usefulness of meta-heuristic algorithms on optimizing of the integrated risk in banking system

Document Type : Original Article

Authors
eskandar vaziri 1
Farhad dehdar 2
Mohamad reza abdoli 3
1 students of phd، accounting، Islamic azad university of shahroud
2 Assistance Professor of accounting، islamic azad university of shahroud
3 Associate Professor of accounting، islamic azad university of shahroud
Abstract
aim of this study was to evaluate the integrated risk of the banking system through the metaphysical algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection has been done through library studies, articles and sites in deductive form and data collection to refute and confirm hypotheses inductively. The statistical population of this research is the banking system and the sample includes banks listed on the Tehran Stock Exchange during the fiscal years 1392 to 1397. In order to collect the required data, the financial database of the Ministry of Economic Affairs and Finance, codal site, etc. have been used. After extracting the information, and setting them in the form of an integrated risk model, the objective function and constraints are entered in MATLAB software and the variables of risk and return (profit and loss on assets and Debts) were obtained using particle swarm algorithms, genetics and gray wolves and we compared their results using SPSS 16 software. After that, first the descriptive statistics were analyzed and then inferential statistics were performed. after reviewing the results of comparing the evaluation indicators of algorithms, it was determined that the gray wolf algorithm is efficient. Provides better goal function optimization. Also, by examining the research hypotheses, it was found that particle swarm algorithms and genetics have the same efficiency for assessing the integrated risk of the banking system. Provides better problem solving.
Keywords
risk
risk assessment
grey wolf algorithm
genetic algorithm
particle swarm algorithm

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    1. Mirjalili and S. Z. M Hashim, “A New Hybrid PSOGSA Algorithm for Function Optimization,” International Conference on Computer and Information Application, pp.374-377, 2010