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...

Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors. Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets. Retail...

The abnormal return associated with a stock being added to the S&P 500 has fallen from an average of 3.4% in the 1980s and 7.6% in the 1990s to 0.8% over the past decade. This has occurred despite a significant increase in the percentage of stock market assets linked to the index. A similar...

We characterize how risk evolves during a crisis. Using high-frequency data, we find that the first two principal components (PCs) of the covariance matrix of global asset returns experience large, sudden, and temporary spikes coinciding with well-known crises - Covid-19 pandemic, Global...

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...

We provide evidence for a causal link between the US economy and the global financial cycle. Using intraday data, we show that US macroeconomic news releases have large and significant effects on global risky asset prices. Stock price indexes of 27 countries, the VIX, and commodity prices all...

We study strategic disclosure timing by correlated firms in the presence of risk-averse investors. Firms delay disclosures in the hope that positively correlated firms will announce especially good news and lift their own price. Risk premia rise before disclosures, drop when disclosures occur,...

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,...

This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. We find that most relevant news, both idiosyncratic and systematic, lead quickly to price jumps, as market...

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...