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...

This paper develops and implements a new test to investigate whether sell-side analysts herd around the consensus when they make stock recommendations. Our empirical results support the herding hypothesis. Stock price reactions following recommendation revisions are stronger when the new...

We study a market for funding real investment in which valuation creates information on which adverse selection can occur. Unlike in previous models, higher amounts of valuation are associated with lower market prices and so greater returns to valuation, and this strategic complementarity in the...

Even if an asset has no fundamental uncertainty with a constant dividend process, a stochastic sentiment-driven equilibrium for the asset price exists besides the well-known fundamental equilibrium. Our paper constructs such sentiment-driven equilibria under general utility functions within an...

Recent crises have seen very large spikes in asset price risk without dramatic shifts in fundamentals. We propose an explanation for these risk panics based on self-fulfilling shifts in risk made possible by a negative link between the current asset price and risk about the future asset price....

Motivated from investment-based asset pricing, we propose a new factor model consisting of the market factor, a size factor, an investment factor, and a return on equity factor. The new factor model outperforms the Carhart four-factor model in pricing portfolios formed on earnings surprise,...

After the Covid-shock in March 2020, stock prices declined abruptly, reflecting both the deterioration of investors' expectations of economic activity as well as the surge in aggregate risk aversion. In the following months however, whereas economic activity remained sluggish, equity markets...

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...