The aim of this paper is to study the performance of carbon-based portfolios when all emissions scopes are accounted for. We formalize low-carbon portfolio strategies by integrating a carbon intensity penalty to a constrained mean-variance optimization framework. To do so, we resort to direct...

In this paper we address the problem of robust portfolio allocation under uncertainty with respect to the dependence between risky asset returns and for an ambiguity averse investor. We use the multiplier preferences framework with a penalty for ambiguity aversion that is proportional to the...

We show that the introduction of a leverage constraint improves the practical implementation of characteristics-based portfolios. The addition of the constraint leads to significantly lower transaction costs, to a reduction of negative portfolio weights, and to a decrease in volatility and...

This article aims to enhance factor investing with reinforcement learning (RL) techniques. The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and...

This article aims to enhance factor investing with reinforcement learning (RL) techniques. The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and...