FINANCIAL PERFORMANCE EVALUATION USING FUZZY GRA AND FUZZY ENTROPY METHODS: WHOLESALE AND RETAIL INDUSTRY

Yıl 2020, Sayı: 28, 47 - 64, 21.06.2020

Nada Sarsour , Hüseyin Dağlı , Selçuk Perçin

https://doi.org/10.18092/ulikidince.653144

Cited By: 1

Öz

Financial evaluation is the starting point for making business plan and strategy, therefore, this study aimed mainly to present an effective approach to evaluate the financial performance of the BIST Wholesale and Retail Industry firms listed on Istanbul Stock Exchange over the period of 2015-2018. The Fuzzy Grey Rational Analysis (GRA) method is used to evaluate financial performance of different alternatives considering the distances between fuzzy sets based on the grey and fuzzy theory. While, Fuzzy Entropy method is used to determine the relative importance of 15 financial criteria (ratios). Based on a comprehensive financial evaluation framework the study ranks the financial performance of the 17 wholesale and retail trade index firms and shows that MIPAZ firm has the best relative financial performance. Moreover, net profit margin ratio has the highest relative importance indicator for evaluating financial performance. A sensitivity analysis is presented for confirming validity of the proposed model, in addition to a comparison between fuzzy GRA and GRA is demonstrated to test the reliability of the proposed model.

Anahtar Kelimeler

Financial Performance Evaluation, Financial Ratio, Fuzzy GRA, Fuzzy Entropy

FINANCIAL PERFORMANCE EVALUATION USING FUZZY GRA AND FUZZY ENTROPY METHODS: WHOLESALE AND RETAIL INDUSTRY

Öz

Finansal değerlendirme, işletme planı ve stratejisi için başlangıç noktasıdır. Bu nedenle, çalışmada BIST Toptan ve Perakende Ticaret indeksindeki şirketlerin 2015-2018 dönemindeki finansal performanslarını değerlendirmek için etkili bir model sunmak amaçlanmıştır. Finansal performans çok boyutlu bir kavram olduğundan, çalışmada çok kriterli karar verme yöntemlerinden Bulanık Gri İlişkisel Analiz (GİA) yöntemi ve Bulanık Entropi yöntemi kullanılmıştır. Bulanık GİA, Gri teoriye dayalı bulanık kümeler arasındaki mesafeleri göz önünde bulundurarak farklı alternatiflerin finansal performansını değerlendirmek için kullanılmıştır. Bulanık Entropi yöntemi ise, çalışmada kullanılan kârlılık, kaldıraç, büyüme, likidite, verimlilik ve piyasa oranlarının ağırlıklarını hesaplamak için kullanılmıştır. Önerilen modeli, toptan ve perakende ticaret endeksinde yar alan 17 şirkete uygulanmış olup ve MIPAZ şirketinin en iyi finansal performansa sahip olduğu sonucuna ulaşılmıştır. Önerilen modelin geçerliliğini doğrulamak için duyarlılık analizi ve GİA ile karşılaştırma analizi yapılmıştır. Bu doğrultusunda, çalışma sonuçları toptan ve perakende ticaret firmalarının değerlendirilmesinde önerilen modelin geçerli ve güvenilir olduğunu göstermiştir.

Anahtar Kelimeler

Finansal Performans değerlendirme, Finansal oranları, Bulanık GİA, Bulanık Entropi

Kaynakça

• Ahrendsen, B.L. and Katchova, A.L. (2012). Financial ratio analysis using ARMS data. Agricultural Finance Review, 72(2), 262-272.
• Aras, G., Tezcan, N. and Kutlu Furtuna, O. (2018). Comprehensive evaluation of the financial performance for intermediary institutions based on multi-criteria decision-making method. Journal of Capital Markets Studies, 2(1), 37-49.
• Bertoneche, M. and Knight, R. (2001). Financial Performance, Chapter 3: Assessing financial health, 74-105.
• Borhan, H., Naina Mohamed, R. and Azmi, N. (2014). The impact of financial ratios on the financial performance of a chemical company: The case of Lyondell Basell Industries. World Journal of Entrepreneurship, Management and Sustainable Development, 10(2), 154-160.
• Chadwick, L. (1984). Comparing financial performance: Ratio analysis and retail management. Retail and Distribution Management, 12(2), 35-37.
• Chan, T. K. and Abdul-Aziz, A. R. (2017). Financial performance and operating strategies of Malaysian property development companies during the global financial crisis. Journal of Financial Management of Property and Construction, 22(2), 174-191.
• Deng, J. L. (1982). Control problems of grey systems. Sys. & Contr. Lett., 1(5), 288-294.
• Deng, J. L. (1988). Properties of relational space for grey system. Grey System, China Ocean, Beijing.
• Deng, J. L. (1989). Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1-24.
• Dimara, E., Skuras, D., Tsekouras, K. and Goutsos, S. (2004). Strategic orientation and financial performance of firms implementing ISO 9000. International Journal of Quality & Reliability Management, 21(1), 72-89.
• Duncan, E. and Elliott, G. (2004). Efficiency, customer service and financial performance among Australian financial institutions. International Journal of bank marketing, 22(5), 319-342.
• Edirisinghe, N. C. P. and Zhang, X. (2008). Portfolio selection under DEA-based relative financial strength indicators: case of US industries. Journal of the Operational Research Society, 59(6), 842-856.
• Feng, C. M. and Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
• Gumus, A., Yayla, A., Çelik, E. and Yildiz, A. (2013). A combined fuzzy-AHP and fuzzy-GRA methodology for hydrogen energy storage method selection in Turkey. Energies, 6(6), 3017-3032.
• Horrigan, J. O. (1968). A short history of financial ratio analysis. The Accounting Review, 43(2), 284-294.
• Ho, C. and Wu, Y. (2006). Benchmarking performance indicators for banks. Benchmarking: An International Journal, Vol. 13 No. 1/2, pp. 147-159.
• Jia, Z. and Zhang, Y. (2019). Interval-Valued Intuitionistic Fuzzy Multiple Attribute Group Decision Making with Uncertain Weights. Mathematical Problems in Engineering, 2019.
• Karimi, A. and Barati, M. (2018). Financial performance evaluation of companies listed on Tehran Stock Exchange: A negative data envelopment analysis approach. International Journal of Law and Management, 60(3), 885-900.
• Karimi, A., Ahmadpour, B. and Marjani, M. R. (2018). Using the fuzzy grey relational analysis method in wastewater treatment process selection. Iranian Journal of Health, Safety and Environment, 5(3), 1041-1050.
• Katchova, A. L. and Enlow, S. J. (2013). Financial performance of publicly-traded agribusinesses. Agricultural Finance Review, 73(1), 58-73.
• Kazan, H. and Ozdemir, O. (2014). Financial performance assessment of large scale conglomerates via TOPSIS and CRITIC methods. International Journal of Management and Sustainability, 3(4), 203.
• Li, N. and Zhao, H. (2016). Performance evaluation of eco-industrial thermal power plants by using fuzzy GRA-VIKOR and combination weighting techniques. Journal of Cleaner Production, 135, 169-183.
• Lotfi, F. H. and Fallahnejad, R. (2010). Imprecise Shannon’s entropy and multi attribute decision making. Entropy, 12(1), 53-62.
• Önder, E. and Altintaş, A. (2017). Financial Performance Evaluation of Turkish Construction Companies in Istanbul Stock Exchange (BIST). International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(3), 108-113.
• Perçin, S. and Aldalou, E. (2018). Financial Performance Evaluation Of Turkish Airline Companies Using Integrated Fuzzy AHP Fuzzy TOPSIS Model*. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 583-598.
• Shaverdi, M., Heshmati, M.R. and Ramezani, I. (2014), Application of fuzzy AHP approach for financial performance evaluation of Iranian Petrochemical Sector, Procedia Computer Science, 31, 995-1004.
• Reddy, K.S., Nangia, V. K. and Agrawal, R. (2013). Corporate mergers and financial performance: a new assessment of Indian cases. Nankai Business Review International, 4(2), 107-129.
• Teker, S., Teker, D. and Güner, A. (2016). Financial performance of top 20 airlines. Procedia-Social and Behavioral Sciences, 235, 603-610.
• Wu, H. H. (2002). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering, 15(2), 209-217.
• Wu, W. (2017). Grey relational analysis method for group decision making in credit risk analysis. EURASIA Journal of Mathematics, Science and Technology Education, 13(12), 7913-7920.
• Yalcin, N., Bayrakdaroglu, A. and Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364.
• Yin, S., Qian, Y. and Gong, M. (2017). Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recognition, 68, 245-259.