The main goal of this study was to measure the impact of the environmental, social, and governance (ESG) sustainability score and value added to companies’ market capitalization. Therefore, financial and sustainable performance were measured in a sample of 5557 companies divided into 9 economic sectors of activity from 78 countries and 6 regions (Americas: 2144; Asia: 1770; Europe: 1232; Oceania: 311; Africa: 90; United Kingdom: 10). The analyzed sample consisted of publicly traded companies ranked by market capitalization (from small-cap to large-cap), for which the ESG score was measured in the analyzed period: the financial year was 2019, before the advent of the COVID-19 pandemic. Using two methods (multiple linear regression and complementary quantile regression), we found a direct link between the ESG score and value added variables and market capitalization, with distinct impacts at the economic sector level for ESG score and relatively constant impact for value added.

The problem of tax policy design has been an important concern over the years, involving comprehensive scientific research. In this study, our major goal was to examine and map the optimal taxation research thematic structure by using bibliometric analysis. The analysis was carried out with the CiteSpace software on publications indexed by Web of Science (WoS) between 1975 and 2021. This document offers an actual bibliometric analysis of the current research climate in terms of optimal taxation, based on the following aspects: (1) descriptive characteristics of publication outputs (distribution by years, authors, countries, journals); (2) collaboration analysis of authors, institutions, and countries; (3) co-citation analysis of cited journals, cited authors, and cited references; and (4) keywords’ co-occurrence analysis. We constructed a knowledge map about optimal taxation research to provide a wide visual brief of the actual research in the domain of optimal tax policy. The current study adds knowledge by presenting the state of the art of the most significant studies published in the field of optimal taxation research.

The primary goal of this study was to determine the impact of mergers and acquisitions (M&A) and the environmental, social, and governance (ESG) sustainability scores of companies. In this regard, efforts to measure and analyze the evolution of a company’s performance, taking into account financial and non-financial measures using a score function, are adapted to the pharmaceutical sector. The sample consisted of 100 leading pharmaceutical companies, ranked by stock market capitalization, who registered 30% (n = 492) of the total M&A transactions over the study period (2010–2020). There was a direct and positive link between the M&A process and the evolution of company performance. The ESG score, as an indicator for measuring sustainability, has a positive and direct impact on company performance, indicating that a high ESG score determines an increase in company performance. A similar impact is identified for companies involved in M&A processes, meaning that companies in the pharmaceutical sector tend to register a performance improvement.

The present article proposes a methodology for modeling the evolution of stock market indexes for 2020 using geometric Brownian motion (GBM), but in which drift and diffusion are determined considering two states of economic conjunctures (states of the economy), i.e., non-crisis and financial crisis. Based on this approach, we have found that the GBM proved to be a suitable model for making forecasts of stock market index values, as it describes quite well their future evolution. However, the model proposed by us, modified geometric Brownian motion (mGBM), brings some contributions that better describe the future evolution of stock indexes. Evidence in this regard was provided by analyzing the DAX, S&P 500, and SHANGHAI Composite stock indexes. Throughout the research, it was also found that the entropy of these markets, analyzed in the periods of non-crisis and financial crisis, does not differ significantly for DAX—German Stock Exchange (EU) and S&P 500—New York Stock Exchange (US), and insignificant differences for SHANGHAI Composite—Shanghai Stock Exchange (Asia). Given the fact that there is a direct link between market efficiency and their entropy (high entropy—high efficiency; low entropy—low efficiency), it can be deduced that the analyzed markets are information-efficient in both economic conjunctures, and, in this case, the use of GBM for forecasting is justified, as the prices have a random evolution (random walk).

Built on the premise that a company’s competitiveness is given by its robust financial performance and the strength of its position on the market, but only under conditions of sustainability, this study aims to investigate the key factors of company competitiveness while considering sustainability issues. The two research objectives were: (1) to identify and analyze the key factors of sustainable competitiveness at company level (2) to find sector-related discrepancies regarding the identified factors. To achieve them, a horizontal analysis covering ten financial years (2012–2021) was performed on a sample of 1,449 companies from four global economic sectors (Consumer Cyclicals, Energy, Health Care, and Technology) – using Exploratory factor analysis (EFA) and ANOVA. Nine factors (both financial and non-financial) were found: liquidity, profitability, revenue efficiency, inventory management efficiency, ESG performance, receivables management efficiency, R&D efficiency, book value, and market value. Of them, ESG performance has emerged as an independent factor based on non-financial variables, its introduction into the research model being one of the study’s novelties. Significant mean differences were found between the nine factors, depending on the sector, which allowed the ranking of sectors in terms of sustainable competitiveness, in descending order: Health Care, Technology, Consumer Cyclicals, and Energy.

In recent years, bank-related decision analysis has reflected a relevant research area due to key factors that affect the operating environment of banks. This study’s aim is to develop a model based on the linkages between the performance of banks and their operating context, determined by country risk. For this aim, we propose a multi-analytic methodology using fuzzy analytic network process (fuzzy-ANP) with principal component analysis (PCA) that extends existing mathematical methodologies and decision-making approaches. This method was examined in two studies. The first study focused on determining a model for country risk assessment based on the data extracted from 172 countries. Considering the first study’s scores, the second study established a bank performance model under the assumption of country risk, based on data from 496 banks. Our findings show the importance of country risk as a relevant bank performance dimension for decision makers in establishing efficient strategies with a positive impact on long-term performance. The study offers various contributions. From a mathematic methodology perspective, this research advances an original approach that integrates fuzzy-ANP with PCA, providing a consistent and unbiased framework that overcomes human judgement. From a business and economic analysis perspective, this research establishes novelty based on the performance evaluation of banks considering the operating country’s risk.