This study examines the role of ESG indicators in predicting financial distress in 362 US and EU-28 commercial banks (2012-2019). It finds that ESG factors improve the accuracy of distress prediction and reduce misclassification of troubled banks. The model, using statistical, machine learning, and ensemble methods, offers practical insights for banking supervisors and contributes to default prediction research.

This paper examines the impact of digitalization on bank performance using a new digitalization index, based on data from 279 European banks (2017-2022). We find that higher IT investments, better network efficiency, and digital channel development enhance profitability. The Covid-19 pandemic, along with a country’s digital infrastructure and expertise, moderates these effects, shaping the profitability of digitalized banks.

We analyze an extension of the maximal correlation risk measure in the set-valued scenario, based on Burgert and Rüschendorf (2006). We introduce stochastic ordering for random vectors using the upper expectation operator (Hamel and Heyde, 2021) and explore its consistency for set-valued risk measures. These measures, like law-invariant set-valued risk measures (e.g., set-valued distortion risk measure from Chen and Hu, 2019), offer flexibility in adhering to portfolio aggregator axioms and proper set-valued risk measure properties.

Ticket availability is crucial for passenger satisfaction and transportation choice, yet the impact of competition on it is underexplored. Our study fills this gap by examining how a new competitor affects ticket availability, particularly regarding price and timing before departure. Using the 2018 entry of NTV (Italo) on Turin-Venice high-speed rail routes as a case, we analyzed ticket availability changes. Our findings, based on survival analysis, show that competition significantly increases the availability of cheaper tickets closer to departure.