بررسی سطح کارایی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران بر مبنای تکنیک تحلیل پوششی داده‌ها
محورهای موضوعی : مدیریت صنعتی
Hiresh Soltanpanah
1
(
Department of Management, Sanandaj Branch, Islamic Azad University, Kurdistan, Iran
)
Iman Dadashi
2
(
Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran
)
Samira Zarei
3
(
Department of Accounting, West Tehran Branch, Islamic Azad University, Tehran, Iran
)
کلید واژه: Data envelopment analysis, تحلیل پوششی داده‌ها, کارآیی نسبی, کارآیی مطلق, متغیر ورودی و خروجی, CCR, BCC, Relative Efficiency, Absolute efficiency, Input and Output variable,
چکیده مقاله :
در این پژوهش، کارایی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران با استفاده از تکنیک تحلیل پوششی داده‌ها مورد سنجش قرار گرفته است. بدین منظور با استفاده از مدل‌های CCR داده‌گرا، BCC و رویکردهای CRS و VRS، کارآیی نسبی شرکت‌ها را محاسبه نموده و ناکارآیی آنها را به دو بخش تکنیکی و مقیاس، تفکیک نمودیم. در ادامه از بین واحدهای با کارآیی نسبی 100%، اقدام به تعیین شرکت‌هایی با کارآیی مطلق نموده و در پایان، با شناسایی واحدهای کارآمد ضعیف، گروه‌های مرجع را به عنوان الگویی برای بهبود کارآیی آنان، مشخص کردیم. داده‌‌های موردنیاز پژوهش، از صورت‌های مالی 75 شرکت پذیرفته‌شده در بورس اوراق بهادار تهران در 3 صنعت، مواد و محصولات شیمیایی، فرآورده‌های غذایی و آشامیدنی و محصولات کانی غیرفلزی، برای دوره زمانی 1389-1385 گردآوری شده‌اند. با بررسی مطالعات انجام‌شده و نیز نظرسنجی از خبرگان مالی، جهت محاسبه کارایی شرکت‌ها، از 2 متغیر ورودی شامل، کل دارایی‌ها و نسبت کل بدهی به کل دارایی‌ها و نیز 3 متغیر خروجی شامل، سود هر سهم، نرخ بازده سرمایه‌گذاری‌ها و نرخ بازده حقوق صاحبان سهام، استفاده شده است. نتایج حاصل از بررسی داده‌ها برای شرکت‌های ناکارآ، حاکی از آن بود که میزان قابل توجهی از ناکارآیی‌های موجود، ناشی از بهینه نبودن حجم تولید در این شرکت‌هاست. همچنین یافته‌ها بیانگر آن بودند که تمامی شرکت‌های کارآ در سه صنعت مورد مطالعه، از نوع کارآی ضعیف بوده و هیچ شرکتی با کارآیی مطلق در بین آنها وجود ندارد.
In this research, we investigate the efficiency of companies listed on Tehran stock exchange using Data envelopment analysis (DEA). To do so, we compute the relative efficiency of the companies using input oriented CCR, BCC and CRS and VRS approaches and separate their inefficiency into two technical and scale sections. In continuous, we tend to determine the companies with the absolute efficiency among the companies with one hundred percent relative efficiency. Finally, we try to determine the reference groups as a pattern for improving their efficiency by identifying the weakly efficient companies. The research data were collected from financial statement of 75 companies listed in three different industries including chemical, food and non-metal in the Tehran stock exchange from 2006 to 2010. By considering the previous researchers, in order to compute the efficiency of the companies, we use from two input variables including total assets and total liability to total assets ratio and three output variables including EPS, ROA and ROE. The results show that the significant amount of existing inefficiency is because of the scale inefficiency in these companies, while all the efficient companies in these three industries are as the weakly efficiency type and there isn’t any company with the absolute efficiency among them.
1- Charnes A., W.W.Cooper and E.Rhodes. (1978). Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, (2), 429-444.
2- Chang SY, Chen TH. (2008). Performance ranking of Asian lead frame firms: A slack-based method in data envelopment analysis. Int. J. Prod. Res., 46, 3875-3885.
3- Duzakin. E., Duzakin. H. (2007). Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in Turkey. European Journal of Operational Research; 182, 1412-1432.
4- Durand R, Vargas V. (2003). Ownership, organization, and private firms' efficient use of resources. Strat. Manage. J., 24(7), 667– 675.
5- Eken, M.H. and S. Kale. (2011). measuring bank branch performance using data envelopment analysis: The case of Turkish bank branches. African Journal of Business Management, 5(3), 889-901.
6- Ertürk, M., Aşık, S. (2011). Efficiency analysis of Turkish natural gas distribution companies by using data envelopment analysis method, Retrieved from: http://www.sciencedirect.com.
7- Eslami, gholamreza & Kashanipour, Mohamad. (2004). Measuring Iranian bank branch performance using data envelopment analysis, The Iranian accounting and auditing review, 38, 3-27
8- Fallahi, A. , Ebrahimi. R. & Ghaderi. S.F. (2011). Measuring efficiency and productivity change in power electric generation management companies by using data envelopment analysis: A case study. Retrieved from: http://www.sciencedirect.com.
9- Farsijani, Hasan & et al. (2011). Model for data envelopment analysis approach, input - output shaft, Vision Industrial Management, 1, 39-56.
10- Feroz, E., Kim. S. & Raab. R.L. (2003). Financial Statement Analysis: A Data Envelopment Analysis Approach. Journal of the operational Research Society; 54, 48–58.
11- Fombrun CJ. (2007). List of Lists: A Compilation of International Corporate Reputation Ratings. Corp. Reputation Rev., 10(2), 144-153.
12- Halkos, G. E., Salamouris D. S. (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach. Management Accounting Research; 15, 201–224.
13- Haq, M., Skully, M., Pathan, Sh. (2006). Efficiency of Microfinance Institutions: A Data Envelopment Analysis, Electronic copy available at: http://ssrn.com/abstract=1405709.
14- Kao C, Hung HT. (2008). Efficiency analysis of university departments: An empirical study. Omega, 36, 653-664.
15- Kao C, Hwang SN. (2008). Efficiency decomposition in two-stage dataenvelopment analysis: An application to non-life insurance companiesin Taiwan. Eur. J. Oper. Res., 185, 418-429.
16- Khajavi, Shokrolah & et al. (2008). Data envelopment analysis complementary to the traditional analysis of financial ratios, The Iranian accounting and auditing review, 60, 41-56.
17- Liang, G. et al. (2006). A data envelopment analysis of shipping industry bond rating, Tamkang Journal of Science and Engineering. 9(4), 403-408.
18- Malhotra, R. Malhotra, D.K. & Russel, P. (2007). Using data envelopment analysis to rate bonds, Proceedings of the Northeast Business & Economics Association. 4, 420-423.
19- Malhotra, R. Malhotra, D.K. & Lermack, H. (2008). Using data envelopment analysis to analyze the performance of North American class I freight railroads, Retrieved from: http://www.aar.org/media/AAR/BackgroundPapers/775.ashx.
20- Martin D.H., G.Kocher and M. Sutter. (2000). Measuring Efficiency of German Football Teams by DEA, University of Innsbruck, Australia, 4-5.
21- Masihabadi, Abolghasem & Vahedian, Maysam. (2009). Measuring performance using data envelopment analysis and Ability to pay the debt at maturity, www.Iranianaa.com.
22- Mohammadi, Ali. (2007). Application of mathematical programming techniques for Pharmaceutical companies financial statements analysis, Journal of Social Sciences and Humanities of Shiraz University, 26. 117-135.
23- Momeni, Mansour. (2010). New Topics in Operations Research, 147-175.
24- Oryani, Bahare. (2005). Credit Risk rating for bank customers by data envelopment analysis Thesis Master of Economic Sciences, Hamedan.
25- Powers, J. McMullen, P. R. (2000). Using Data Envelopment Analysis to Select Efficient Large Market Cap Securities, Journal of Business and Management, 7, 31-42.
26- Romano, G. Guerrini, A. (2011). Measuring and comparing the efficiency of water utility companies: A DEA approach. Retrieved from: http://www.sciencedirect.com.
27- Salehi, Jamshid & et al. (2008). Ranking efficiency units by combination DEA and AHP in Provincial commercial organizations, Journal of Knowledge Management. 81, 15-29.
28- Sueyoshi, T. Mika, G. (2009). Can R&D Expenditure Avoid Corporate Bankruptcy? Comparison between Japanese Machinery and Electric Equipment Industries Using DEA Discriminant Analysis. European Journal of Operational Research; 196, 289–311.
29- Sufian F, Habibullah MS. (2009). Do mergers and acquisitions leads to a higher technical and scale efficiency? Evidence from Malaysia. Afr. J. Bus. Manage. 3(8), 340-349.
30- Tien-Hui Chen. (2011). Using Data Envelopment Analysis (DEA) to the efficiency evaluation and improvement of a Taiwanese commercial bank. African Journal of Business Management, 5(18), 7716-7722.
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