Environmental, Social, and Governance (ESG) scores are quantitative assessments of companies’ commitment to sustainability that have become extremely popular tools in the financial industry. However, transparency in the ESG assessment process is still far from being achieved. In fact there is no full disclosure on how the ratings are computed. As a matter of fact, rating agencies determine ESG ratings (as a function of the E, S and G scores) through proprietary models which public knowledge is limited to what the data provider effectively chooses to disclose, that, in many cases, is restricted only to the main ideas and essential principles of the procedure. The goal of this work is to exploit machine learning techniques to shed light on the ESG ratings issuance process. In particular, we focus on the Refinitiv data provider, widely used both from practitioners and from academics, and we consider white-box and black-box mathematical models to reconstruct the E, S, and G ratings’ assessment model. The results show that it is possible to replicate the underlying assessment process with a satisfying level of accuracy, shedding light on the proprietary models employed by the data provider. However, there is evidence of persisting unlearnable noise that even more complex models cannot eliminate. Finally, we consider some interpretability instruments to identify the most important factors explaining the ESG ratings.

We present a new methodology based on maturity randomization to price discretely monitored arithmetic Asian options when the underlying asset evolves according to a generic Lévy process. Our randomization technique considers the option expiry to be a random variable distributed according to a geometric distribution of a parameter independent of the underlying process. This allows one to transform the pricing backward procedure into a set of independent integral equations. Numerical procedures for a fast and accurate solution of the pricing problem are provided.

Environmental, Social, and Governance (ESG) scores are crucial for evaluating corporate sustainability. However, the undisclosed and complex methodologies used by rating agencies have raised concerns about their reliability and consistency. This study replicates LSEG's ESG scoring methodology using machine learning to shed light on the key drivers behind ESG ratings, with a focus on the balance between forward‐looking promises (aspirational) and past achievements (performance). Our analysis finds that approximately 60% of ESG scores are based on aspirational promises, while only approximately 40% reflect actual performance. This imbalance suggests a potential over‐reliance on future commitments, which could inflate ESG scores and mislead investors about a company's true sustainability efforts, even accounting for LSEG's transparency stimulation mechanism, where non‐disclosure of material data is penalized. The findings emphasize the need for greater transparency and a clearer distinction between aspirational and performance metrics to ensure credible ESG assessments for informed investment decisions.