زهرا نعمتی

Considering globalization, advertisement translation has turned into an important issue in the field of translation studies, yet it is a rather difficult and challenging job for translators to overcome cultural, social, and ideological differences present in advertisements. With regards to this fact, the present study is designed with the aim of investigating advertisement translation from the perspective of Relevance Theory, an uprising pragmatic theory that provides a unified account of translation. For this purpose, a sample of 10 English advertising slogans, along with their Persian translations were selected from among the slogans available on the Internet. The collected sample was analyzed critically on the grounds of Relevance Theory’s concepts and principles, proposed by Sperber and Wilson (1995). In the process of analyzing the data, Gutt’s (2000) theory of translation, which is based on the application of Relevance Theory to translation, was also brought into focus. Hence, the translators’ choices were justified based on the main tenets of Relevance Theory, and it was observed that in most of the examples, the translations adhered to the principles of Relevance Theory; in Gutt’s terms, the translated slogans interpretively resembled the originals. So, most of the translations were successful advertisements in appealing to the target audience. The findings of the present study gave evidence for the fact that Relevance Theory can be an appropriate approach for evaluating translations, especially in the realm of advertisement translation.

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.