure to stocks before earnings announcements. A novel result on the sensitivity of flows to individual stock returns provides a potential explanation. We show that extreme...

This paper uses transaction-level data across millions of accounts to identify cryptocurrency investors and evaluate how fluctuations in individual crypto wealth affect household consumption, equity investment, and local real estate markets. We estimate an MPC out of unrealized crypto gains that...

Socially responsible (SR) institutions tend to focus more on the ESG performance and less on quantitative signals of value. Consistent with this difference in focus, we find that SR institutions react less to quantitative mispricing signals. Our evidence suggests that the increased focus on ESG...

We analyze institutional investors' preferences for stocks and the implications that these preferences have for stock-market prices and returns. We find that -- a category including all managers with greater than $100 million under discretionary control -- have nearly doubled their share of the...

This paper uses a new data set of quarterly portfolio holdings of 769 all-equity pension funds between 1985 and 1989 to evaluate the potential effect of their trading on stock prices. We address two aspects of trading by money managers: herding, which refers to buying (selling) the same stocks...

We present an alternative expectation formation mechanism that helps rationalize well known asset pricing anomalies, such as the predictability of excess returns, excess volatility, and the equity-premium puzzle. As with rational expectations (RE), the expectation formation mechanism we consider...

This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have...

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised...

Recent studies have used the value spread to predict aggregate stock returns to construct cash-flow betas that appear to explain the size and value anomalies. We show that two related variables, the book-to-market spread (the book-to-market of value stocks minus that of growth stocks) and the...