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

Subject Areas : Journal of Investment Knowledge

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

Received: 2021-04-21 Accepted : 2021-05-12 Published : 2024-06-21

Keywords: Risk, Genetic algorithm, Particle Swarm Algorithm, Risk Assessment, grey wolf algorithm,

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.

References:

اردانه، علی (1394). بهینه‌سازی دسترس‌پذیری سیستم‌های سری- موازی با اجزای سه‌حالته تعمیرپذیر. پایان‌نامه کارشناسی ارشد، مؤسسه آموزش عالی رجا، دانشکده فنی و مهندسی.

خوش سیما ر، شهیکی تاش م.( 1391). تاثیر ریسک های اعتباری، عملیاتی و نقدینگی بر کارایی نظام بانکی ایران، فصلنامه برنامه ریزی و بودجه، 4: 16-11.

راعی . رضا.، سعیدی، علی.،  (1388)مبانی مهندسی مالی و مدیریت ریسک"، انتشارات سمت.، ص. 9 5 -58 ،

زارع، احسان و حمیدی، فرهاد و راحتی، امین،1394،الگوریتم گرگ خاکستری بهبودیافته،چهارمین کنگره مشترک سیستم های فازی و هوشمند ایران (پانزدهمین کنفرانس سیستم های فازی و سیزدهمین کنفرانس سیستم های هوشمند)

سرمد ز، بازرگان ع، حجازی ا. 1380. روش‏های تحقیق در علوم رفتاری. چاپ پنجم. تهران: انتشارات آگاه.

سعیدی ع، آقایی آ.  1388.  پیش بینی درماندگی مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران با استفاده از شبکه های بیز بررسی‌های حسابداری و حسابرسی،56: 78-59.

Afshar, M. H. and R. Moeini. 2008. Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems. Water Resources Management, 22(12): 1835– 1857.

Afshar, M. H. and M. Shahidi. 2009. Optimal solution of large-scale reservoir-operation problems: Cellular-automata versus heuristic-search methods. Engineering Optimization, 41(3):275-293.

Angeline, P. J. 1998. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. Evolutionary Programming VII, Lecture Notes in Computer Science, 1447: 601-611.

Fama E. F., (1970), “Efficient capital markets: A review of theory and empirical work”, Journal of finance, 25:2, pp. 283-417 [10] Goldberg, D., (1989), “Genetic Algorithm in search, Optimization and machine Learning”, Addison-Wesely

Huang, X., (2008), “Portfolio selection with new definition of risk”, European journal of Operational research, 186, PP. 351-357

Keller, R. E., Banzhaf, W., Nordin, P., Francone, F.D., (1998), “Genetic Programming–An Introduction; On the Automatic Evolution of Computer Programs and its Applications” Morgan Kaufmann, dpunkt.verlag

Geem Z.W., Kim J-H, Loganathan GV. "Harmony search optimization: Application to pipe network design". Int J Modell Simulat;22(2):125 33, 2002.

Lin, Chang., Lin, Yi, Ting., (2008), “Genetic algorithm for portfolio selection problem with transaction lost”, European journal of operational research., Vol. 185, ISSUE, 1, 16, PP. 393-401

Lin, Chi., Ming, Mitsuo, Gon., (2007), “An effective decision-based genetic algorithm approach to multi objective portfolio optimization problem”, Applied mathematical science, Vol. 1, S, E, PP.1-21

Markowitz, H., M., (1952), “Portfolio Selection”, The Journal of Finance, Vol. 7, 1, PP. 77-91

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

Muro. C   r. R and Spector. L, Escobedo. R from emerge strategies hunting) lupus canis (pack-wolf Behavioural, simulations computational in rules simple .197-192( 2011) 3(88)

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