This paper investigates the relationship between the ESG score and market values. Specifically, we test the moderating role of CSR committee defined as organizational subcommittees of boards of directors that make social and environmental recommendations to the boards of directors and support members in their CSR‐related tasks. We built a panel data set with all the listed companies in STOXX Europe 600, covering the period 2014–2020. Firms' data come from Refinitiv Eikon database which contains financial and ESG scores data of all EU listed companies. Our sample of firm‐level data contains a dataset of 600 European listed companies which are part of the STOXX Europe 600 Index. We included ESG data of STOXX Europe 600 Index components in the period 2014–2020. Our dataset contains a total of 4800 firm‐year observations. We found a negative relationship between ESG score and stock prices while the presence of CSR committee as moderating variable generates no significant evidence of ESG score. The presence of CSR committee is not considerably supporting ESG in achieving higher market performance. The CSR committee plays an essential role in monitoring management activities. This may support management practitioners in better understanding and reacting to stakeholder expectations.

This study aims to examine the effect of the COVID-19 pandemic on environmental, social, and governance (ESG) performance for European listed companies. The purpose of this study is to understand if and how the COVID-19 pandemic outbreak influenced the behavior of European companies in terms of best practices in ESG. In this paper, we consider the ESG score as a proxy of management practices. The ESG score was collected for all companies included in the STOXX 600 index (from the Refinitiv Eikon database) and analyzed using fixed and random effects. The sample is composed of 600 European listed companies and covers the period from 2018 to 2021. The results show that even in a health crisis with economic repercussions for the whole world, companies have continued to increase their commitment to ESG targets. The results are robust, also considering the different components of the ESG score (environment, social, governance) individually. This paper validates the significance for companies to improve their ESG performance even during unstable times. Our analysis has implications from several perspectives, adding supplementary information and considerations to the uncompleted debate examining the effects of external shocks on ESG performance.

Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced machine learning techniques. This study aims to develop a model that achieves state-of-the-art accuracy while being fully interpretable, overcoming the limitations of previous research. The model demonstrates excellent predictive performance on par with black-box approaches while maintaining complete transparency by leveraging Explainable Boosting Machines, an intrinsically interpretable tree-based ensemble method, and hyperparameter optimization. The approach automatically considers all possible interactions and uncovers relevant aspects not considered in past studies. This line of research provides compelling results that can bring new insights to the literature on corporate crisis prediction. The interpretable nature of the model is a key advancement, enabling practical application and a deeper understanding of the factors driving corporate financial distress.

Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced statistical learning techniques. The study shows that using tree-based methods and hyper-parameters optimization leads to excellent results in terms of accuracy. Moreover, this approach allows us to automatically consider all possible interactions and discover relevant aspects never considered in past studies. This line of research provides fascinating results that can bring new knowledge into the reference literature.