عباس میرزایی

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.

This study examined the correlation between economic growth and the impact on the environment, specifically focusing on the concept of environmental sustainability. The World Bank's Adjusted Net Savings (ANS) data is utilized in this study to gauge the strain on the environment, specifically through the measurement of natural disinvestment. This measurement encompasses the cumulative effects of carbon dioxide (CO2) damage, as well as depletions in minerals, energy, and forest resources. This study uses panel data with respect to the endogeneity of explanatory variables to estimate the real effect of per capita income and the other variables on environmental pressure. In this regard, employing the panel Fixed-Effects Instrumental Variable (IV) methodology, the data from 213 countries have been used in the period from 1990 to 2018. Through regression analysis, it has been discovered that there is a direct correlation between income and the impact on the environment in developing nations. However, this relationship is notably more pronounced in low-income countries compared to high-income countries. Additionally, the study reveals that trade expansion contributes to an increase in environmental pressure across all groups of countries. An increase in the school enrolment rate can affect the environment in developed and high-income developing countries. Moreover, the variable effect of capital openness on environmental pressure was estimated to be positive for developed and high-income countries. However, this effect was found to be negative for low-income countries. Finally, the result showed that developing countries should improve their legal structure and also reduce the bureaucracy and complexity of the laws.