EVALUATION OF THE FINANCIAL PERFORMANCES OF TURKISH BASIC METAL INDUSTRY COMPANIES DURING THE COVID-19 PANDEMIC WITH ENTROPY – MARCOS INTEGRATED APPROACH

Abstract

The economies of many countries have come to a standstill due to the contraction in global production caused by COVID-19 epidemic which was declared a pandemic by World Health Organization (WHO) in March 2020. The basic metal industry, which is the locomotive of the Turkish manufacturing sector, is one of the sectors greatly affected by the pandemic. The financial performances of companies in the sector seriously affect the decision-making processes of managers and capital market investors. In this study, the financial performances of 20 firms listed in the BIST Base Metal Index (XMANA) during the pandemic were analyzed. The analysis results of the pandemic period (2020/06 – 2021/06) were compared with the previous one-year period and significant changes were found. For performance measurement, 15 different criteria were determined which were compiled from the balance sheet and income statements, consisting of liquidity, profitability, cost, value, growth, capital structure and activity ratios. In the study, Entropy, which is a multi-criteria decision-making method, was used to calculate the importance weights of the criteria; in addition, the MARCOS Method, a new method proposed in 2020, was used to rank companies according to the determined criteria. According to the analysis results, the metal industry company with the highest financial performance during the pandemic period is ÇEMTAŞ Çelik Makina Sanayi ve Ticaret A.Ş. (ÇEMTS). Kardemir Karabük Demir Çelik Sanayi A.Ş. (KRDMD) and AYES Çelik Hasır ve Çit Sanayi A.Ş. (AYES) followed it, respectively.

Keywords

• COVID-19 Pandemic
• Financial Performance
• Multi-Criteria Decision Making
• Entropy
• MARCOS

TÜRK ANA METAL SANAYİ ŞİRKETLERİNİN COVID-19 PANDEMİSİ DÖNEMİNDEKİ FİNANSAL PERFORMANSLARININ ENTROPİ – MARCOS BÜTÜNLEŞİK YAKLAŞIMI İLE DEĞERLENDİRİLMESİ

Abstract

Dünya Sağlık Örgütü’nün (WHO) Mart 2020’de pandemi olarak ilan ettiği COVID-19 salgınının yol açtığı küresel üretim daralması, birçok ülkenin ekonomisini durma noktasına getirmiştir. Türk imalat sektörünün lokomotifi konumunda olan ana metal sanayisi de pandemiden en çok etkilenen sektörlerin başında gelmektedir. Sektördeki şirketlerin finansal performansları, yöneticilerin ve sermaye piyasası yatırımcılarının karar verme süreçlerini ciddi bir şekilde etkilemektedir. Bu çalışmada Borsa İstanbul Metal Ana Endeksinde (XMANA) yer alan 20 şirketin pandemi dönemi finansal performansı analiz edilmiştir. Pandemi dönemi (2020/06 – 2021/06) analiz sonuçları bir önceki bir yıllık dönem ile karşılaştırılmış ve önemli değişimlere rastlanmıştır. Performans ölçümü amacı ile bilanço ve gelir tablolarından derlenmiş; likidite, kârlılık, maliyet, değer, büyüme, sermaye yapısı ve faaliyet oranlarından oluşan 15 farklı kriter belirlenmiştir. Çalışmada çok kriterli karar verme tekniklerinden Entropi Yöntemi kriterlerin önem ağırlıklarının hesaplanmasında; 2020 yılında geliştirilmiş yeni bir yöntem olan MARCOS Yöntemi ise şirketlerin belirlenen kriterlere göre sıralanmasında kullanılmıştır. Çalışma sonuçlarına göre, pandemi dönemi finansal performansı en yüksek firma olan ÇEMTAŞ Çelik Makina Sanayi ve Ticaret A.Ş. (CEMTS)’yi; sırasıyla Kardemir Karabük Demir Çelik Sanayi ve Ticaret A.Ş. (KRDMD) ve AYES Çelik Hasır ve Çit Sanayi A.Ş. (AYES) takip etmiştir.

Keywords

• COVID-19 Pandemisi
• Finansal Performans
• Çok Kriterli Karar Verme
• Entropi
• MARCOS

References

1. Alimohammadlou, M., Bonyani, A. (2017). A novel hybrid MCDM model for financial performance evaluation in Iran’s food industry. Accounting and Financial Control, 1(2), 38-45.
2. Ayçin, E. (2018). BIST menkul kıymet yatırım ortaklıkları endeksinde (xyort) yer alan işletmelerin finansal performanslarının entropi ve gri ilişkisel analiz bütünleşik yaklaşımı ile değerlendirilmesi. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(2), 595-622.
3. Ayçin, E. (2020). Çok Kriterli Karar Verme (Bilgisayar Uygulamalı Çözümler) (Genişletilmiş ve Güncellenmiş 2. Basım b.). Ankara: Nobel Yayıncılık.
4. Bağcı, H., Kaygın, C. Y. (2020). The financial performance measurement of the companies listed in the BIST holding and investment index by the MCDM methods. Muhasebe ve Finansman Dergisi, Temmuz, 301- 324.
5. Bülbül, S., Köse, A. (2011). Türk gıda şirketlerinin finansal performansının çok amaçlı karar verme yöntemleriyle değerlendirilmesi. Atatürk Ü. İİBF Dergisi, 10. Ekonometri ve İstatistik Sempozyumu Özel Sayısı, 71-97.
6. Çanakçıoğlu, M. (2019). Evaluation of banking performance of the balkan countries with an ıntegrated MCDM approach consist of Entropy And Ocra techniques. Economy & Business Journal,, 13(1), 341-366.
7. Dwivedi, R., Kanika, P., Jha, P. K., Singh, S. (2020). An Integrated CRITIC-MARCOS Technique for Analysing The Performance of Steel Industry. Editors: K. Kalita, R. K. Ghadai, X.-Z. Gao, Data-Driven Optimization of Manufacturing Processes (s. 115-127). USA: IGI Global.
8. Ecer, F. (2020). Çok Kriterli Karar Verme (Geçmişten Günümüze Kapsamlı Bir Yaklaşım). Ankara: Seçkin Kitabevi. Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 1-19.

9. Ecer, F., Pamucar, D. (2021). MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Applied Soft Computing, 104, 1-18.
10. Edirisinghe, N. C., 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.
11. Erdoğan, H. H., Kırbaç, G. (2021). Financial performance measurement of logistics companies based on Entropy and Waspas methods. Journal of Business Research-Turk (İşletme Araştırmaları Dergisi), 13(2), 1093-1106.
12. Gartner, I. R. (2015). Multi-attribute Utility Model Based on the Maximum Entropy Principle Applied in the Evaluation of the Financial Performance of Brazilian Banks. Editor: P. Guarnieri, Decision Models in Engineering and Management (s. 29-55). Brasil: Springer International Publishing.
13. Ghadikolaei, A. S., Esbouei, S. K., Antucheviciene, J. (2014). Applying fuzzy MCDM for financial performance evaluation of Iranian companies. Technological and Economic Development of Economy , 274-291.
14. Ho, C.-T. (2006). Measuring bank operations performance: an approach based on Grey Relation Analysis. Journal of the Operational Research Society, 57(4), 337-349.
15. Ho, C.-T., & Wu, Y.-S. (2006). Benchmarking performance indicators for banks. Benchmarking An International Journal, 13(1/2), 147-159.
16. 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.
17. Kalogeras, N., Baourakis, G., Zopounidis, C., Dijk, G. V. (2005). Evaluating the financial performance of agrifood firms: a multicriteria decision aid approach. Journal of Food Engineering, 70, 365-371.
18. Kehribar, Ö., Karademir, F., Evci, S. (2021). İşletmelerin COVID-19 pandemisi sürecindeki finansal performanslarının Entropi ve MAIRCA yöntemleri ile değerlendirilmesi: BIST gıda, içecek endeksi örneği. Business & Management Studies: An International Journal,, 9(4), 200-214.
19. Lam, W. S., Liew, K. F., Lam, W. H.(2019). Investigation on the Performance of construction companies in Malaysia with Entropy-TOPSIS model. IOP Conference Series: Earth and Environmental Science, 1-7.
20. Lee, P. T.-W., Lin, C.-W., Shin, S.-H. (2012). A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis. Expert Systems with Applications, 39(5), 5649-5657.
21. Park, K., Jang, S. (. (2012). Effect of diversification on firm performance: Application of the entropy measure. International Journal of Hospitality Management, 31(1), 218-228.
22. Sanayi ve Teknoloji Bakanlığı (2020). Demir Çelik Sektör Raporu.
23. Sarsour, N., Dağlı, H., Perçin, S. (2020). Financial performance evaluation using fuzzy GRA and fuzzy Entropy methods: wholesale and retail industry. International Journal of Economic and Administrative Studies, 28, 47-64.
24. Sharma, A., Kaur, G., Bansal, J. (2018). A comparative analysis of Promethee,AHP and Topsis aiding in financial analysis of firm performance. Proceedings of The First International Conference On Information Technology and Knowledge Management, 14, 145-150.
25. Stevic, Ž., Brkovic, N. (2020). A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company. Logistics, 4(4), 1-14.
26. Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 40, 1-15.
27. Wang, T.-C., Lee, H.-D. (2009). Developing a fuzzy Topsis approach based on subjective weights and objective weights. Expert Systems With Applications, 36(5), 8980-8985.
28. 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, 350-364.
29. Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on ımproved Entropy weight method. Scientific World Journa, 1-7.