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...

The market risk premium is central in finance, and has been analyzed by numerous studies in the time-series predictability literature and by growing studies in the options literature. In this paper, we provide a novel link between the two literatures. Theoretically, we derive a lower bound on...

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 investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Allocation , Value at Risk,...

Dynamic equilibrium models based on present value computation imply that returns are predictable but also generate particular patterns of predictability in asset returns. I take advantage of this to construct a set of tests of Equilibrium Generated Predictability (EGP). I apply the tests to...

Value and momentum returns and combinations of them are explained by their loadings on global macroeconomic risk factors across both countries and asset classes. These loadings describe why value and momentum have positive return premia while at the same time being negatively correlated. The...

Our paper reexamines whether 29 variables from 26 papers published after Goyal and Welch (2008), as well as the original 17 variables, were useful in predicting the equity premium in-sample and out-of-sample. Our samples include the original periods in which these variables were identified, but...

This article introduces a very flexible framework for causal and predictive market views and stress-testing. The framework elegantly combines Bayesian networks (BNs) and Entropy Pooling (EP). In the new framework, BNs are used to generate a finite set of joint causal views / stress-tests for the...

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term structure and are associated with the time-varying...