خوشه‌بندی بانک‌های عضو بازار سرمایه ایران بر اساس ریسک اعتباری و شاخص‌های ﻣﺆثر بر عملکرد مالی با استفاده از رهیافت رگرسیونی بردار پشتیبان

نوع مقاله : علمی

نویسندگان

1 دانشکده اقتصاد دانشگاه مازندران

2 دانشگاه مازندران / گروه علوم اقتصادی

3 دانشیار گروه آمار دانشکده علوم ریاضی و آمار، دانشگاه بیرجند

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

موضوعات


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 Soltaninezhad 1
  • Mohammad Ali Ehsani 2
  • Mohamadghasem Akbari 3
1 Faculty of Economics, Mazandaran University
2 University of Mazandaran
3 Associate 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
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