درخت حوزه‌های تخصصی

In the real world, situations frequently occur when we want to allocate a fixed cost between a set of decision-making units (DMUs) such as institutions, organizations. In this paper, we use the data envelopment analysis (DEA) technique to allocate fixed costs among DMUs. First, we introduce semi-additive production technology in DEA and present efficiency evaluation models in this technology. In estimating the frontier of this technology, in addition to the observed DMUs, the set of all aggregations of these DMUs are also used. In the following, we propose an interactive process for fixed cost allocation between DMUs in DEA based on the concept of cross-efficiency. We show that our proposed iterative approach is always feasible, and ensures that all DMUs become efficient after the fixed cost is allocated as an additional input measure. The cross-efficiency scores corresponding to all DMUs are improved at each stage of the interactive process. We also illustrate the proposed approach with a numerical example. The proposed approaches are demonstrated using an application of the fixed cost allocation problem for branches of commercial banks. Finally, we bring the results of the research.

The existence of bubbles in the market, especially the capital market, can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. On the other hand, due to the goal of investors in achieving a portfolio of high returns with the least amount of risk, the need to pay attention to these markets increases. In this research, with the aim of maximizing return and minimizing investment risk, an attempt has been made to form an optimal portfolio in conditions where the capital market has a price bubble. According to the purpose, the research is of applied type, and in terms of data, quantitative and post-event, and in terms of type of analysis, it is of descriptive-correlation type. In order to identify the months with bubbles in the period from 2015 to 2021 in the Tehran Stock Exchange market, sequence tests and skewness and kurtosis tests were used. After identifying periods with bubbles, the meta-heuristic algorithms were used to optimize the portfolio. The results indicate the identification of 14 periods with price bubbles in the period under study. Also, in portfolio optimization, selected stock portfolios with maximum returns and minimum risk are formed. This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

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.