نوع مقاله : مقاله پژوهشی - کاربردی

نویسندگان

1 گروه مهندسی مالی، دانشکده مدیریت، واحد علوم تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه ریاضی، دانشکده علوم، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 گروه مدیریت بازرگانی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران.

چکیده

هدف:  کارایی یک مفهوم اقتصادی است که عملکرد طیف گسترده‌ای از فعالیت‌های اقتصادی را در حوزه‌های مختلف یک بخش اقتصادی نشان می‌دهد. در مطالعاتی که از تکنیک مرزی تحلیل پوششی داده‌ها (DEA) استفاده می‌شود، اغلب رابطه‌ی بین برآورد کارایی با شاخص‌های کلیدی عملکرد مورد آزمایش قرار نمی‌گیرد. این درحالی است که DEA از ابزارهای مناسب و کارا در زمینه‌ی سنجش و ارزیابی بهره‌وری است. با این وجود، برآورد رابطه‌ی کارایی با معیارهای عملکرد مالی پذیرفته‌شده، می‌تواند فعالیت‌های الگوبرداری، تصمیمات قیمت‌گذاری و نظارت قانونی را هدایت کند.
روش‌شناسی پژوهش:  در این مقاله فرمول ابرکارایی DEA در دو مدل سودآوری، مورد آزمایش قرار می‌گیرد. چهار نسبت خالص درآمد بهره به کل دارایی‌ها، سود پس از مالیات به کل دارایی‌ها، بازده حقوق صاحبان سهام و وام‌های غیرجاری به کل دارایی‌ها، با مدل سودآوری توسعه‌یافته و همچنین نرخ رشد دارایی‌ها در مدل سودآوری اصلی برپایه‌ی متغیرهای کمکی در تحلیل پوششی داده‌ها محاسبه شده اند.
یافته ها:  در این تحقیق فرمول ابرکارایی DEA در دو مدل سودآوری، برای 15 بانک به مدت دو سال مورد آزمایش قرار می‌گیرد. همبستگی به‌دست‌ آمده به‌طورکلی پایین است. با این حال، چهار نسبت خالص درآمد بهره به کل دارایی‌ها، سود پس از مالیات به کل دارایی‌ها، بازده حقوق صاحبان سهام و وام‌های غیرجاری به کل دارایی‌ها در مدل EPM و نرخ رشد دارایی‌ها در مدل CPM دارای ارتباط قابل‌توجهی با برآوردهای کارایی هستد. درنهایت، نتایج به کیفیت اعتباری ضعیف در بانک‌های ایرانی در سال‌های 1396-1397 اشاره دارد.
اصالت/ارزش افزوده علمی:  در این تحقیق، برای نخستین بار ماهیت ارتباط  کارایی با شاخص‌های کلیدی عملکرد برآورد شده است. تکنیک تحلیل پوششی داده‌ها به منظور شناسایی هدفمند معیارهایی برای تحلیل نسبت‌های مالی مورد استفاده قرار گرفته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A comparison of super-efficiency through data envelopment analysis technique and financial ratios in Iranian stock exchange banks

نویسندگان [English]

  • nasrin bagheri mazraeh 1
  • Mohsen Rostami Mal Khalife 2
  • Meysam Varzi 3

1 Department of Financial Engineering, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Math, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Business Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

چکیده [English]

Purpose: Efficiency is an economic concept which shows the performance of a wide range of economic activities in different areas of an economic sector. Most of studies using frontier technique Data Envelopment Analysis (DEA) do not test for the relationship of efficiency estimation with key performance indicators. This is despite the fact that DEA is one of the most effective tools for measuring and evaluating efficiency. Nevertheless, identifying the relationship between efficiency estimates and commonly accepted financial measures of performance could guide benchmarking activities, pricing decisions, and regulatory monitoring.
Methodology:  In this paper, the DEA super-efficiency formula is tested in two profitability models. Four ratios of net interest income to total assets, post-tax profit to total assets, owner’s equity returns and impaired loans to total assets, were calculated with a developed profitability model; besides, the growth rate of assets was calculated with main profitability model and all the aforementioned ratios addressed a significant association with efficiency estimates.
Findings: In this study, the DEA super-efficiency formula is tested in two profitability models for 15 banks for two years. The correlation obtained is generally low. However, the four ratios of net interest income to total assets, post-tax profit to total assets, owner’s equity returns and impaired loans to total assets, in the EPM model and asset growth rate in the CPM model have a significant relationship with performance estimates. Finally, the results indicate poor credit quality in Iranian banks in 1397-1397.
Originality/Value: In this study, for the first time, the nature of the relationship between performance and key performance indicators has been estimated. DEA technique has been used to purposefully identify criteria for analyzing financial ratios.

کلیدواژه‌ها [English]

  • Data Envelopment Analysis
  • Financial Ratios
  • Efficiency
  • benchmark Ratio
  • Banking
Ahmadian, A. (2013). Evaluating the performance of the banking industry in Iran. Monetary and banking research quarterly, 10(31), 1-110. (In Persian). http://www.mbri.ac.ir/userfiles/file/working%20paper/1392/MBRI9210.pdf
Amiri, H., & Tofighi, M. (2017). Requirements for the existence of deposit insurance and its relationship with banking resistance. Financial economics, 11(41), 177-199. (In Persian). https://www.sid.ir/en/journal/ViewPaper.aspx?id=601606
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science39(10), 1261-1264.
Ariff, M., Skully, M. T., & Ahmad, R. (2007). Factors determining mergers of banks in Malaysia's banking sector reform. Multinational finance journal11(1/2), 1-31.
Avkiran, N. K. (2009). Removing the impact of environment with units-invariant efficient frontier analysis: an illustrative case study with intertemporal panel data. Omega37(3), 535-544.
Avkiran, N. K., & Morita, H. (2010). Predicting Japanese bank stock performance with a composite relative efficiency metric: a new investment tool. Pacific-basin finance journal18(3), 254-271.
Avkiran, N. K., & Rowlands, T. (2008). How to better identify the true managerial performance: state of the art using DEA. Omega36(2), 317-324.
Avkiran, N. K., & Thoraneenitiyan, N. (2010). Purging data before productivity analysis. Journal of business research63(3), 294-302.
Barzegarinegad, A., Jahanshahloo, G., & Rostamy-Malkhalifeh, M. (2014). A full ranking for decision making units using ideal and anti-ideal points in DEA. The scientific world journal, 2014. (In Persian). https://doi.org/10.1155/2014/282939
Bhattacharyya, A., Lovell, C. K., & Sahay, P. (1997). The impact of liberalization on the productive efficiency of Indian commercial banks. European journal of operational research98(2), 332-345.
Brockett, P. L., Charnes, A., Cooper, W. W., Huang, Z. M., & Sun, D. B. (1997). Data transformations in DEA cone ratio envelopment approaches for monitoring bank performances. European journal of operational research98(2), 250-268.
Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Management science27(6), 668-697.
Choi, I. (2001). Unit root tests for panel data. Journal of international money and finance20(2), 249-272.
Cook, W. D., Liang, L., Zha, Y., & Zhu, J. (2009). A modified super-efficiency DEA model for infeasibility. Journal of the operational research society60(2), 276-281.
Cooper, W. W., Ruefli, T. W., Deng, H., Wu, J., & Zhang, Z. (2008). Are state-owned banks less efficient? a long-vs. short-run data envelopment analysis of Chinese banks. International journal of operational research3(5), 533-556.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software (Vol. 2). New York: Springer.
Drake, L., Hall, M. J., & Simper, R. (2006). The impact of macroeconomic and regulatory factors on bank efficiency: a non-parametric analysis of Hong Kong’s banking system. Journal of banking & finance30(5), 1443-1466.
Drake, L., Hall, M. J., & Simper, R. (2009). Bank modelling methodologies: A comparative non-parametric analysis of efficiency in the Japanese banking sector. Journal of international financial markets, institutions and money19(1), 1-15.
Elyasiani, E., Mehdian, S., & Rezvanian, R. (1994). An empirical test of association between production and financial performance: the case of the commercial banking industry. Applied financial economics4(1), 55-60. (In Persian). https://doi.org/10.1080/758522125
Havrylchyk, O. (2006). Efficiency of the Polish banking industry: foreign versus domestic banks. Journal of banking & finance30(7), 1975-1996.
Kao, C., & Liu, S. T. (2004). Predicting bank performance with financial forecasts: a case of Taiwan commercial banks. Journal of banking & finance28(10), 2353-2368.
Karshenasan, A., & Khodayari Fard, M. (2013). Performance analysis and ranking of Iranian banks using financial ratios. First national conference on monetary and banking management development, Tehran. (In Persian). https://civilica.com/papers/l-5082/
Laurenceson, J., & Qin, F. (2008). Has minority foreign investment in China's banks improved their cost efficiency?. China & world economy16(3), 57-74.
Leightner, J. E., & Lovell, C. K. (1998). The impact of financial liberalization on the performance of Thai banks. Journal of economics and business50(2), 115-131.
Lotfi, F. H., Navabakhs, M., Tehranian, A., Rostamy-Malkhalifeh, M., & Shahverdi, R. (2007). Ranking bank branches with interval data—the application of DEA. International mathematical forum, 2(9), 429-440. (In Persian). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.684.785&rep=rep1&type=pdf
Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford bulletin of economics and statistics, 61(S1), 631-652.
Miller, S. M., & Noulas, A. G. (1996). The technical efficiency of large bank production. Journal of banking & finance20(3), 495-509.
Mousavi, S. J., & Kazemi, A. (2013). Ranking of Iranian banks using multivariate decision making method. Journal of management studies, 4(3), 121-140. (In Persian). https://www.sid.ir/en/journal/ViewPaper.aspx?id=371790
Ray, S. C. (2007). Are some Indian banks too large? an examination of size efficiency in Indian banking. Journal of productivity analysis27(1), 41-56.
Rezaei, M. (2018). Evaluation major causes and consequences of economic crisis in Iran. Financial economics, 12(42), 201-227. (In Persian). http://ecj.iauctb.ac.ir/article_543462.html
Rostamy-Malkhalifeh, M., & Mollaeian, E. (2012). Evaluating performance supply chain by a new non-radial network DEA model with fuzzy data. Journal of data envelopment analysis and decision, 1-9. (In Persian). https://d-nb.info/102941341X/34
Sathye, M. (2005). Technical efficiency of large bank production in Asia and the Pacific. Multinational finance journal9(1/2), 1-22. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2625078
Sherman, H. D., & Zhu, J. (2006). Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: evidence from a US bank application. Annals of operations research145(1), 301-319.
Sturm, J. E., & Williams, B. (2004). Foreign bank entry, deregulation and bank efficiency: lessons from the Australian experience. Journal of banking & finance28(7), 1775-1799.
Sudani, A. (2017). Ranking of banks and financial institutions based on Kamels international indices. Monetary-banking research quarterly, 10(31), 141-171. (In Persian). https://jmbr.mbri.ac.ir/article-1-713-fa.pdf
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research130(3), 498-509.
Xiaogang, C., Skully, M., & Brown, K. (2005). Banking efficiency in China: application of DEA to pre-and post-deregulation eras: 1993–2000. China economic review16(3), 229-245.
Yeh, Q. J. (1996). The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. Journal of the operational research society47(8), 980-988.