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 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 document that value-to-price, the ratio of Residual-Income-Model-based valuation to market price, subsumes the power of book-to-market ratio and many other value or quality measures in predicting stock returns. Long-short value-to-price portfolios hedge against momentum, revitalize 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 propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

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,...

In this paper we use the functional vector autoregression (VAR) framework of Chang, Chen, and Schorfheide (2024) to study the effects of monetary policy shocks (conventional and informational) on the cross-sectional distribution of U.S. earnings (from the Current Population Survey), consumption,...

We develop a method to identify the individual latent propensity to select into treatment and marginal treatment effects. Identification is achieved with survey data on individuals' subjective expectations of their treatment propensity and of their treatment-contingent outcomes. We use the...

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the...

We propose a new tool to filter non-linear dynamic models that does not require the researcher to specify the model fully and can be implemented without solving the model. If two conditions are satisfied, we can use a flexible statistical model and a known measurement equation to back out the...