Ranking of firms by performance using I-distance method
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Author(s)Nurdan DeğirmenciLink to ORCID Index: https://orcid.org/0000-0002-8759-8871
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Tuba Yakici AyanLink to ORCID Index: https://orcid.org/0000-0002-5222-3500
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DOIhttp://dx.doi.org/10.21511/imfi.15(4).2018.07
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Article InfoVolume 15 2018, Issue #4, pp. 85-97
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494 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The objective of this article is to rank firms by their financial performance using statistical I-distance method, which has the ability to determine both ranking and important factors. For this purpose, the method was first applied to 110 Turkish industrial firms without any sectorial separation and then to 7 different sectors, and various findings about firms, sectors and variables were obtained. The I-distance method is used to get rid of the high correlation between variables during the analysis. The reason for choosing the I-distance method is that it allows you to sort the variables by importance and eliminate insignificant variables, as well as take into account correlations between variables. The authors believe that the method is superior to other alternative methods thanks to these qualities. Through a number of analyses, it was possible to see positions of firms both within the whole sample and their own sectors. Furthermore, this method provided valuable information on which factors were important in assessing firms’ financial performance. It has been observed in the analyses that the most effective factors in ranking firms and separating them from each other were profitability ratios, and the fact that liquidity and financial leverage ratios are not effective at all. When examined from a sectoral perspective, the nonmetal mining sector and the chemical, petroleum and plastic sectors seem to be better than other sectors in the performance rankings.
- Keywords
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JEL Classification (Paper profile tab)C38, C44, G39
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References26
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Tables11
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Figures0
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- Table 1. Financial ratios used in the study
- Table 2. The results of the I2-distance method first calculation
- Table 3. The correlation between the I2-distance and the initial indicators
- Table 4. The results of the I2-distance method final calculation
- Table 5. The correlation between the I2-distance and the final variables
- Table 6. The correlation between I2-distance and variables in respect of sectors
- Table 7. Weights of the variables by ENTROPY
- Table 8. The firm ranking by TOPSIS
- Table 9. The firm ranking by the VIKOR
- Table 10. Serial correlation coefficient between ranking methods
- Table A1. The firm list according to sectors
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- Bayrakdaroğlu, Ali, & Yalçın, Neşe (2012). Strategic Financial Performance Evaluation of the Turkish Firms. Traded on ISE Ege Akademik Bakış, 12(4), 529-539.
- Borda, Jean-Charles de (1781). Memoire sur les elections au scrutin. Hiswire de I’Academie Royale des Sciences, 657-665.
- Brealey, Richard A., Myers, Stewart, C., & Marcus, Alan J. (2001). Fundementals of Corporate Finance. USA, McGraw-Hill Education.
- Brigham, Eugene F., & Houston, Joel F. (2007). Fundamentals of Financial Management. USA, Thomson South-Western.
- Bulajic, Milica, Jeremic, Veljko, Knezevic, Snezana, & Zarkic- Joksimovic, Nevenka (2013). Statistical Approach to Evaluating Efficiency of Banks. Ekonomska Istražıvanja-Economic Research, 26(4), 91-100.
- Bulgurcu, Berna (2013). Financial Performance Ranking of the Automotive Industry Firms in Turkey: Evidence from an Entropy-Weighted Technique. International Journal of Economics and Financial Issues, 3(4), 844-851.
- Çelen, A. (2014). Evaluating the Financial Performance of Turkish Banking Sector: A Fuzzy MCDM Approach. Journal of Economic Cooperation and Development, 35(2), 43-70.
- Dmitrovic, Veljko, Dobrota, Marina, & Knezevic, Snezana (2016). A Statistical Approach to Evaluating Bank Productivity. Management, 20(75), 47-56.
- Ertuğrul, Irfan, & Karakaşoğlu, Nilsen (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36, 702-715.
- Feng, Cheng-Min, & Wang, Rong-Tsu (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6, 133-142.
- Hwang Ching-Lai, & Yoon, Kwangsun (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
- Ivanovic, B. (1977). Classification Theory. Institute for Industrial Economic, Belgrade, 171-200.
- Ivanovic, B. A. (1973). Method of Establishing a List of Development Indicators. Paris: UNESCO.
- Mandic, Ksenija, Delibasic, Boris, Knezevic, Snezana, & Benkovic, Sladjana (2014). Analysis of the Financial Parameters of Serbian Banks Through the Application of the Fuzzy AHP and TOPSIS Methods. Economic Modelling, 43, 30-37.
- Mihailovic, Nevena, Bulajic, Milica, & Savić, Gordana (2009). Ranking of Banks in Serbia. Yugoslav Journal of Operations Research, 19(2), 323-334.
- Milenkovic, Nemanja, Vukmirovic Jovanka, Bulajic Milica, & Radojicic Zoran (2014). A Multivariate Approach in Measuring Socio-Economic Development of MENA Countries. Economic Modelling, 38, 604-608.
- Moghimi, Rohollah, & Anvari, Alireza (2014). An Integrated Fuzzy MCDM Approach and Analysis to Evaluate the Financial Performance of Iranian Cement Firms. The International Journal of Advanced Manufacturing Technology, 71(1), 685-698.
- Opricovic, Serafim, & Tzeng, Gwo-Hshiung (2004). The Compro¬mise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445- 455.
- Popovic, Blazenka, Ceranić, Slobodan, & Paunovic, Tamara (2016). I-Distance and Separability Coefficient in Business Evaluation of Sme’s in Agribusiness. Economics of Agriculture, 63(3), 1039-1052.
- Saaty, Thomas, L. (1980). The Anal¬ytic Hierarchy Process. New York: McGraw-Hill.
- Stillwell, William, G., Seaver, David, A., & Edwards, Ward (1981). A comparison of weight approximation techniques in multiattribute utility decision making. Organizational Behavior and Human Performance, 28(1), 62-77.
- Wang, Tien-Chin, & Lee, Hsien-Da (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980-8985.
- Wang, Yu-Jie (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems with Applications, 34, 1837- 1845.
- Wang, Yu-Jie (2009). Combining Grey Relation Analysis with FMCGDM to Evaluate Financial Performance of Taiwan Container Lines. Expert Systems With Applications, 36, 2424- 2432.
- Wang, Yu-Jie (2014). The Evaluation of Financial Performance for Taiwan Container Shipping Firms by Fuzzy TOPSIS. Applied Soft Computing, 22, 28-35.
- Yalçın, Nese, Bayrakdaroğlu, Ali, & Kahraman, Cengiz (2012). Application of Fuzzy Multi-Criteria Decision Making Methods for Financial Performance Evaluation of Turkish Manufacturing Industries. Expert Systems with Applications, 39, 350- 364.
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Industrial development in Saudi Arabia: disparity in growth and development
Anis Ali
doi: http://dx.doi.org/10.21511/ppm.18(2).2020.03
Problems and Perspectives in Management Volume 18, 2020 Issue #2 pp. 23-35 Views: 3089 Downloads: 1043 TO CITE АНОТАЦІЯIndustrial development in any nation plays a vital role in the strengthening of the economy and employs human resources as per specifications and directly towards the mainstream of the nation. In Saudi Arabia, there was rapid industrialization for four decades. Some manufacturing industries performed well, while some internal and external business environment factors influenced the others’ performance. The study focuses on historical development, as well as recent trends of well-performing and poor-performing industries of Saudi Arabia. Small manufacturing industries in Saudi Arabia are underdeveloped and need more attention to attain sustainable and progressive growth rate in the form of the number of establishments to enjoy the potential. Manufacture of tobacco, computer, electronics and optics, pharmaceuticals and transport equipment are the deprived and underdeveloped industries of the manufacturing sector. There should be some easy credit schemes for the development of deprived or underdeveloped manufacturing industries. To attain sustainable growth and development of the manufacturing sector, a positive attitude of the government towards underdeveloped manufacturing industries, favorable labor laws, and entrepreneurial awareness, technical and financial assistance are necessary. The growth and development of the manufacturing sector of Saudi Arabia will be helpful in the attainment of goals of Vision 2030.
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Why banks should consider ESG risk factors in bank lending?
Sarwar Uddin Ahmed ,
Samiul Parvez Ahmed ,
Ikramul Hasan
doi: http://dx.doi.org/10.21511/bbs.13(3).2018.07
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Integrated reporting and financial performance of South African listed banks
Reon Matemane ,
Rozane Wentzel
doi: http://dx.doi.org/10.21511/bbs.14(2).2019.11
Banks and Bank Systems Volume 14, 2019 Issue #2 pp. 128-139 Views: 2800 Downloads: 553 TO CITE АНОТАЦІЯThe recent development of integrated reporting intends to address the limitations associated with corporate reporting practices. This paper aims to examine whether a statistically significant relationship exists between integrated reporting quality and financial performance. Secondary data was used, namely the integrated reports and annual financial statements of South African banks listed on the Johannesburg Stock Exchange (JSE) for 2010–2014. For the period 2005–2009, only the financial statements were used, since integrated reporting was not yet mandatory. The research design was longitudinal and it combined qualitative and quantitative methods. Descriptive statistics and Feasible Generalized Least Square (FGLS) were used to explore the relationships between financial performance and integrated reporting quality. The results indicate that there is a positive relationship between integrated reporting quality (IRQ) and earnings per share (EPS). However, there is no significant relationship between IRQ and Tobin’s q (Q-Ratio), IRQ and return on equity (ROE), IRQ and return on assets (ROA) as well as IRQ and economic value added (EVA). Moreover, there are no significant differences on the financial performance of the listed banks before and after the introduction of integrated reporting.
