With the advent of Sustainable Finance Disclosure Regulation (SFDR), the question of the obsolescence of Socially Responsible Investment (SRI) labels in the fight against greenwashing arises. To address this question, this paper examines the portfolios of French SRI labelled mutual funds at a...

This paper provides a global, transparent and dynamic decision support tool that clusters listed banks depending on their riskiness using an unsupervised learning algorithm. This entirely automatic process is updated weekly on a dedicated website, and refreshable on-demand. A large set of...

Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and...

This paper explores how European SRI funds could go green between 2015 and 2021. For that purpose, we assess and then cluster the green performance of their portfolios using k-means.Overall, our results reveal that greening SRI funds depends on manager experience in SRI and market timing...

We develop refined probability bounds for bank insolvency risk measures based on the Z-score, analogous to those given by Cantelli's inequality under the additional assumption of unimodality of returns, drawing on the one-sided Vysochanskii-Petunin inequality. Illustrating empirically for US...

We analyse the portfolio of European funds, which hold the French SRI label using a Machine Learning approach for clustering given a set of environmental variables.We show that classifying funds in four clusters allows to better capture their heterogeneous level of greenness compared to the...