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 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,...

The extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the...

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...

Several recent studies have attributed a large part of asset price volatility to self-fulfilling expectations. Such an explanation is unattractive to many since it allows allocations that need bear no particular relation to those implied by the economist's standard kit of

We propose a framework for understanding recurrent historical episodes of vigorous economic expansion accompanied by extreme asset valuations, as exhibited by the U.S. in the 1990s. We interpret this phenomenon as a high-valuation equilibrium with a low effective cost of capital based on...

We analyze cross-sectional and time series information from forty-seven equity markets around the world, to consider whether short-sales restrictions affect the efficiency of the market, and the distributional characteristics of returns to individual stocks and market indices. Using the approach...

A number of countries have delayed the opening of their capital markets to international" investment because of reservations about the impact of foreign speculators on both expected" returns and market volatility. We propose a cross-sectional time-series model that attempts to" assess the impact...

There have been enormous differences of opinion between U.S. and Japanese institutional investors about the outlook for stock prices, differences across the two countries in average one-year-ahead forecasts for the Japanese stock market as great as twenty percentage points. In the past two years...

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...