This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

Chinese multinational enterprises (MNEs) have increasingly engaged in outward foreign direct investment in recent years, and particularly into the infrastructure sector of developing economies. This has been prompted by the infrastructure-led economic integration plan of China’s Belt and Road Initiative. However, such collaboration faces many challenges. Infrastructure projects are often undertaken in industries, countries, and regions posing particular and difficult challenges, and with divergent, often conflicting interests, with the ensuing conclusion that the MNE is simply exploiting the project and not delivering value to the host country. Overall, not only does the infrastructure project have to be well-functioning with expected returns (or savings) realized, but these projects face close scrutiny from local communities, labor, opposition parties, neighboring countries, and various international bodies and nonprofits, requiring delicate handling of the principals involved. The unfolding of these issues and their management by the multinational are examined through an in-depth longitudinal case study. The data are drawn from major participants and stakeholders around a leading Chinese MNE and the mega project of the construction of a major hydropower plant in Pakistan.

This research reviews the environmental, social, and governance (ESG) performance of corporate social responsibility (CSR) and technology innovation development, and analyzes the impact of technology innovation on ESG performance and its influencing mechanism. In additional, the main purpose of this study is to gain an understanding the relationships of ESG performance, CSR and technology innovation in Art industry. We found that technology innovation impact CSR of art firm, and ESG performance with the moderating variable of technology innovation has a significant and positive impact on CSR. Likewise, the study is based on primary panel data collected from 161 consumer, product and service manufacturing companies through an electronic questionnaire (Google, Microsoft online survey) with five-point Likert measurement scale. The exploratory factor analysis is proposed to be carried out using IBM SPSS 27.0 and the confirmatory factor analysis (CFA analysis) is proposed to be carried out using SmartPLS.4.0 analysis software, and this study investigate the measurement factors and the reliability of the construct items and to validate the factorial structure of the research variables. Moreover, digital technology and CSR has the potential to contribute to this impact. Based on these findings, we propose relevant ESG performance recommendations to improve technology innovation and CSR. Our findings offer an excited knowing and learning of the impact of ESG performance, CSR and technology innovation in Chinese art industry. Furthermore, this study extends stakeholders theory and Schumpeter’s Innovation Theory by proving their utility in the perspective of CSR, ESG performance.

Pakistan is a leading emerging market as per the recent classification of the International Monetary Fund (MF), and hedging is used as a considerable apparatus for minimizing a firm’s risk in this market. In these markets, investors are customarily unaware about the hedging activities in firms, due to the occupancy of asymmetric environment prevailing in firms. This research paper adds a new insight and vision to the existing literature in the field of behavioral finance by examining the impact of hedging on investors’ sentiments in the presence of asymmetric information. For organizing this research, 366 non-financial firms are taken up as the size sample; all these firms are registered in the Pakistan Stock Exchange. A two-step system of generalized method of moments (GMM) model is implemented for regulating the study. The findings of empirical evidence exhibit that there is a positive relationship between investors’ sentiments and hedging. Investors’ sentiments are negative in relationship with asymmetric information. Due to the moderate presence of asymmetric information, hedging is positively related to investors’ sentiments although this relation is non-significant.