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...

Our aim in this article is to predict local airline market shares in the United States at the company level combining traditional economic indicators (at the national and local levels) with Google search engine requests. We resort both to simple econometric models and to more sophisticated...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...

Die vorliegende Arbeit hat zwei wesentliche Ziele. Auf der einen Seite soll sie existierende Konzepte zum systematischen Faktorinvestieren erweitern, mögliche Problemstellungen aufdecken und Verbesserungen vorschlagen. Zweitens, untersucht diese Arbeit eine neue Form von FinTech Innovationen:...

We analyze the tail of the sum of two random variables when the dependence structure is driven by the Bernstein family of copulas. We consider exponential and Pareto distributions as marginals. We show that the first term in the asymptotic behavior of the sum is not driven by the dependence...