درخت حوزه‌های تخصصی

In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

Sustainability consists of fulfilling the needs of current generations without compromising the needs of future generations, while ensuring a balance between economic growth, environmental care and social well-being. Sustainability includes three environmental, economic and social dimensions; whose environmental dimension has a decisive weight in sustainability. Many empirical sustainability-related researches have been carried out in the 2000s and 2010s, and the empirical model used in these studies was based on the Environmental Kuznets Curve (EKC). Also, the two concepts of human development and sustainability have been widely used in development literature in the past years, and humans have been considered as an important factor in the development of different societies in various studies. Composite index of human development includes various dimensions that can affect sustainability and especially environmental sustainability. Also, the influence of large oil revenues in the oil-owning and exporting countries on human development in these countries has played a prominent role, and has categorized the countries into three groups: oil-producing countries with high human development, oil-producing countries with medium human development, and oil-producing countries with low human development. This study has used the panel data method and the Kuznets function in the period of 2010-2019 to investigate the effect of the human development index in 35 major oil exporting countries in the world. According to the result of model estimation by econometric method, environmental sustainability in these countries increases with the increase in the human development index (HDI).