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.

The implementation of an effective tax system requires specific conditions, among the most critical being equity and efficiency. Taxation on income must align with the principle of the ability to pay; therefore, governments strive to set tax rates appropriately and impactfully. An disproportionate increase in income tax rates can lead to significant social repercussions on income distribution and public welfare. Consequently, calculating the optimal tax rate to maximize social welfare is deemed essential. This paper models the strategic interaction between taxpayers and the tax authority to achieve an optimal tax rate and identifies the factors influencing this rate. By designing a game among taxpayers, assumed to be uniformly distributed within the interval [0, 1], and evaluating the revenue generation of the tax authority, the paper delves into the game formulation and analyses the results. Findings indicate that the optimal tax rate exhibits an inverse relationship with the assessment rates set by investigating groups. Additionally, under specific conditions, the optimal performance tax rate has been determined to be 18%.

This study aims at investigating the effects of the change in the information environment from risk to ambiguity, disclosure dynamics, and the ambiguity aversion behavior of investors on the disclosure of integrated information for selected corporates operating in digital industry listed on the Tehran Stock Exchange during 2012-2022. The corporate voluntary disclosure lag, stock return volatility, and the probability of favorable returns incidence represent respectively the disclosure dynamics, firm risk, and investors' ambiguity aversion, within dynamic panel models. To investigate robustness of the estimates we have also included capital market ambiguity, firm size and stock liquidity indices in the model. The findings are consistent with existing theories. Firm risk and the capital market ambiguity have an increasing impact on the integrated information voluntary disclosure. However, the ambiguity at firm's level has decreased its voluntary disclosure. Likewise, a rise in ambiguity aversion (in-dependent of the ambiguity level), which makes investors more pessimistic about the firm's cash flows, makes the manager to increase the level of voluntary disclosure. Further, inertia in voluntary disclosure has been detected among the studied digital corporates. Depending on the magnitudes of the risk, ambiguity, ambiguity aversion, and the source of investors' ambiguity, the corporate managers decide on their voluntary disclosure policy appropriately.