Financial distress is a research topic in finance that has attracted attention from academia following past financial crises. Although previous studies associate financial distress with several elements, the relationship between distress and ESG has not been broadly explored. This paper investigates these issues by elaborating a Dynamic Network DEA model to address the underlying connections between accounting and financial indicators. Thus, a model that includes profit and loss, balance sheet, and capital and operating expenditures indicators is demonstrated under the dynamic network structure to compute financial-distress efficiency scores. Then, the impact of carryovers is considered for the accurate calculation of efficiency scores for the three substructures. The influence of contextual variables, such as socioeconomic and macroeconomic variables, and whether the firm owns an ESG Risk Score or not, is assessed through a stochastic non-linear model that combines three distinct regression types: Simplex, Tobit, and Beta. The results indicate that firms that hold an ESG Risk Score are less prone to be in financial distress, and Governance Score is negatively associated with financial distress efficiency.

Purpose: This paper investigates the endogenous relationships between banking performance and social welfare in Southern African Development Community (SADC) countries.Design/methodology/approach: A comprehensive three-stage multi-criteria decision-making (MCDM) approach based on alternative informational assumptions is applied.Findings: Results indicate that banking performance is paradoxically associated with stagnant economic activity and higher wealth concentration for the minority. The authors found that SADC banking performance promotes higher Human Development Index (HDI) standards possibly via efficient financial intermediation, dissemination of best managerial practices and other forms of positive spillovers in these countries.Originality/value: This paper contributes to the MCDM literature by simultaneously exploring the key concepts of “utility functions” (using COPRAS) and “distance to ideal solutions” (using TOPSIS) in mapping and explaining the feedback and cause-effect processes between banking performance and social welfare that may exist. Another distinctive aspect is related to the computation of bias-free criteria weights, using a robust SWARA order-rank based on information entropy. Finally, this paper is concerning the endogeneity measurement, using a novel stochastic structural relationship non-linear programme.

This paper describes a new model for portfolio optimization (PO), using entropy and mutual information instead of variance and covariance as measurements of risk. We also compare the performance in and out of sample of the original Markowitz model against the proposed model and against other state of the art shrinkage methods. It was found that ME (mean-entropy) models do not always outperform their MV (mean-variance) and robust counterparts, although presenting an edge in terms of portfolio diversity measures, especially for portfolio weight entropy. It further shows that when increasing return constraints on portfolio optimization, ME models were more stable overall, showing dampened responses in cumulative returns and Sharpe indexes in comparison to MV and robust methods, but concentrated their portfolios more rapidly as they were more evenly spread initially. Finally, the results suggest that it was also shown that, depending on the market, increasing return constraints may have positive or negative impacts on the out-of-sample performance.

Utilizing Multi-Criteria Decision Analysis (MCDA) methods based on environmental, social, and governance (ESG) factors to rank countries according to these criteria aims to evaluate and prioritize countries based on their performance in environmental, social, and governance aspects. The contemporary world is influenced by a multitude of factors, which consequently impact our lives. Various models are devised to assess company performance, with the intention of enhancing quality of life. An exemplary case is the ESG framework, encompassing environmental, social, and governmental dimensions. Implementing this framework is intricate, and many nations are keen on understanding their global ranking and avenues for enhancement. Different statistical and mathematical methods have been employed to represent these rankings. This research endeavors to examine both types of methods to ascertain the one yielding the optimal outcome. The ESG model comprises eleven factors, each contributing to its efficacy. We employ the Performance Contribution Analysis (PCA), Clifford algebra method, and entropy weight technique to rank these factors, aiming to identify the most influential factor in countries’ ESG-based rankings. Based on prioritization results, political stability (PSAV) and the voice of accountability (VA) emerge as pivotal elements. In light of the ESG model and MCDA methods, the following countries exhibit significant societal impact: Sweden, Finland, New Zealand, Luxembourg, Switzerland, Denmark, India, Norway, Canada, Germany, Austria, and Australia. This research contributes in two distinct dimensions, considering the global context and MCDA methods employed. Undoubtedly, a research gap is identified, necessitating the development of a novel model for the comparative evaluation of countries in relation to prior studies.