We present a model that captures risks of hedge funds only using their historical performance as input. This statistical model is a multivariate distribution where the marginals derive from an AR(1)/AGARCH(1,1) process with t_5 innovations, and the dependency is a grouped-t copula. The process...

We analyze the performance of investable portfolios built using predicted stock returns from machine learning methods and attribute their performance to linear, marginal non-linear and interaction effects. We use a large set of features including price-based, fundamental-based, and...

Recently many research articles have focused on the prediction of stock returns using machine learning methods. All show that regression trees and neural networks have superior predicting power than linear models. In this paper we analyze the performance of investable portfolios built using...

We describe a model that takes into account the tail dependence present in a large set of historical risk factor data using the modern concept of copulas. We extend the popular t-copula to obtain a new grouped t-copula which describes more accurately the dependence among risk factors of...

What are the implications of disagreement about environmental, social, and governance (ESG) ratings for portfolio choice? Constructing an optimal portfolio for each ESG rating agency by minimizing tracking error while satisfying a portfolio-level ESG constraint, we found that ESG rating...