نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت مالی، دانشگاه تهران، تهران، ایران.

2 گروه اقتصاد و مالی، دانشگاه تهران، تهران، ایران.

3 گروه مدیریت، دانشگاه میبد، یزد، ایران.

چکیده

هدف: ریسک اعتباری و مطالبات غیر جاری بانک­ها ازجمله مهم­ترین معضلات نظام بانکداری در ایران است و بر اساس آمارهای موجود، میانگین نکول وام­ها و مطالبات غیر جاری بانک­ها در ایران، بسیار بالاتر از متوسط جهانی آن است. هدف اصلی این پژوهش شناسایی و تحلیل عوامل اساسی و مهم مؤثر بر ریسک اعتباری در سیستم بانکی ایران است.
روش‌شناسی پژوهش: در این پژوهش ابتدا با استفاده از روش مصاحبه هدایت‌شده با بیست نفر از کارشناسان و مدیران ریسک اعتباری حوزه بانکی کشور که با روش گلوله برفی انتخاب گردیدند، مهم‌ترین عوامل مؤثر بر ریسک اعتباری شناسایی گردید؛ سپس این عوامل به جهت رتبه‌بندی در قالب یک پرسشنامه مقایسه زوجی مجدداً به خبرگان برگردانده شد و در نهایت عوامل مهم موثر بر این ریسک، به همراه زیر عوامل آن‌ها با استفاده از تکنیک دنپ (دیمتل مبتنی بر فرآیند تحلیل شبکه‌ای) مورد تحلیل قرار گرفت.
یافته‌ها: نتایج حاصل از پژوهش نشان می­دهد که عوامل کلان اقتصادی مهم‌ترین عامل، همچنین بی‌ثباتی در محیط کلان اقتصادی، عدم اقدام قاطع و به‌موقع با نکول کنندگان، پایش اعتباری ضعیف و ناکافی، تسهیلات تکلیفی و طولانی و زمان‌بر بودن رویه‌های قضایی مهم‌ترین زیر عوامل مؤثر بر ریسک اعتباری در نظام بانکداری ایران هستند.
اصالت/ارزش افزوده علمی: وجه تمایز این پژوهش از سایر پژوهش‌های مشابه استفاده از تکنیک دنپ به‌منظور بررسی روابط بین عوامل و زیر عوامل است.




 
 
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating non-performing loans and ranking factors affecting credit risk in Iranian banking using DANP method

نویسندگان [English]

  • Jalal Naderi 1
  • Mohamad Nadiri 2
  • Fatemeh Zarei 3

1 Department of Financial Management, University of Tehran, Tehran, Iran.

2 Department of Economics and Finance, University of Tehran, Tehran, Iran.

3 Department of Management, Meybod University, Yazd, Iran.

چکیده [English]

Purpose: The credit risk and non-performing loans of banks are among the most important problems of the banking system in Iran. And according to available statistics, the average default of loans and non-performing loans of banks in Iran is much higher than the global average. The main purpose of this study is to identify and analyze the basic and important factors affecting credit risk in the Iranian banking system.
Methodology: For this purpose, first, using the method of guided interviews with twenty experts and credit risk managers of the country's banking sector, who were selected by the snowball method, the most important factors affecting credit risk were identified; then, these factors were returned to the experts for ranking in the form of a pairwise comparison questionnaire, and finally, the important factors affecting this risk, along with their sub-factors were analyzed using denp technique (the dematel based analytic network process).
Findings: The results of the research show that the macroeconomic factors are the most important factor as well as instability in macroeconomic environment, failure to take timely and inappropriate actions with defaulters, poor and inadequate credit monitoring, ordered loans, and lengthy and time-consuming judicial procedures are the most important factors affecting risk credit in Iranian banking.
Originality/Value: The distinguishing feature of this study from other similar studies is the use of DANP technique to investigate the relationships between factors and sub-factors.




 

کلیدواژه‌ها [English]

  • Credit risk
  • DANP method
  • Industry specific factors
  • Macroeconomic factors
  • Legal and judicial factors
  • Bank specific factors
Acheampong, A., & Elshandidy, T. (2021). Does soft information determine credit risk? Text-based evidence from European banks. Journal of international financial markets, institutions and money75, 101303. https://doi.org/10.1016/j.intfin.2021.101303
Agnello, L., & Sousa, R. M. (2012). How do banking crises impact on income inequality?.Applied economics letters19(15), 1425-1429.
Amuakwa-Mensah, F., Marbuah, G., & Ani-Asamoah Marbuah, D. (2017). Re-examining the determinants of non-performing loans in Ghana’s banking industry: role of the 2007–2009 financial crisis. Journal of African business18(3), 357-379.
Arpa, M., Giulini, I., Ittner, A., & Pauer, F. (2001). The influence of macroeconomic developments on Austrian banks: implications for banking supervision. Bis papers1, 91-116.
Awang, A., Aizam, N. A. H., & Abdullah, L. (2019). An integrated decision-making method based on neutrosophic numbers for investigating factors of coastal erosion. Symmetry11(3), 328. https://doi.org/10.3390/sym11030328
Bhattacharya, B., & Roy, T. N. S. (2008). Macroeconomic determinants of asset quality of Indian public sector banks: a recursive VAR approach. The IUP journal of bank management7(1), 20-40.
BoruLelissa, T. (2014). Factors influencing the level of credit risk in the Ethiopian commercial banks: the credit risk matrix conceptual framework. European journal of business and management6(23), 21. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.684.4346&rep=rep1&type=pdf
Castro, V. (2013). Macroeconomic determinants of the credit risk in the banking system: the case of the GIPSI. Economic modelling31, 672-683.
Central Bank of Republic Islamic Iran. (2020). Summary of the country's economic changes. (In Persian). Retrieved from https://www.cbi.ir/SimpleList/AnnualReview_fa.aspx
Chen, S. H., & Lin, W. T. (2018). Analyzing determinants for promoting emerging technology through intermediaries by using a DANP-based MCDA framework. Technological forecasting and social change131, 94-110.
Crouhy, M., Galai, D., & Mark, R. (2006). The essentials of risk management (Vol. 1). McGraw-Hill.
Dehghan Dehnavi, M., Moharram Oghli, O., & Baei, M. (2017). Determinants of banks' risk-taking in Iran with emphasis on ownership structure. Financial research journal, 19(1), 61-80. (In Persian). DOI: 10.22059/JFR.2017.206195.1006192
Demirgüç-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. Staff papers45(1), 81-109.
Doumpos, M., Lemonakis, C., Niklis, D., & Zopounidis, C. (2019). Introduction to credit risk modeling and assessment. In analytical techniques in the assessment of credit risk (pp. 1-21). Springer, Cham.
Egger, D. J., Gutiérrez, R. G., Mestre, J. C., & Woerner, S. (2020). Credit risk analysis using quantum computers. IEEE transactions on computers70(12), 2136-2145.
Garr, D. K. (2013). Determinants of credit risk in the banking industry of Ghana. Developing country studies3(11), 64-77.
 
Griffith-Jones, S. & Persaud, A. (2003). The political economy of Basle II and implications for emerging economies. In Seminar on "management of volatility, financial liberalization and growth in emerging conomies". ECLAC Headquarters, Santiago. http://www.stephanygj.net/papers/
THEPOLITICALECONOMYOFBASLEIIANDIMPLICATIONS2003.pdf
Gweyi, M. O. (2013). Credit risk mitigation strategies adopted by commercial banks in Kenya. International journal of business and social science4(6), 71-87.
Hewett, E. A. (2019). Basic issues in US-soviet economic relations. In Economic relations with the Soviet Union (pp. 91-98). Routledge.
Hsu, C. H., Wang, F. K., & Tzeng, G. H. (2012). The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR. Resources, conservation and recycling66, 95-111.
Incekara, A., & Çetinkaya, H. (2019). Credit risk management: a panel data analysis on the Islamic banks in Turkey. Procedia computer science158, 947-954.
Izadikhah, M., Shamsi, M., Sheikhsn, A., & Ghafouri, F. (2022). A new hybrid approach based on fitch ranking and data envelopment analysis to evaluate the credit performance of the bank's legal clients. Journal of decisions and operations research6(4), 553-569. (In Persian). DOI: 10.22105/DMOR.2021.250078.1225
Jahangiri, A. (2019). Application of data envelopment analysis technique in Iran banking system. Journal of decisions and operations research3(4), 368-401. (In Persian). DOI: 10.22105/DMOR.2019.82229
Janati, A., Arbabian, Sh., & Khojasteh, Z. (2016). The impact of macroeconomic determinants on banking stability and risk. Journal of monetary and banking research, 9(29), 487-511. (In Persian). https://ideas.repec.org/a/mbr/jmbres/v9y2016i29p487-511.html
Jiménez, G., & Saurina, J. (2006). Credit cycles, credit risk, and prudential regulationInternational journal of central banking, 65-98. https://www.ijcb.org/journal/ijcb06q2a3.pdf
Khademzadeh, M. (2018). Legal review of the types of crimes. Second international conference on political science law and Islamic education, Tehran, Iran. Civilica. (In Persian). https://civilica.com/doc/869276
Khojasteh, G., Daei Karimzadeh, S., & Sharifi Ranani, H. (2019). Credit risk measurement of trusted customers using logistic regression and neural networks. Journal of system management5(3), 91-104. (In Persian). http://sjsm.iaushiraz.ac.ir/article_668418_ab6cabdfcf13923aebf23e2b09a3ecc5.pdf
Khorrami, A., Taghavifard, M. T., & Khatami Firouzabadi, S. M. A. (2020). Assessment of credit status of bank loan applicants using CBR method. Journal of industrial management studies, 18(59), 79-116. (In Persian). https://www.sid.ir/paper/391098/en
Kil, K., & Miklaszewska, E. (2017). The competitive threats and strategic challenges to polish cooperative banks: a post crisis perspective. In institutional diversity in banking (pp. 121-146). Palgrave Macmillan, Cham.
Koch, T. W., & MacDonald, S. S. (2003). Bank management. United state: Navta Associates.
Kolapo, T. F., Ayeni, R. K., & Oke, M. O. (2012). Credit risk and commercial banks'performance in Nigeria: a panel model approach. Australian journal of business and management research2(2), 31-38.
Labbafi, M., Darabi, R., & Sarraf, F. (2021). Modeling of asset-liability management in Bank Melli Iran under uncertainty: fractional programming model approach. Journal of decisions and operations research5(4), 446-461. (In Persian). DOI: 10.22105/DMOR.2020.255392.1252
Li, T., & Lin, H. (2020). Credit risk and equity returns: an augmented fama-french five-factor model in the Chinese market. Retrieved from https://ssrn.com/abstract=3625609
Manab, N. A., Theng, N. Y., & Md-Rus, R. (2015). The determinants of credit risk in Malaysia. Procedia-social and behavioral sciences172, 301-308.
Martín-Oliver, A., Ruano, S., & Salas-Fumás, V. (2020). How does bank competition affect credit risk? Evidence from loan-level data. Economics letters196, 109524. https://doi.org/10.1016/j.econlet.2020.109524
Modaber, S., Rafei, S., & Agha Mohammadi, Z. (2020). Identifying the factors affecting the possibility of deferral of bank customers' facilities. Journal of research in accounting and economic sciences, 4(14), 43-54. (In Persian). http://ensani.ir/file/download/article/1621229933-10157-14-4.pdf
Naderi, J., Mousavian, S. A., Nadiri, M., & Zarei, F. (2019). Comparative study of credit risk in Islamic banking and conventional banking; with emphasis on the impact of bank specific factors. Journal of Iran's economic essays16(32), 61-87. (In Persian). DOI: 10.30471/IEE.2019.5527.1778
Parab, C. R., & Patil, M. R. (2018). Sensitivity of credit risk to bank specific and macro economic determinants: empirical evidence from Indian banking industry. International journal of management studies5(2), 46-56.
Pestova, A. & Mamonov, M. (2014). Macroeconomic and bank-specific determinants of credit risk: evidence from Russia. EERC Working Paper Series 13/10e. Available at: https://ideas.repec.org/p/eer/wpalle/13-10e.html
Poudel, R. P. S. (2013, June). Macroeconomic determinants of credit risk in Nepalese banking industry. Proceedings of 21st international business research conference (pp. 10-11). Ryerson University, Toronto, Canada. https://www.researchgate.net/publication/282286715_Macroeconomic_Determinants_of_Credit_Risk_in_Nepalese
_Banking_Industry
Rostami, M., Nabizade, A., & Shahi, Z. (2018). Factors affecting credit risk of commercial banks of Iran with emphasis on banking and macroeconomic specific factors. Journal of asset management and financing6(4), 79-92. (In Persian). DOI: 10.22108/AMF.2018.105889.1156
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922, No. 2). RWS Publications.
Saaty, T. L. (2005). Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. RWS Publications.
Salas, V., & Saurina, J. (2002). Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of financial services research22(3), 203-224.
Singh, S. K., Basuki, B., & Setiawan, R. (2021). The effect of non-performing loan on profitability: empirical evidence from Nepalese commercial banks. The journal of Asian finance, economics and business8(4), 709-716.‏
Syamlan, Y. T., & Jannah, W. (2019). The determinant of credit risk in Indonesian Islamic commercial banks. Share: jurnal ekonomi dan keuangan Islam8(2), 181-206.
Waqas, M., Fatima, N., Khan, A., & Arif, M. (2017). Determinants of non-performing loans: a comparative study of Pakistan, India, and Bangladesh. International journal of finance & banking studies6(1), 51-68. https://badge.dimensions.ai/details/doi/10.20525/ijfbs.v6i1.617?domain=http://www.ssbfnet.com
Woo, S. H., Kwon, M. S., & Yuen, K. F. (2021). Financial determinants of credit risk in the logistics and shipping industries. Maritime economics & logistics23(2), 268-290.
Zangane, E., Zamanian, G., Shahiki, M., & Cheshomi, A. (2019). The effect of macroeconomic variables on credit default cycles in the country\'s monetary market. Journal of monetary and banking research12(41), 443-484.‎ (In Persian). http://jmbr.mbri.ac.ir/browse.php?a_code=A-10-1286-3&slc_lang=fa&sid=1
Zhou, P. (2018). The impact of institution factors on commercial banks’ risk in China. Journal of financial risk management7(02), 157-173.
Zolkifli, N. A., Uda, M. A. H., & Binti Janor, H. (2018). Determinants of credit risk in Islamic and conventional bank: evidence from Malaysia. International journal of academic research in business and social sciences8(6), 1054-1068.