We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We examine how sell-side equity analysts strategically disclose information of differing quality to the public versus the buy-side mutual fund managers to whom they are connected. We consider cases in which analysts recommend that the public buys a stock, but some fund managers sell it. We...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

We propose a unified theory of asset price determination encompassing both "conventional" and "alternative" asset classes (private equity, real estate, etc.). The model features disruption of old by young firms and skewness in the distribution of innovative rents among the young innovators. The...

This paper defines risk-on risk-off (RORO), an elusive terminology in pervasive use, as the variation in global investor risk aversion. Our high-frequency RORO index captures time-varying investor risk appetite across multiple dimensions: advanced economy credit risk, equity market volatility,...

Is shareholder interest in corporate social responsibility driven by pecuniary motives (abnormal rates of return) or non-pecuniary ones (willingness to sacrifice returns to address various firm externalities)? To answer this question, we categorize the literature into seven tests: (1) costs of...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

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,...

We find that procyclical stocks, whose returns comove with business cycles, earn higher average returns than countercyclical stocks. We use almost a three-quarter century of real GDP growth expectations from economists' surveys to determine forecasted economic states. This approach largely...

In recent years, impact investors - private investors who seek to generate simultaneously financial and social returns - have attracted intense interest and controversy. We analyze a novel, comprehensive data set of impact and traditional investors to assess how the non-financial characteristics...