We consider some pricing and risk management issues related to defaultable bonds, in the context of sovereign debt default and restructuring. Standard recovery schemes such as fractional recovery of market value, of Treasury and of face value are investigated: we discuss their consistency with market practice both from a pricing and a risk management perspective. We also pay attention to the tradable basic instruments such as defaultable discount bonds or coupon and principal strips that are the building blocks of traded level coupon bonds. Modelfree pricing formulas are provided and the use of a hypothetical default-free yield curve is challenged. We show that the fractional recovery of par involves two discount curves, one for principal payments and one for coupon payments, a departure from standard bootstrapping and pricing engines. In a second step, this pricing framework can be specialized along the modelling lines routinely used in credit derivatives markets. In light of collective action clauses applicable to the issuance of new bonds in the eurozone and the pricing characteristics of strips, we investigate some practical issues on bond and credit derivatives markets: stripping of bonds in distressed periods, implied market recovery scheme and consistent recovery rates. JEL Classification: G01, G12, G33

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies, and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, countries, or revenue buckets. We also compare the model results to those of other providers and find our estimates to be more accurate. Explainability tools based on Shapley values allow the constructed model to be fully interpretable, the user being able to understand which factors split explains the GHG emissions for each particular company.

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We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market. Using interpretable machine learning, we examine whether ESG scores can explain the part of price returns not accounted for by classic equity factors, especially the market one. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Gradient boosting models successfully explain the part of annual price returns not accounted for by the market factor. We check with benchmark features that ESG data explain significantly better price returns than basic fundamental features alone. The most relevant ESG score encodes controversies. Finally, we find the opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter and reversely for the former.

In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes. In this study, we propose a novel collaborative filtering algorithm that captures the temporal context of a user-item interaction through the users' and items' recent interaction histories to provide dynamic recommendations. The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors. The performance and properties of the algorithm are monitored in a series of experiments on a G10 bond request for quotation proprietary database from BNP Paribas Corporate and Institutional Banking.