Evaluation of Effective Criteria for the Desirability of Financial Stability Integration Based on the Comparison of Metaheuristic Algorithms: A Case Study of Banks Listed on the Tehran Stock Exchange

Autor: Zahra Jafari, Rahim Bonabi Ghadim, Rasoul Abdi
Jazyk: perština
Rok vydání: 2024
Předmět:
Zdroj: مطالعات تجربی حسابداری مالی, Vol 21, Iss 81, Pp 179-225 (2024)
Druh dokumentu: article
ISSN: 2821-0166
2538-2519
89560337
DOI: 10.22054/qjma.2024.78048.2541
Popis: The purpose of this research is the evaluation of effective criteria for the desirability of financial stability integration based on the comparison of metaheuristic algorithms in banks listed on the Tehran Stock Exchange. Initially, through a systematic content screening process, the effective criteria for the desirability of financial stability integration are used to evaluate banks listed on the Tehran Stock Exchange. Then, relying on two algorithms of Particle Swarm Optimization and Gray Wolf, the study reveals that both innovative algorithms used in this study have the necessary capability to determine the desirability of the financial stability of banks listed on the Tehran Stock Exchange.Metaheuristic Algorithms, Financial Stability Integration, The Desirability of Banks' Efficiency. IntroductionOne of the most important changes in the economic systems of societies is the increasing focus on the functions of financial stability in the banking systems of countries, which has been increasingly taken into account in macroeconomic policies. It is important to note that, due to reasons such as international sanctions, the banking system in developing countries faces many challenges, including disruptions in the banking system and financial exchanges as a result of the reduced foreign trade. This can lead to increased financial costs and risks, reduce public trust in the banking system, diminish international interactions with foreign banks, and disrupt the economic balance. The purpose of this research is the evaluation of effective criteria for the desirability of financial stability integration based on the comparison of metaheuristic algorithms in banks listed on the Tehran Stock Exchange. Literature ReviewFinancial stability in the banking system is defined as a low level of vulnerability to possible risks, which creates a level of balance and stability in banking systems through the ability to resist economic challenges. Elsa et al. (2018) also considered the financial stability of banks as a basis for economic growth functions in a definition and stated that a dynamic banking system needs to control the risks and costs of commercial transactions in a balanced economy to achieve stable financial stability. On the other hand, Verma and Chakarwarty (2023) suggested that if financial stability does not govern the banking systems of countries and they do not have the necessary efficiency, the optimal direction of resources to industries faces a serious challenge, and this issue can affect the country's economic growth in a short period. MethodologyThis study employs a combined and applied methodology. Initially, through a systematic content screening process, the effective criteria for the desirability of financial stability integration are used to evaluate banks listed on the Tehran Stock Exchange. Then, relying on the two algorithms of Particle Swarm Optimization and Gray Wolf and extracting data related to the criteria identified between 2017 and 2018, efforts are made to determine the optimal point of desirability of financial stability integration for banks listed on the Tehran Stock Exchange. In this process, based on the expansion of the mathematical equations of each metaheuristic algorithm and the command codes of the MATLAB software, necessary actions are taken to answer the research questions. ResultThe results showed that both innovative algorithms used in this study have the necessary capability to determine the desirability of the financial stability of banks listed on the Tehran Stock Exchange. However, based on the Wilcoxon Signed-Rank Test coefficients, the Gray Wolf algorithm is more accurate than the Particle Swarm Optimization algorithm for predicting the function of the identified criteria in determining the desirability of financial stability of banks listed on the Tehran Stock Exchange. The results after executing command processes in MATLAB software indicated that both algorithms have the necessary capability to determine the desirability of the financial stability of banks admitted to the Tehran Stock Exchange. However, based on the coefficients of the Wilcoxon test, the Gray Wolf algorithm has a higher accuracy than the Particle Swarm Optimization algorithm for predicting the performance of the identified criteria in determining the desirability of the financial stability of accepted banks. It is also found that the most effective criterion in strengthening the determination of the desirability of financial stability of banks is the liquidity circulation "ϑ3" in the Gray Wolf algorithm. DiscussionIt is also found that the most effective criterion in strengthening the determination of the desirability of banks' financial stability is the Turnover Ratio in the Gray Wolf algorithm. The coefficients obtained in the Gray Wolf algorithm indicate a more effective optimization of effective criteria in determining the financial desirability of the country's banking system. This issue provides an explanation for the interpretation that banks can benefit from this algorithm for financial planning and covering their weaknesses in preserving resources even in the risky conditions of today's economy. ConclusionThe results show that the banks whose total value of transactions in the capital market is higher than the average value of their total shares over a certain period have higher capacities for liquidity circulation. Furthermore, in providing banking services in current and investment matters in competitive projects, these banks have the upper hand compared to other banks. The existence of such added value of shares in the capital market can be considered as contributing to higher returns and lower risk for investing in these banks. Therefore, as the basics of determining the comparative evaluation between algorithms, i.e., the constant return to scale (CRS) and the variable return to scale (VRS) showed banks with higher liquidity circulation and relying on the Gray Wolf algorithm reach the optimal point faster. This finding illustrates the flexibility of financial resources in timely allocation to the market and industries, which can bring higher returns for their shareholders in the long run.
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