پیوندهای مفید
آمار
| تعداد نشریات | 31 |
| تعداد شماره‌ها | 485 |
| تعداد مقالات | 4,727 |
| تعداد مشاهده مقاله | 7,361,029 |
| تعداد دریافت فایل اصل مقاله | 5,497,357 |
خوشه‌بندی بانکهای عضو بازار سرمایه ایران بر اساس ریسک اعتباری و شاخص‌های ﻣﺆثر بر عملکرد مالی با استفاده از رهیافت رگرسیونی بردار پشتیبان | ||
| پژوهشنامه اقتصاد کلان Macroeconomics Research Letter | ||
| مقاله 4، دوره 19، شماره 43، آبان 1403، صفحه 87-123 اصل مقاله (1.12 M) | ||
| نوع مقاله: علمی | ||
| شناسه دیجیتال (DOI): 10.22080/mrl.2024.27753.2115 | ||
| نویسندگان | ||
| حامد سلطانی‌نژاد* 1؛ محمد علی احسانی2؛ محمدقاسم اکبری3 | ||
| 1دانشکده اقتصاد دانشگاه مازندران | ||
| 2دانشگاه مازندران / گروه علوم اقتصادی | ||
| 3دانشیار گروه آمار دانشکده علوم ریاضی و آمار، دانشگاه بیرجند | ||
| تاریخ دریافت: 28 شهریور 1403، تاریخ بازنگری: 07 آذر 1403، تاریخ پذیرش: 05 دی 1403 | ||
| چکیده | ||
| نهادهای پولی و مالی نقش اساسی در توسعه اقتصادی هر کشور بر عهده دارند. نظام‌های مالی می‌توانند با تمرکز منابع و وجوه محدود برای سرمایه‌گذاری‌های عظیم، یک اقتصاد را بهره‌ورتر کنند. بررسی عملکرد مالی بانک و ایجاد یک سیستم خوشه‌بندی مناسب بر اساس شاخص‌های ﻣﺆثر بر عملکرد مالی بانک‌ها، برای ناظرین بانکی، سپرده‌گذاران و سهامداران بانک‌ها و سیاستگذاران حوزه بانکی دارای اهمیت زیادی است. در پژوهش حاضر، خوشه‌بندی بانکهای عضو بازار سرمایه ایران بر اساس ریسک اعتباری و شاخص‌های ﻣﺆثر بر عملکرد مالی با استفاده از داده‌های دوره زمانی 1388 تا 1400 مربوط به یازده بانک منتخب بازار سرمایه ایران و مدل رگرسیون بردار پشتیبان(SVR) انجام شده است. دو معیار بازده دارایی‌ها(ROA) و بازده حقوق صاحبان سهام(ROE) به عنوان شاخص‌های عملکرد مالی بانک پیاده‌سازی و مورد بررسی قرار گرفته‌اند. بدین منظور ابتدا ضرایب بانک‌ها با استفاده از مدل رگرسیون بردار پشتیبان استخراج و سپس با این ضرایب به خوشه‌بندی آن‌ها با استفاده از روش میانگین همسایگی‌ها پرداخته‌شده است. نتایج حاکی از آن است که خوشه‌بندی بانک‌ها با استفاده از هر دو معیار عملکرد مالی مشابه می‌باشد. بر این اساس در خوشه‌بندی با سه خوشه بانکهای صادرات، ملت، پارسیان، پست‌بانک، پاسارگاد، سینا، سامان، اقتصادنوین و کارآفرین در یک خوشه و بانکهای تجارت و سرمایه در خوشه‌های دیگر قرارگرفته‌اند. در خوشه‌بندی با چهار خوشه بانکهای صادرات، ملت، پارسیان، پاسارگاد، سینا، سامان، اقتصادنوین و کارآفرین در یک خوشه و بانکهای پست‌بانک، تجارت و سرمایه در خوشه‌های دیگر قرارگرفته‌اند. در خوشه‌بندی با پنج خوشه با توجه به داده‌های بانکهای مورد بررسی، بانکهای صادرات، ملت، پارسیان، سینا، سامان و اقتصادنوین در یک خوشه(دسته)، بانکهای پاسارگاد و کارآفرین در خوشه دوم و بانکهای پست‌بانک، سرمایه و تجارت در خوشه‌های دیگر قرارگرفته‌اند. | ||
| کلیدواژه‌ها | ||
| بانک؛ بردارپشتیبان؛ رگرسیون بردارپشتیبان؛ خوشه‌بندی | ||
| عنوان مقاله [English] | ||
| Clustering of member banks of Iran's capital market based on credit risk and indicators affecting financial performance using the support vector regression approach | ||
| نویسندگان [English] | ||
| Hamed Soltaninezhad1؛ Mohammad Ali Ehsani2؛ Mohamadghasem Akbari3 | ||
| 1Faculty of Economics, Mazandaran University | ||
| 2University of Mazandaran | ||
| 3Associate Professor, Department of Statistics, Faculty of Mathematical Sciences and Statistics | ||
| چکیده [English] | ||
| Monetary and financial institutions play an essential role in the economic development of any country. Financial systems can make an economy more productive by concentrating scarce resources and funds for massive investments. Examining the bank's financial performance and creating a suitable clustering system based on indicators that affect the financial performance of banks is of great importance for bank supervisors, bank depositors and shareholders, and banking sector policymakers. In the current research, the clustering of the member banks of Iran's capital market is done based on credit risk and indicators affecting financial performance using data from the period of 2009 to 2021 related to eleven selected banks of Iran's capital market and the support vector regression (SVR) model. Two measures of return on assets (ROA) and return on equity (ROE) have been implemented and analyzed as indicators of the bank's financial performance. For this purpose, the coefficients of the banks were first extracted using the support vector regression model and then clustered with these coefficients using the average linkage method. The results indicate that the clustering of banks using both financial performance measures is similar. Based on this, in the clustering with three clusters, Saderat, Mellat, Parsian, Postbank, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are placed in one cluster and trade and capital banks are placed in other clusters. In the clustering with four clusters, Saderat, Mellat, Parsian, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are in one cluster and Postbank, Trade and Capital banks are in other clusters. In the clustering with five clusters according to the data of the examined banks, Saderat, Mellat, Parsian, Sina, Saman and Ekhtaznovin banks are in one cluster (category), Pasargad and Karabehan banks are in the second cluster, and Postbank, Capital and Tejarat banks are in other clusters. | ||
| کلیدواژه‌ها [English] | ||
| Bank, support vector, support vector regression, clustering | ||
| مراجع | ||
|
Afriyie, H. O., & Akotey, J. O. (2012). Credit risk management and profitability of selected rural banks in Ghana. Ghana: Catholic University College of Ghana, 7(4), 176-181. Ahmadi, A., Ahmadijashfeghani, H., & Abolhahsanihastiyani, A. (2016). The effect of credit risk on the performance of the Iranian banking system: an interbank study with the PANEL VAR approach. Financial Economics,34(1),131-152.( In Persian) Asadolahi, M., Akbari, M. G., Hesamian, G., & Arefi, M. (2021). A robust support vector regression with exact predictors and fuzzy responses. International Journal of Approximate Reasoning, 132, 206-225. https://doi.org/10.1016/j.ijar.2021.02.006. Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of international financial Markets, Institutions and Money, 18(2), 121-136. https://doi.org/10.1016/j.intfin.2006.07.001. Ayadi, N., & Boujelbene, Y. (2012). The determinants of the profitability of the Tunisian deposit banks. IBIMA Business Review, 2012, 1. https://doi.org/ 10.5171/2012.165418. Basel,J.A., & Oudat, M. S. (2020). Financial risk and the financial performance in listed commercial and investment banks in Bahrain bourse. International Journal of Innovation, Creativity and Change, 13(12), 160-180. Bekhradinasab, V. (2021). The impact of loan portfolio quality on financial performance considering the moderating role of financial health. Scientific Journal of Strategic Budget and Finance Research, 1(4), 143-177.( In Persian) Berger, A. N., & Bouwman, C. H. (2013). How does capital affect bank performance during financial crises?. Journal of financial economics, 109(1), 146-176. https://doi.org/10.1016/j.jfineco.2013.02.008. Bikker, J. A., & Hu, H. (2002). Cyclical patterns in profits, provisioning and lending of banks and procyclicality of the new Basel capital requirements. PSL Quarterly Review, 55(221). https://doi.org/10.13133/2037-3643/9907. Bourke, P. (1989). Concentration and other determinants of bank profitability in Europe, North America and Australia. Journal of banking & Finance, 13(1), 65-79. https://doi.org/10.1016/0378-4266(89)90020-4.
Boffey, R., & Robson, G. N. (1995). Bank credit risk management. Managerial Finance, 21(1), 66-78. https://doi.org/10.1108/eb018497. Chuang, C. C. (2007). Fuzzy weighted support vector regression with a fuzzy partition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(3), 630-640. https://doi.org/10.1109/TSMCB.2006.889611 Dabagh, R., & Jam, M.(2022). Study of factors affecting the performance of banks admitted to the Tehran Stock Exchange using a hybrid approach DEMATEL-ANP. Development and Capital Magazine of Shahid Bahonar University of Kerman,7(1),135-155.( In Persian) Demirguc-Kunt, A., & Huizinga, H. (1998). Determinants of commercial bank interest margins and profitability: some international evidence (No. 1900). The World Bank. Ekinci, R., & Poyraz, G. (2019). The effect of credit risk on financial performance of deposit banks in Turkey. Procedia computer science, 158, 979-987. https://doi.org/10.1016/j.procs.2019.09.139. Esfandyari, M., Eskandari, Z., & Dahmardeh, N.(2023). Studying the impact of macroeconomic shocks on the performance of the countrys banking system; An application of the computable dynamic regression model(RDCGE). Quarterly Journal of Monetary and Financial Economics,29(2),1-32.( In Persian) Farahani,T., & Sabori, M. (2020). The effect of capital adequacy, capital structure and liquidity on the financial performance of banks admitted to the stock exchange.Scientific. Quarterly Journal of Islamic Economics and Banking,31(2),247-262.( In Persian) Frank, U., & Ukpong, E. (2024). Effect of risk management committee attributes on financial performance of listed deposit money banks in Nigeria. International Journal of Advances in Management and Economics, 13, 53-69. Hong, D. H. & Hwang, C. (2003). Support vector fuzzy regression machines. Fuzzy sets and systems, 138, 271-281. https://doi.org/10.1016/S0165-0114(02)00514-6 Izenman, A. J. (2008). Modern multivariate statistical techniques (Vol. 1). New York: Springer. Johnson, R.A., & Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. Pearson Education, Inc., Six Edition.
Koch, T. W., & Macdonald, S. S. Bank Management, South-Western Cengage Learning, 2014.
Lawrence, B., Doorasamy, M., & Sarpong, P. (2024). The Impact of Credit Risk on Performance: A Case of South African Commercial Banks. Global Business Review, 25(2_suppl), S151-S164. https://doi.org/10.1177/0972150920969927.
Levine, R. (2000). Bank-based or market-based financial systems: which is better?. University of Minnesota, mimeo.
Mehrabanpour, M., Naderinoorayni, M.A., Enanlo,E., & Ashari, E. (2017). Factors affecting the profitability of banks. Quarterly Scientific Journal of Financial Accounting Experimental Studies,54(2),113-134.( In Persian) Mesakar, S. S., & Chaudhari, M. S. (2012). Review paper on data clustering of categorical data. International Journal of Engineering Research & Technology (IJERT), 1(10), 1-18. Nasiriaghdam, A., Mohageri, P., & Afrozkalardeh, A. (2019). Investigating the effect of the type of ownership on the performance of the banking system; A case study of Iranian banks. Quarterly journal of economic research and policies,89(27),269-298.( In Persian)
Oudat, M. S., Ali, B. J., Abdelhay, S., Hazaimeh, H. M., Altalay, M. S. R., Marie, A., & El-Bannany, M. (2024). The effect of financial risks on the performance of Islamic and commercial banks in UAE. Frontiers in Applied Mathematics and Statistics, 9, 1250227. https://doi.org/10.3389/fams.2023.1250227 Pradhan, R. P., Arvin, M. B., Hall, J. H., & Bahmani, S. (2014). Causal nexus between economic growth, banking sector development, stock market development, and other macroeconomic variables: The case of ASEAN countries. Review of Financial Economics, 23(4), 155-173. https://doi.org/10.1016/j.rfe.2014.07.002 Pourakbari,V., Hesari, M.R., & Nazembokayee, M. (2014,October,26). Clustering of Iran's banks using GAFC, FCM and k-means fuzzy genetics and discovering the best method for clustering according to banks' performance indicators. International Conference on Accounting, Economics and Financial Management.( In Persian) Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European journal of operational research, 201(3), 873-881. https://doi.org/10.1016/j.ejor.2009.03.032 Rahimi, G. (2014). Performance evaluation and continuous improvement of the organization. Tadbir magazine,173,36-44.( In Persian) Rahimi, R., & Baghestani, A.K.(2023). Investigating the effect of inflation and stock exchange index on bank profitability (case study of Eghtesadenovin , karafrin and Saman banks). Financial Economics Quarterly,17(65),107-120.( In Persian) Salahshor, R. (2010). Pathology of credits and banking risk. Industry and entrepreneurship,49,68-70. (In Persian) Shahchera, M., & Abdfathi, F.(2016). Investigating factors affecting the quality and assets of banks in the country's banking network. Al-Zahra University economic development policy quarterly,3,151-181.( In Persian) Shahreza, A. , Faghanimakrani, K.H., & Fazeli, N. (2021). Comprehensive study of factors affecting the profitability of commercial banks. Quarterly Scientific Research Journal of Investment Knowledge,14(1),641-661.( In Persian) Shavalpour, S., & Ashari, E. (2013). Investigating the effect of credit risk on banks profitability. Financial research,15(2),229-246.( In Persian) Shleifer, A. (1998). State versus private ownership. Journal of economic perspectives, 12(4), 133-150. Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. The journal of finance, 52(2), 737-783. https://doi.org/10.1111/j.1540-6261.1997.tb04820.x Stuart, T. (2005). New Players, New Landscape. The Banker, Special Supplement, Financial Times.Supervision, B. (2011). Supervision, B. (2011). Basel committee on banking supervision. Principles for Sound Liquidity Risk Management and Supervision (September 2008). Suykens, J. A., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1-4), 85-105.https://doi.org/10.1016/S0925-2312(01)00644-0 Tamizi, A. (2024). Economic and banking factors affecting the financial performance of banks. Scientific Quarterly Journal of Islamic Economics and Banking, 46(1), 279-299.( In Persian) Temba, G. I., Kasoga, P. S., & Keregero, C. M. (2024). Impact of the quality of credit risk management practices on financial performance of commercial banks in Tanzania. SN Business & Economics, 4(3), 38. Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.. Wieszczy, P., & Grzegorzewski, P. (2016). Support vector machines in fuzzy regression. Challenging Problems and Solutions in Intelligent Systems, 103-138. | ||
|
آمار تعداد مشاهده مقاله: 195 تعداد دریافت فایل اصل مقاله: 94 |
||