Financial Ratios

In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

Implementation credit rating for Corporates is influenced by Different circum-stances, systems, processes, and cultures in each country. In this study, we pro-posed a Factor analysis modified approach for determine important factors on real data set of 123 accepted corporate in Tehran Securities Exchange for the years 2009-2017 of diverse range of 52 variable. We estimated the priority score for 49 factors. The three factors, Debt to Equity Ratio, Current debt-to-equity ratio and proprietary ratio exclude due to high correlation with others. The results indicated that three macroeconomic factors: Price Index of Consumer Goods and Services, exchange rate and Interest rate determinants were more effective on the credit ratings. In addition, Financial Ratios and non-Financial Ratios such as Return on equity (ROE), Long-term debt-to-equity ratio, Benefit of the loan, ratio of com-modity to working capital, Current capital turnover, Return on Working Capital, Quick Ratio, Current Ratio, Net Profit margin, Gross profit margin, had effect on credit rating accepted corporate in Tehran Securities Exchange. The Nonparametric statistical test to validate the consistency between AHP ranking and Factor analysis revealed, the new approach has a moderated consistency with AHP. In conclusion, the Factor analysis modified approach could be applied significantly to evaluate efficiency and ranking factors with minimum loss of information.

The purpose of this study is to evaluate the financial performance of provincial gas distribution companies as affiliates for National Iranian Gas Company. To this end, we identified financial performance indicators in accordance with the requirements of NIGC through a review of theoretical foundations and interviews with a number of financial and planning experts and then prioritized using the Analytical Hierarchy Process method. These indicators lied in four groups of liquidity, capital structure, profitability and activity criteria, respectively. Then, the weighted indicators were analyzed using TOPSIS technique in Expert-Choice software and the final ranking of the companies was provided. The results showed that based on the identified criteria, provincial gas companies of Hormozgan, Yazd, Markazi, and Kermanshah had a favorable financial performance and Ilam, Mazandaran, Chaharmahal and Bakhtiari and Zanjan provincial gas companies had a weak financial performance with respect to other companies in three years under review and some suggestions have been made in this regard.

Evaluating the performance of companies using their financial ratios is a challenging task that is expected to become more straightforward by reducing the dimensionality of the data. The purpose of this study is to evaluate the performance of companies using a hybrid model for investment-related decision making through which the mean value of various financial ratios are calculated based on the investor's risk-taking behavior so that the number of all criteria is reduced to one single value for each alternative. To do so, a sample of 172 companies listed in Tehran Stock Ex-change was selected from 2008 to -2018. Firstly, the financial ratios were prioritized using decision trees regression analysis (type CART) and TOPSIS Technique. The results showed that Gross Profit Margin and Debt to Equity Ratio are the most and the least important factors, respectively. Then, using OWA (Ordered Weighted Averaging Aggregation) operator, the role of investor’s risk-taking behavior was investigated, and the results showed that investor’s risk-taking behavior changes the outcome of the decision-making process significantly.

The most important tool for promoting the bank’s stability and health is the establishment of a standard corporate governance structure for managing the bank's business. Redesigning the relationships between bank management, shareholders and the rest of the bank’s stockholder, including the objectives, the risk and audit indices, and internal control of the bank, is recognized as the foundation of corporate governance. Good corporate governance in a bank increases productivity reduces financial risk and enhances systemic sustainability. Bad corporate governance increases the likelihood of a bank's bankruptcy and creates risks that are likely to contagion the entire banking network. In this paper, considering the importance of the corporate governance in the banking network, and issuing Central Bank circular in 2016, we will review corporate governance requirements, as well as quantify its effective indicators. To determine the corporate governance structure, we have introduced and quantified several important indicators about the board structure, internal control, and auditing of the banks. The period for the analysis of corporate governance in the banking network by indicators is 2011 to 2017. This information is extracted from financial statements or through the official website of the bank network. The results confirm that good corporate governance affects financial statement and precautionary ratios in banks.

A stock exchange is an entity which provides ‘‘trading’’ facilities for stock brokers and traders to trade stocks and other securities. How to invest in stock exchange is one of the important issues in investment, and one of the factors that can help investors in the process of investment is the efficiency of the corporation under consideration. Data envelopment analysis is a mathematical methodology that has been widely applied to assess the performance of banks and financial institutes. The main feature of this method is that this methodology evaluate firms by considering multiple inputs and outputs. Conventional DEA models consider each firms as black box and don’t note into the inner activities. Two-stage data envelopment analysis has been researched by a number of authors that evaluate each firm by considering the inner operations. This paper proposes a new two stage BAM model and further evaluates the banks and financial institutes in Tehran stock exchange by considering the financial ratios. Conventional DEA models consider each firms as black box and don’t note into the inner activities. Two-stage data envelopment analysis has been researched by a number of authors that evaluate each firm by considering the inner operations. This paper proposes a new variant of two stage DEA models and further evaluates the banks and financial institutes in Tehran stock exchange by considering the financial ratios.

Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.

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.