Corporate governance and financial statement fraud: Evidence on the moderating influence of financial distress

Environmental, social, and governance (ESG) disclosure becomes vital for banks to be transparent and accountable for their investments and lending decisions to shareholders, regulators, and society. The potential enhancement of shareholder value through ESG disclosure is still inconsistent. Empirical studies on the association between ESG disclosure and financial performance are mixed and limited in emerging economies. This study aims to examine whether ESG disclosure impacts the financial performance of 24 Vietnamese commercial banks in terms of return on assets (ROA), return on equity (ROE), and net interest margin (NIM). The study uses the feasible generalized least squares estimation method based on panel data from 2018 to 2022. The study employs content analysis on 12 themes related to environmental, social, and governance pillars to score policy disclosure based on the Fair Finance Guide Methodology. The results highlight the positive effects of ESG policy disclosure, individual environment disclosure (E), and individual governance disclosure (G) on bank financial performance. Notably, ESG, E, and G have the largest influence on ROE, with coefficients of 0.051, 0.036, and 0.027, respectively, at a 5% significance level. However, the study does not provide evidence of a statistically significant association between social disclosure and financial performance. These results provide empirical evidence for regulators and bank managers to shape ESG policies and practices aligning with international standards.

Acknowledgment
ESG disclosure score of 11 banks as primary data in this study is conducted under the project coordinated by the Fair Finance Vietnam coalition, as part of Fair Finance International.

This study applied Altman Z-score model to assess the bankruptcy risk of a set of multidisciplinary enterprises of various types, mainly small and medium enterprises, with data taken from official financial reports of 180 enterprises in Soc Trang province. The binary logistic regression was employed to assess the impact of non-financial and financial factors on the bankruptcy risk of enterprises. The research findings showed that both the non-financial factors such as business area, types and size of the business, the educational level of managers and executors and other characteristics, and the financial factors (indicators) such as earnings before tax, net profit/equity ratio, earnings before interest and tax/total assets ratio, equity/total debt ratio, affect the bankruptcy risk of enterprises. Predicting the bankruptcy risk and measuring its determinants play an important role not only as an effective managing tool of the business, but also as evidence for policymakers to support the sustainable development of business.

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.
The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.
Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries’ positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.