Financial Performance Evaluation of Food and Drink Index Using Fuzzy MCDM Approach

Abstract

Performance evaluation presents a very complex field involving different criteria and contradicted information. Though, there is an insisting need to a reliable and consistent approach where the application procedures are not complicated. In this study, a fuzzy MCDM approach is developed to evaluate the financial performance of companies listed in food and drink index of Istanbul Stock Exchange. Financial ratios were identified to create a base for financial performance evaluation in the areas of: profitability, efficiency, growth, liquidity, leverage and market ratios. Weight coefficients were obtained by the objective method of FSE. Evaluation and ranking were made on the base of the new method of FEDAS. In order to test the reliability of the approach a sensitivity analysis is conducted based on CRITIC weighting method. Comparison with FTOPSIS, FVIKOR, FCOPRAS, FMOORA and FSAW methods and spearman correlation are conducted to test validity of the proposed approach. The proposed approach is reliable and provides the most suitable result comparing with other MCDM methods, and has a strong positive correlation with average results.

Keywords

• Fuzzy EDAS,Financial Performance Evaluation,Food and Drink Index

Financial Performance Evaluation of Food and Drink Index Using Fuzzy MCDM Approach

Öz

Performans değerlendirmesi, farklı kriterler ve çelişkili veriler içeren çok karmaşık bir çalışma alanıdır. Daha kaliteli bir sonuca ulaşmak için araştırmacılar var olan bütün verilere dayanarak en uygun yöntemleri kullanmaya çalışmışlardır. Bu çalışmada, ÇKKV yöntemlerine dayanan bir finansal performans değerlendirme modeli önerilmektedir.  Önerilen bu modeli Gıda ve İçecek İndeksinde yer alan firmaların finansal performansını değerlendirmek için uygulanmıştır. Çalışmada, karlılık, verimlilik, büyüme, likidite, kaldıraç ve piyasa oranları kullanılmıştır. Kriterleri ağırlıklandırmak amacıyla FSE, alternatifleri sıralamak amacıyla ise FEDAS yöntemleri kullanılmıştır. Çalışmada önerilen yaklaşımın güvenilirliğini test etmek için CRITIC ağırlıklandırma yöntemine dayalı bir duyarlılık analizi yapılmıştır. Ayrıca, yaklaşımın geçerliliğini test etmek için FTOPSIS, FVIKOR, FCOPRAS, FMOORA ve FSAW yöntemleriyle karşılaştırmalar yapılmıştır. Çalışma sonucunda önerilen modelin güvenilir olduğu tespit edilmiş olup diğer ÇKKV yöntemleriyle karşılaştırıldığında en uygun sonucu sağladığı görülmüştür.

Anahtar Kelimeler

• Bulanık EDAS,Finansal Performans Değerlendirme,Gıda ve İçecek İndeksi

References

1. Aras, G., Tezcan, N., and Kutlu Furtuna, Ö. (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.
2. Chadwick, L. (1984). Comparing financial performance: Ratio analysis and retail management. Retail and Distribution Management, 12(2), 35-37.
3. Diakoulaki, D., Mavrotas, G., and Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res, 22(7), 763–70.
4. 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.
5. Erkayman, B., Khorshidi, M., and Usanmaz, B. (2018). An integrated fuzzy approach for ERP deployment strategy selection under conflicting criteria. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 32(3), 807-823.
6. Gündoğdu, F.K., Kahraman, C., and Civan, H.N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent and Fuzzy Systems, 35, 6353-6365.
7. Ilieva, G., Yankova, T. and Klisarova-Belcheva, S. (2018). Decision Analysis with Classic and Fuzzy EDAS Modifications. Computational & Applied Mathematics. 37.
8. Jitmaneeroj, B. (2017). Does investor sentiment affect price-earnings ratios?. Studies in Economics and Finance, 34(2), 183-193.

9. Kahraman, C., Keshavarz-Ghorabaee, M., Zavadskas, E., Çevik, S., Yazdani, M. and Öztayşi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management. 25, 1-12.
10. Karaşan, A., and Kahraman, C. (2017). Interval-Valued Neutrosophic Extension of EDAS Method. Advances in Intelligent Systems and Computing, 343–357.
11. 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.
12. Katchova, A.L., and Enlow, S.J. (2013). Financial performance of publicly‐traded agribusinesses. Agricultural Finance Review, 73(1), 58-73.
13. Keshavarz-Ghorabaee, M., Zavadskas, E., Olfat, L., and Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica. 26, 435–451.
14. Keshavarz-Ghorabaee, M., Zavadskas, E., Amiri, M. and Turskis, Z. (2016). Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection. International Journal of Computers Communications & Control, 11(3), 358-371.
15. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z. and Antucheviciene, J., (2017a). A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Computers & Industrial Engineering, 112, 156-174.
16. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E., and Turskis, Z. (2017b). Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets. E+M Ekonomie a Management. 20. 48-68.
17. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z., and Antucheviciene, J. (2017c). Stochastic EDAS method for multi-criteria decision-making with normally distributed data. Journal of Intelligent and Fuzzy Systems, 33, 1627-1638.
18. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E., Hooshmand, R., and Antuchevičienė, J. (2017d). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19.
19. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z. and Antucheviciene, J. (2017e). A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria. J. Air Transport Manage. 63, 45–60.
20. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E., Turskis, Z. and Antucheviciene, J. (2018a). A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation. Information (Switzerland). 9.
21. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., and Antucheviciene, J. (2018b). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18(1), 32–49.
22. Khuan Chan, T., 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.
23. Kundakcı, N. (2019). An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives. J Multi-Crit Decis Anal. 26:27–34.
24. Lev, B. (1969). Industry Averages as Targets for Financial Ratios. Journal of Accounting Research, 7(2), 290-299.
25. Liang, W., Zhao, G., and Luo, S. (2018). An Integrated EDAS-ELECTRE Method with Picture Fuzzy Information for Cleaner Production Evaluation in Gold Mines. IEEE Access, 6, s. 65747-65759.
26. Lotfi, F.H. and Fallahnejad, R., (2010). Imprecise Shannon’s Entropy and Multi Attribute Decision Making. Entropy, 12, 53-62.
27. Malagie, M., Jensen, G., Graham, J. C., and Smith, D. L. (1998). Food industry processes. In ‘‘Encyclopedia of Occupational Health and Safety’’, (J. M. Stellman, Ed.), 4th edn, 67, 2–7. International Labour Office, Geneva.
28. Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications. 38, 12983–12990.
29. Peng, X. and Liu, C. (2017). Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set. Journal of Intelligent & Fuzzy Systems. 32(1), 955-968.
30. 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.
31. Ren, J. and Toniolo, S. (2018). Life cycle sustainability decision-support framework for ranking of hydrogen production pathways under uncertainties: An interval multi-criteria decision making approach. Journal of Cleaner Production. 175. 222-236.
32. Roszkowska, E. and Kacprzak, D. (2016). The fuzzy saw and fuzzy TOPSIS procedures based on ordered fuzzy numbers. Information Sciences, 369, 564-584.
33. Siddiqui, Z.A. and Tyagi, K. (2016). Application of fuzzy-MOORA method: Ranking of components for reliability estimation of component-based software systems. Decision Science Letters, 5, 169–188.
34. Stević, Ž., Pamučar, D., Vasiljević, M., Stojić, G. and Korica, S. (2017). Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company. Symmetry, 9, 279.
35. Stević, Ž., Vasiljević, M., Zavadskas, E., Sremac, S. and Turskis, Z. (2018). Selection of carpenter manufacturer using fuzzy EDAS method. Engineering Economics, 29. 281-290.
36. Tan, P.M., Koh, H.C. and Low, L.C. (1997). Stability of Financial Ratios: A Study of Listed Companies in Singapore. Asian Review of Accounting, 5(1), 19-39.
37. Turskis, Z. and Juodagalvienė, B. (2016). A novel hybrid multi-criteria decision-making model to assess a stairs shape for dwelling houses. Journal of Civil Engineering and Management, 22(8), 1078-1087.
38. Turskis, Z., Morkunaite, Z. and Kutut, V. (2017). A hybrid multiple criteria evaluation method of ranking of cultural heritage structures for renovation projects. International Journal of Strategic Property Management, 21(3), 318-329.
39. Worldfood Istanbul (2018). How well do you know Turkey's food & drink industry?. https://www.worldfood-istanbul.com/Articles/taking-a-look-at-the-turkish-food-drink-indus (Access: 07.06.2019)
40. Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy, Expert Systems with Applications, 38, 5162-5165.
41. Yeh Q-J (1996). Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation. JORS 47(8), 980-988.
42. Zarbakhshnia, N., Soleimani, H. and Ghaderi, H. (2018). Sustainable third-party reverse logistics provider evaluation andselection using fuzzy SWARA and developed fuzzy COPRAS in thepresence of risk criteria. Applied Soft Computing, 65, 307–319.