We show that the out-of-sample forecast of the equity risk premium can be significantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fifteen predictors from the existing literature, for the...

I explore the relationship between financial frictions and asset prices in a closed economy model, and study the implications for the leverage cycle of financial intermediaries. I develop a continuous-time dynamic macroeconomic model with heterogeneous agents and limited stock market...

How do arbitrageurs find variables that predict returns? If a predictor lasts 30 days or more, then a clever arbitrageur can use his intuition to get the job done. But, what's an arbitrageur supposed to do if a predictor lasts 30 minutes or less? An arbitrageur's intuition is useless if the...

A representative investor confronts two levels of model uncertainty. The investor has a set of well defined parametric “structured models” but does not know which of them is best. The investor also suspects that all of the structured models are misspecified. These uncertainties about...

We propose a belief-generating model from which we build a statistical measure of investor disagreement. We simulate differences in beliefs across investors by endowing them with different machine learning models for forecasting returns from the same set of inputs. We measure disagreement as the...

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

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 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 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...