Prioritizing wholesale companies based on their financial performance: A multi-criteria decision analysis (MCDA) approach

Öz

Financial markets are considered among the vital indicators that can signalize the development of countries. Shares traded in these markets attract the attention of various financial stakeholders. On the other hand, capital markets, as in many other fields, have been affected by the uncertainty brought by the pandemic. In complex scenarios where many variables come into play, multi-criteria decision-making analyzes (MCDA) are used to make accurate inferences. Companies operating in the wholesale sector and traded on Borsa Istanbul, which contribute to the growth rate and export level that Türkiye has achieved in recent years, were analyzed through MCDA methods in this study. Relevant companies were examined with TOPSIS and ELECTRE III methods for 8 periods when the pandemic was felt most intensely. In the study, where 6 different criteria were weighted with CRITIC, companies were ranked according to the scores produced by the aforementioned methods. Notably, both methods identified the same companies as the top-performing alternative for 6 of the 8 periods examined. Since both methods examined produce consistent results, these methods are recommended as a decision support system that financial decision makers can use together in the periods of uncertainty.

Anahtar Kelimeler

• Capital Markets
• Financial Performance
• MCDA

Toptan ticaret şirketlerinin finansal performanslarına göre önceliklendirilmesi: Çok kriterli karar analizi (ÇKKA) yaklaşımı

Öz

Finansal piyasalar ülkelerin gelişmişliklerini gösteren önemli indikatörler arasında sayılmaktadır. Bu piyasalarda işlem gören hisse senetleri, birçok finansal paydaşın ilgisini çekmektedir. Öte yandan, tıpkı birçok alanda olduğu gibi, sermaye piyasalarında da salgının getirdiği belirsizlik etkisini göstermiştir. Birçok değişkenin devreye girdiği karmaşık senaryolarda, doğru çıkarımlar yapabilmek adına çoklu kriterli karar verme analizleri (ÇKKA) kullanılmaktadır. Türkiye'nin son yıllarda yakaladığı büyüme hızı ve ihracat seviyesine katkıda bulunan toptan ticaret sektöründe faaliyet gösteren ve Borsa Istanbul'da işlem gören firmalar, bu çalışmada ÇKKA yöntemleri aracılığıyla analiz edilmiştir. İlgili şirketler, pandeminin en yoğun hissedildiği 8 dönem için TOPSIS VE ELECTRE III yöntemleri ile incelenmişlerdir. Belirlenen 6 farklı kriterin CRITIC ile ağırlıklandırıldığı çalışmada, şirketler ilgili yöntemlerin ürettiği skorlara göre sıralanmışlardır. Dikkat çekici olarak, her iki yöntem de incelenen 8 dönemin 6'sı için aynı şirketleri en yüksek performans gösteren alternatif olarak belirlemiştir. İncelenen iki yöntemin de tutarlı sonuçlar üretmesi nedeniyle bu yöntemler, belirsizlik sürecinde finansal karar vericilerin birlikte kullanabileceği bir karar destek sistemi olarak önerilmişlerdir.

Anahtar Kelimeler

• Sermaye Piyasaları
• Finansal Performans
• ÇKKA

Kaynakça

1. Abdel-Basset, M., Ding, W., Mohamed, R., & Metawa, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries. Risk Management, 22, 192-218. https://doi.org/10.1057/s41283-020-00061-4.
2. Aktaş, Ö. (2022). BİST’de İşlem Gören Bilişim Sektörü Şirketlerinin Pandemi Öncesi ve Pandemi Dönemi Finansal Performanslarının Promethee Yöntemi İle Ölçülmesi. İşletme, 3(2), 15-31.
3. Aldalou, E., & Perçin, S. (2020). Application of integrated fuzzy MCDM approach for financial performance evaluation of Turkish technology sector. International Journal of Procurement Management, 13(1), 1-23. https://doi.org/10.1504/IJPM.2020.105198.
4. Asker, V. (2022). Kovid-19 salgını öncesi ve kovid-19 salgını döneminde imalat sektöründe finansal performansın incelenmesi. Alanya Akademik Bakış, 6(2), 2261-2274. https://doi.org/10.29023/alanyaakademik.1037351.
5. Aytekin, A. (2019). Evaluation of the financial performance of tourism companies traded in BIST via a hybrid MCDM model. International Journal of Applied Research in Management and Economics, 2(4), 20-32. https://doi.org/10.33422/ijarme.v2i4.274.
6. Bağcı, H., & Yerdelen Kaygın, C. (2020). The financial performance measurement of the companies listed in The BIST holding and investment index by the MCDM methods. The Journal of Accounting & Finance / Muhasebe ve Finansman Dergisi, (87). 301-324. https://doi.org/10.25095/mufad.756394.
7. Bayrakdaroğlu, A., & Yalçın, N. (2012). Strategic financial performance evaluation of the Turkish companies traded on ISE. Ege Academic Review, 12(4), 529-539.
8. Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386-1407. https://doi.org/10.1108/K-03-2017-0105.

9. Bektaş, H., & Tekin, H. (2013). Finansal Oranlar ve Borsa Performans Oranları İlişkisi: İMKB’de İşlem Gören Bankaların Kononik Korelasyon Analizi. Marmara Üniversitesi İ.İ.B. Dergisi, 34(1), 317-329.
10. Bulgurcu, B. K. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia-Social and Behavioral Sciences, 62, 1033-1040. https://doi.org/10.1016/j.sbspro.2012.09.176.
11. Chamodrakas, I., Leftheriotis, I., & Martakos, D. (2011). In-depth analysis and simulation study of an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS. Applied Soft Computing, 11(1), 900-907. https://doi.org/10.1016/j.asoc.2010.01.010.
12. De Almeida, A. T., Cavalcante, C. A. V., Alencar, M. H., Ferreira, R. J. P., de Almeida-Filho, A. T., & Garcez, T. V. (2015). Multicriteria and multi-objective models for risk, reliability and maintenance decision analysis. Springer International Publishing. https://doi.org/10.1007/978-3-319-17969-8.
13. Demireli, E. (2010). TOPSIS multi-criteria decision-making method: An examination on state owned commercial banks in Turkey. Journal of Entrepreneurship and Development, 5(1), 101-112.
14. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H.
15. Drury, J. C. (1981). A study of industry financial ratios. Management Decision, 19(1), 24-35. https://doi.org/10.1108/eb001265.
16. Emir, M., Arslantürk, D., & Sevim, U. (2011). 2008 ekonomik krizinin gıda ve tekstil firmaları üzerine etkilerinin finansal oranlar yardımıyla analizi: İMKB’de bir uygulama. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(3), 1-24.
17. Ergül, N., & Seyfullahoğulları, C. A. (2012). The ranking of retail companies trading in ISE. European Journal of Scientific Research, 70(1), 29-37.
18. Ertuğrul, İ., & Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715. https://doi.org/10.1016/j.eswa.2007.10.014.
19. European Commission. (2013). Towards knowledge-driven reindustrialization (Publication No. 347). European Competitiveness Report 2013. European Union Directorate-General for Enterprise and Industry. https://aei.pitt.edu/45452/1/competitiveness_2013.pdf.
20. Feng, C. M., & Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142. https://doi.org/10.1016/S0969-6997(00)00003-X.
21. Figueira, J. R., Greco, S., Roy, B., & Słowiński, R. (2010). ELECTRE methods: Main features and recent developments. Handbook of Multicriteria Analysis, 51-89. https://doi.org/10.1007/978-3-540-92828-7_3.
22. Girubha, J., Vinodh, S., & Kek, V. (2016). Application of interpretative structural modelling integrated multi criteria decision making methods for sustainable supplier selection. Journal of Modelling in Management, 11(2), 358-388. https://doi.org/10.1108/JM2-02-2014-0012.
23. Hwang, C. L., Yoon, K., Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: Methods and applications a state-of-the-art survey, 58-191. Springer Berlin. https://doi.org/10.1007/978-3-642-48318-9_3.
24. İstanbul Menkul Kıymetler Borsası. (1998). Sermaye piyasası ve borsa temel bilgiler kılavuzu (Publication No. 1). İstanbul Menkul Kıymetler Borsası, Eğitim Yayınları.
25. İslamoğlu, M., Apan, M., & Öztel, A. (2015). An evaluation of the financial performance of REITs in Borsa Istanbul: A case study using the entropy-based TOPSIS method. International Journal of Financial Research, 6(2), 124-138. https://doi.org/10.5430/ijfr.v6n2p124.
26. Karahan, M., & Özduran, M. (2012). 2008 küresel krizi sonrası dönemde tekstil sektörünün mali oranlar yöntemiyle finansal durum analizi. SÜ İİBF Sosyal ve Ekonomik Araştırmalar Dergisi, 12(24), 217-239.
27. Karaman, R. (2009). İşletmelerde performans ölçümünün önemi ve modern bir performans ölçme aracı olarak Balanced Scorecard. Sosyal Ekonomik Araştırmalar Dergisi, 8(16), 410-427.
28. Kazan, H., & Özdemir, O. (2014). Financial performance assessment of large-scale conglomerates via TOPSIS and CRITIC methods. International Journal of Management and Sustainability, 3(4), 203-224. https://doi.org/10.18488/journal.11/2014.3.4/11.4.203.224.
29. Kırkulak, B. (2010). Uzun dönemli al ve tut getirileri: İMKB'de halka açık şirketler üzerine bir uygulama. Ege Akademik Bakış, 10(2), 465-486.
30. Kızıl, E. (2019). Borsada işlem gören şirketlerin finansal performansları ile borsa performansları arasındaki ilişki: BİST taş, toprak endeksindeki çimento firmaları üzerine bir uygulama. Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 1(1), 51-67.
31. Koçak, H., Kısakürek, M., & Babacan, A. (2023). Ekonomik kriz dönemlerinde işletmelerin performans etkinlik inclemesi: BİST’te bir araştırma. Erzincan Binali Yıldırım Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(1), 40-60. https://doi.org/10.46482/ebyuiibfdergi.1300289.
32. Köksalan, M. M., Wallenius, J., & Zionts, S. (2011). Multiple criteria decision making: From early history to the 21st century. World Scientific Publishing.
33. Köse, Z. (2020). Covid-19'un küresel ekonomiye etkileri. E. Şen, D. Hıdıroğlu, & O. Yılmaz (Eds.), Covid-19 Pandemisinde Yönetim ve Ekonomi, 44-66. Gazi Kitabevi Yayınları.
34. Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. https://doi.org/10.1016/j.econmod.2014.07.036.
35. Öğüt, A., & Tarhan, S. (2022). Kobi’lerde kurumsallaşmanın firma performansı üzerindeki etkisi, Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 4(2): 157-172. https://doi.org/10.51124/jneusbf.2022.35.
36. Önder, E., Taş, N., & Hepsen, A. (2014). REITs in Turkey: Fundamentals vs. market. The International Journal of Latest Trends in Finance and Economic Sciences, 4(1), 662-676.
37. Roy, B. (1985). Méthodologie multicritère d'aide à la decision, Economica France.
38. Selçuk, B., & Yılmaz, N. (2008, December 19). Küresel finansal sistemde değişim ve Türkiye’ye etkileri [Conference Presentation]. Marmara Üniversitesi Uluslararası Finans Sempozyumu, İstanbul, Türkiye, 335-355.
39. Shaverdi, M., Heshmati, M. R., & Ramezani, I. (2014). Application of fuzzy AHP approach for financial performance evaluation of Iranian petrochemical sector. Procedia Computer Science, 31, 995-1004. https://doi.org/10.1016/j.procs.2014.05.352.
40. Silva, M., & Figueiredo, P. S. (2018). Supplier selection: A proposed framework for decision making.
41. International Journal of Procurement Management, 11(2), 233-249. https://doi.org/10.1504/IJPM.2018.090028. Türkiye Exporters Assembly. (2023). Export Figures of 2022-2023 [Data Set]. https://tim.org.tr/en/export-export-figures.
42. Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Systems with Applications, 36(6), 10135-10147. https://doi.org/10.1016/j.eswa.2009.01.005.
43. Yalçın, N., Bayrakdaroğlu, A., & 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. https://doi.org/10.1016/j.eswa.2011.07.024.
44. Yılmaz, G. (2022). Covid-19 salgınının dünya ve Türkiye ekonomisine etkileri ve 2008 krizi ile mukayesesi, Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 4(2): 132-143. https://doi.org/10.51124/jneusbf.2022.33.
45. Yurdakul, M., & İç, Y. (2003). An illustrative study aimed to measure and rank performance of Turkish automotive companies using TOPSIS. Journal of the Faculty of Engineering and Architecture of Gazi University, 18(1), 1-18.
46. Xidonas, P., Askounis, D., & Psarras, J. (2009). Common stock portfolio selection: A multiple criteria decision-making methodology and an application to the Athens Stock Exchange. Operational Research, 9, 55-79. https://doi.org/10.1007/s12351-008-0027-1.
47. Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2), 397-427. https://doi.org/10.3846/20294913.2011.593291.
48. Zhao, H., & Li, N. (2015). Evaluating the performance of thermal power enterprises using sustainability balanced scorecard, fuzzy Delphic and hybrid multi-criteria decision-making approaches for sustainability. Journal of Cleaner Production, 108, 569-582. https://doi.org/10.1016/j.jclepro.2015.07.141.
49. Zopounidis, C., Galariotis, E., Doumpos, M., Sarri, S., & Andriosopoulos, K. (2015). Multiple criteria decision aiding for finance: An updated bibliographic survey. European Journal of Operational Research, 247(2), 339-348. https://doi.org/10.1016/j.ejor.2015.05.032.
50. Zopounidis, C., Doumpos, M., & Niklis, D. (2018). Financial decision support: An overview of developments and recent trends. EURO Journal on Decision Processes, 6(1-2), 63-76. https://doi.org/10.1007/s40070-018-0078-3.