درخت حوزه‌های تخصصی

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.

In international trade and financial exchanges, global cryptocurrencies have a prominent and effective role. In this paper, using the KOF index, the structure of financial globalization is extracted and the effect of the expansion of global cryptocurrencies is examined in the two components: Foreign Direct Investment and Portfolio Investment. The Ordinary Least Squares (OLS) model has been used separately for 2020, 2021, and 2022 to analyze the results of the expansion of global cryptocurrencies in the foreign direct investment component. Also, the price changes of Bitcoin and Ethereum from March 10, 2016, to the end of December 2022 have been used to investigate the effect of global cryptocurrencies in portfolio investment by applying Modern Portfolio Theory (MPT). Also, according to the availability of data in research sources, the data of 111 countries have been used. OLS estimated results suggest that the adoption and expansion of global cryptocurrencies has no significant relationship with Foreign Direct Investment. Also, using MPT, the results of portfolio optimization suggest that global cryptocurrencies improve the effectiveness of the selected portfolios, and with the same corresponding returns, the risk of the portfolios including global cryptocurrencies decreases as well. Therefore, the results emphasize the role of global cryptocurrencies in financial globalization only as crypto-assets.