Profitability Based Financial Performance Analysis in BIST Basic Metal Industry Sector: LOPCOW-RSMVC Hybrid Multi-Criteria Decision Making Model

Abstract

In this study, it is aimed to measure the financial performance of the companies traded in the BIST basic metal industry sector for the period 2020-2023 by using MCDM methods. In the study, five profitability criteria obtained from the balance sheets and income statements of the companies were included, and the importance levels of the criteria were determined by the LOPCOW method and the performance scores of the alternatives were determined by the RSMVC method. According to the LOPCOW method, the criterion with the highest degree of importance was found to be return on equity for 2020 and 2021, EBITDA margin for 2022 and net profit margin for 2023. The criterion with the lowest degree of importance is EBITDA margin for 2020, net profit margin for 2021 and 2022 and gross profit margin for 2023. According to the RSMVC technique, when the four periods are evaluated in general, CEMTS, PNLSN, KCAER companies ranked in the first three places in terms of average rank values, while CELHA, BMSCH, MEGMT companies ranked in the last three places.

Keywords

• BIST Basic Metal Industry
• LOPCOW
• RSMVC
• Multi-Criteria Decision Making

BİST Ana Metal Sanayi Sektöründe Kârlılığa Dayalı Finansal Performans Analizi: LOPCOW-RSMVC Hibrit Çok Kriterli Karar Verme Modeli

Öz

Bu araştırmada, BİST ana metal sanayi sektöründe işlem gören şirketlerin 2020-2023 dönemi finansal performansının ÇKKV yöntemleri ile ölçülmesi amaçlanmıştır. Şirketlerin bilanço ve gelir tablolarından elde edilen beş kârlılık kriterinin yer verildiği araştırmada, kriterlerin önem dereceleri LOPCOW yöntemi ile alternatiflerin performans skorları ise RSMVC yöntemi ile belirlenmiştir. LOPCOW yöntemine göre önem derecesi en yüksek kriter 2020 ve 2021 yılları için özsermaye kârlılığı, 2022 yılı için FAVÖK marjı ve 2023 yılı için net kâr marjı olduğu görülmüştür. En düşük önem derecesine sahip kriter 2020 yılı için FAVÖK marjı, 2021 ve 2022 yılları için net kâr marjı ve 2023 yılı için ise brüt kâr marjı olarak tespit edilmiştir. RSMVC tekniğine göre dört dönem genel olarak değerlendirildiğinde ortalama sıra değerleri bakımından CEMTS, PNLSN, KCAER şirketleri ilk üç sırada yer alırken, CELHA, BMSCH, MEGMT şirketleri son üç sırada yer almıştır.

Anahtar Kelimeler

• BİST Ana Metal Sanayi
• LOPCOW
• RSMVC
• Çok Kriterli Karar Verme.

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. Acar, E., & Sarıyer, G. (2021). Financial performance evaluation of Turkish basic metal industry: combining AHP and TOPSIS. International Journal of Economics and Administrative Studies, 31, 113-128. https://doi.org/10.18092/ulikidince.734976
3. Akdogan, K., Chadwick, M. G., Saygili, H, & Saygili, S. (2019). Trends in Basic Metal Industry Exports (No. 1914). Research and Monetary Policy Department, Central Bank of the Republic of Türkiye.
4. Akyüz, Y., Bozdoğan, T., & Hantekin, E. (2011). Evaluation of financial performance with TOPSIS method and an application. Afyon Kocatepe University Journal of Economics and Administrative Sciences, 13(1), 73-92.
5. Alon, I. (2020). COVID-19 and international business: A viewpoint. FIIB Business Review, 9(2), 75-77. https://doi.org/10.1177/2319714520923579
6. Alsanousi, A. T., Alqahtani, A. Y., Makki, A. A., & Baghdadi, M. A. (2024). A hybrid mcdm approach using the bwm and the topsis for a financial performance-based evaluation of saudi stocks. Information, 15(5), 258. https://doi.org/10.3390/info15050258
7. Arslan Gürdal, H., & Durmuş, H. (2024). Evaluation of the performances of iron-steel companies traded in borsa istanbul using gray relational analysis. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 19(1), 316-327. https://doi.org/10.17153/oguiibf.1360110
8. Asgharpour, M.J. (1999). Multiple Criteria Decision Making. Tehran: Tehran University Publications.

9. Asker, V. (2024). The Effect of Kahramanmaras Earthquakes on Financial Performance: An Application in Companies Located in the Earthquake Area. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 19(3), 713-725. https://doi.org/10.17153/oguiibf.1420199
10. Aytekin, A. (2021). Comparative analysis of the normalization techniques in the context of MCDM problems. Decision Making: Applications in Management and Engineering, 4(2), 1-25. https://doi.org/10.31181/dmame210402001a
11. Bakirci, F., Shiraz, S. E., & Sattary, A. (2014). Financial performance analysis of iron, steel metal industry sector companies in the borsa istanbul: DEA super efficiency and TOPSIS methods. Ege Academic Review, 14(1), 9-19.
12. Bektaş, S. (2022). Evaluating the performance of the turkish insuranc sector for the period 2002-2021 with merec, lopcow, cocoso, edas ckkv methods. Journal of BRSA Banking and Financial Markets, 16(2), 247-283. https://doi.org/10.46520/bddkdergisi.1178359
13. Biswas, S., & Joshi, N. (2023). A performance based ranking of initial public offerings (IPOs) in India. Journal of Decision Analytics and Intelligent Computing, 3(1), 15-32. https://doi.org/10.31181/10023022023b
14. Biswas, S., Bandyopadhyay, G., & Mukhopadhyaya, J. N. (2022). A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian FMCG and consumer durable sector. Decision Making: Applications in Management and Engineering, 5(2), 140-175. https://doi.org/10.31181/dmame0306102022b
15. Çanakçıoğlu, M. (2020). Evaluation of financial performance of the base metal companies operand in the istanbul stock exchange using an integrated multicriteria decision-making model. Journal of Management and Economics Research, 18(2), 176-197. https://doi.org/10.11611/yead.678063
16. Çelebi Demirarslan, P., Sönmez Çakır, F., & Akansel, I. (2024). Ranking the quality of life indexes by years in Asian countries using multi-criteria decision-making methods. Asia-Pacific Journal of Regional Science, 8(3), 911-942. https://doi.org/10.1007/s41685-024-00350-w
17. Çolak, Z. (2023). Determination of financial performance rankings of the enterprises in the bist basic metal index (xmana) with topsis method. Başkent University Journal of Faculty of Commercial Sciences, 7(1), 1-20.
18. Demir, G. (2022). Evaluation of the development of informatıon and communicatıon technology in the G8 countries. Innovative ideas, 165, 61-89. http://dx.doi.org/10.29228/jobacs.67424
19. Dhruva, S., Krishankumar, R., Zavadskas, E. K., Ravichandran, K. S., & Gandomi, A. H. (2024). Selection of suitable cloud vendors for health centre: a personalized decision framework with fermatean fuzzy set, LOPCOW, and CoCoSo. Informatica, 35(1), 65-98. https://doi.org/10.15388/23-INFOR537
20. Dwivedi, R., Prasad, K., Jha, P. K., & Singh, S. (2021). An integrated CRITIC-MARCOS technique for analysing the performance of steel industry. In Data-Driven Optimization of Manufacturing Processes (pp. 115-127). IGI Global.
21. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
22. Ersoy, N. (2023). Performance Measurement in the BIST Retail and Trade Sector with the LOPCOW-RSMVC Model. Sosyoekonomi, 31(57), 419-436. https://doi.org/10.17233/sosyoekonomi.2023.03.20
23. Eş, A., & Çobanoğlu, C. (2017). A framework for company performance ranking by topsis method: an application in iron and steel industry. Cumhuriyet University Journal of Economics and Administrative Sciences, 18(2), 249-268.
24. Eyüboğlu, K., & Bayraktar, Y. (2019). Evaluation of financial performances in terms of sub-sectors of basic metal industry with ahp and topsis methods. Bulletin of Accounting and Finance Reviews, 2(1), 1-10. https://doi.org/10.32951/mufider.443508
25. Gönüllü, O. (2022). Evaluation of the financial performances of turkish basic metal industry companies during the covid-19 pandemic with entropy – marcos integrated approach. Marmara University Journal of Economic and Administrative Sciences, 44(1), 53-77. https://doi.org/10.14780/muiibd.1135471
26. Goodridge, W. S. (2016). Sensitivity analysis using simple additive weighting method. International Journal of Intelligent Systems and Applications, 8(5), 27. https://doi.org/10.5815/ijisa.2016.05.04
27. Goswami, S. S., Behera, D. K., Afzal, A., Razak Kaladgi, A., Khan, S. A., Rajendran, P., ..., & Asif, M. (2021). Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS. Symmetry, 13(8), 1331. https://doi.org/10.3390/sym13081331
28. Güçlü, P., & Muzac, G. (2024). Multi-period multi-criteria decision making with extended grey multimoora method: the case of financial performance evaluation in iron and steel industry. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 19(1), 267-291. https://doi.org/10.17153/oguiibf.1373450
29. Gülcemal, T., & İzci, A. Ç. (2023). Analyzing the performance of the turkish participation banking sector using the lopcow-moosra model. Dogus University Journal, 25(1), 115-134. https://doi.org/10.31671/doujournal.1287120
30. Hafezparast, M., Araghinejad, S., & Filatova, T. (2015). Comparing the subjective and the objective criteria weighting in agricultural water resources management. Hydrology, 3(4), 38-46. https://doi.org/10.11648/j.hyd.20150304.11
31. Hong-yi, L., Chu, Z., & Di, Z. H. A. O. (2010). Stock investment value analysis model based on AHP and gray relational degree. Management Science and Engineering, 4(4), 1-6.
32. Kahraman, Y. E. (2023). The effect of profitability on business success in bist forest, paper, printing index. Third Sector Social Economic Review, 58(3), 2125-2143. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.23.08.2161
33. Kahreman, Y. (2023). Evaluation of the economic performances of g20 countries in the period of the 2008 crisis with lopcow-cocoso method. İzmir Journal of Economics, 38(3), 786-803. https://doi.org/10.24988/ije.1232306
34. Karande, P., Zavadskas, E., & Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), 399-422. https://doi.org/10.5267/j.ijiec.2016.1.001
35. Keleş, N., & Ersoy, N. (2023). Analyzing climate change performance over the last five years of G20 countries using a multi-criteria decision-making framework. Dokuz Eylül University Journal of Faculty of Business, 24(2), 13-34. https://doi.org/10.24889/ifede.1284974
36. Korkmaz, K., & Öztel, A. (2020). Measurement of financial performance of enterprises operating in bist main metal industry index by entropy-based promethee method: 2014-2018 period. Journal of Management Economics Literature Islamic and Political Sciences, 5(2), 57-85. https://doi.org/10.24013/jomelips.816956
37. Lam, W. H., Lam, W. S., Liew, K. F., & Lee, P. F. (2023). Decision analysis on the financial performance of companies using integrated entropy-fuzzy topsis model. Mathematics, 11(2), 397. https://doi.org/10.3390/math11020397
38. Lu, L., Peng, J., Wu, J., & Lu, Y. (2021). Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: Evidence from Sichuan, China. International Journal of Disaster Risk Reduction, 55, 102085. https://doi.org/10.1016/j.ijdrr.2021.102085
39. Makki, A. A., & Alqahtani, A. Y. (2023). Capturing the effect of the COVID-19 pandemic outbreak on the financial performance disparities in the energy sector: A Hybrid MCDM-Based evaluation approach. Economies, 11(2), 61. https://doi.org/10.3390/economies11020061
40. Marqués, A. I., García, V., & Sánchez, J. S. (2020). Ranking-based MCDM models in financial management applications: analysis and emerging challenges. Progress in Artificial Intelligence, 9(3), 171-193. https://doi.org/10.1007/s13748-020-00207-1
41. Mathew, M., & Sahu, S. (2018). Comparison of new multi-criteria decision making methods for material handling equipment selection. Management Science Letters, 8(3), 139-150. https://doi.org/10.5267/j.msl.2018.1.004
42. Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Structural and multidisciplinary optimization, 29, 312-318. https://doi.org/10.1007/s00158-004-0473-1
43. Mojtaba, F., Hossein, H., & Hamid J. (2016). Two Comparative MCDM Approaches for Evaluating The Fınancial Performance Of Iranian Basic Metals Companies. Iranian Journal of Management Studies, 9(2), 359-382.
44. Nguyen, H. Q., Le, X. H., Nguyen, T. T., Tran, Q. H., & Vu, N. P. (2022). A comparative study on multi-criteria decision-making in dressing process for internal grinding. Machines, 10(5), 303. https://doi.org/10.3390/machines10050303
45. Özcan, A., & Ömürbek, N. (2020). Evaluation of performance of an iron-steel operation by multi criteria decision making methods. IBAD Journal Of Social Sciences, 8, 77-98. https://doi.org/10.21733/ibad.714295
46. Özden, A., & Haçikoğlu, M. (2017). Basic metal industry sector. A&T Bank Economic Research Department. Retrieved from https://www.atbank.com.tr/Documents/Features/ATB_ana-metal-sanayı-sektoru_eylul-2017-pdf_128.PDF
47. Özekenci, E. K. (2024). İnternational market selection based on integrated mcdm methods: a case study of iron and steel sector. Journal of Selçuk University Social Sciences Vocational School, 27(1), 274-293. https://doi.org/10.29249/selcuksbmyd.1440711
48. Öztaş, T., & Öztaş, G. Z. (2024). İnnovation performance analysis of G20 countries: a novel integrated lopcow-mairca mcdm approach including the covid-19 period. Journal of Productivity, 1-20. https://doi.org/10.51551/verimlilik.1320794
49. Public Disclosure Platform (2024). Sectors. Retrieved from https://www.kap.org.tr/en/Sektorler
50. Raikar, A. V. (2019a). An assessment of performance of steel companies in india using promethee method of multi criteria decision technique. International Journal of Management Studies, 6(4), 46-56. http://dx.doi.org/10.18843/ijms/v6si4/06
51. Raikar, A. V. (2019b). Financial performance evaluation of selected steel companies in india by using multi criteria decision technique of aras, saw and topsis with dva based weight determination. International Journal of Scientific Research and Reviews, 8(2), 4261-4277.
52. Rong, Y., Yu, L., Liu, Y., Simic, V., & Garg, H. (2024). The FMEA model based on LOPCOW-ARAS methods with interval-valued Fermatean fuzzy information for risk assessment of R&D projects in industrial robot offline programming systems. Computational and Applied Mathematics, 43(1), 25. https://doi.org/10.1007/s40314-023-02532-2
53. Şit, A., Ekşi, İ. H., & Hacıevliyagil, N. (2017). Financial performance measurement of enterprises operating in bist main metal industry index: the period of 2011-2015. Süleyman Demirel University Visionary Journal, 8(17), 83-91. https://doi.org/10.21076/vizyoner.284906
54. Söylemez, Y. (2020). Comparison of topsis and gray relative analysis methods in financial performance evaluation. Journal of Management and Economics Research, 18(3), 61-79. http://dx.doi.org/10.11611/yead.771575
55. Sumrit, D., & Keeratibhubordee, J. (2024). Risk assessment framework for reverse logistics in waste plastic recycle industry: a hybrid approach incorporating fmea decision model with ahp-lopcow- aras under trapezoidal fuzzy set. Decision Making: Applications in Management and Engineering, 8(1), 42–81. https://doi.org/10.31181/dmame812025984
56. Taşcı, M. Z. (2023). Performance analysis with market multipliers: an application on bist sigorta companies. Nevşehir Hacı Bektaş Veli University Journal of ISS, 13(2), 1211-1224. https://doi.org/10.30783/nevsosbilen.1277228 Tavana, M., Khalili-Damghani, K., & Rahmatian, R. (2015). A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies. Annals of Operations Research, 226, 589-621. https://doi.org/10.1007/s10479-014-1738-8
57. Thinh, H. X., & Van Dua, T. (2023). RSMVC: A new-simple method to select the cutting tool base on multi criteria. Journal of Applied Engineering Science, 21(1), 167-175. https://doi.org/10.5937/jaes0-39772
58. Türkiye Exporters Association (2024). Export Figures. Retrieved from https://tim.org.tr/tr/ihracat-rakamlari Türkiye Steel Association (2024). Türkiye Steel Industry Statistics. Retrieved from https://www.cib.org.tr/tr/istatistikler.html
59. Ulutaş, A., Balo, F., & Topal, A. (2023). Identifying the most efficient natural fibre for common commercial building insulation materials with an integrated PSI, MEREC, LOPCOW and MCRAT model. Polymers, 15(6), 1500. https://doi.org/10.3390/polym15061500
60. Uygurtürk, H., & Korkmaz, T. (2012). The determınatıon of fınancıal performance wıth topsıs multıple crıterıa decısıon makıng method: an applıcatıon on basıc metal industry enterprıses. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 7(2), 95-115.
61. World Steel Associaiton (2024). World Steel in Figures 2024. Retrieved from https://worldsteel.org/wp-content/uploads/World-Steel-in-Figures-2024.pdf
62. World Steel Association (2013). The Steel Story. Retrieved from https://worldsteel.org/wp-content/uploads/The-Steel-Story-1.pdf
63. World Steel Association (2019). The Role of Steel Manufacturing in the Global Economy. Retrieved from https://worldsteel.org/wp-content/uploads/The-role-of-steel-manufacturing-in-the-global-economy.pdf
64. Yıldırım, M., Bal, K., & Doğan, M. (2021). Financial performance analysis by grey relational analysis method: an application on iron and steel companies traded in bist. The World of Accounting Science, 23(1), 122-143. https://doi.org/10.31460/mbdd.788840
65. Yıldırım, M., Karakaya, Ö., & Altan, İ. M. (2019). Measurement of financial performance by using cost and profitability ratios in topsis method: the case of a company in the iron and steel industry. Gazi Journal of Economics and Business, 5(3), 170-181. https://doi.org/10.30855/gjeb.2019.5.3.003
66. Yılmaz Özekenci, S. (2024). Financial performance analysis of bist energy index companies with lopcow-critic based cocoso methods. Journal of Selçuk University Social Sciences Vocational School, 27(1), 48-64. https://doi.org/10.29249/selcuksbmyd.1400056
67. Yılmaz, N. (2023). An integrated lopcow-wisp model for analyzıng performance of banking sector in romania. In H. Akgün, S. Sağlık and U. İnan (Eds.), Academic Studies in Social, Human and Administrative Sciences (pp. 161-176). Ankara: Gece Publishing.
68. Zavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology & Decision Making, 15(02), 267-283. https://doi.org/10.1142/S0219622016500036
69. Zavadskas, E. K., & Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19(2), 303-314.
70. Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of eco-environmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014(1), 797814. https://doi.org/10.1155/2014/797814
71. Žižović, M., Miljković, B., & Marinković, D. (2020). Objective methods for determining criteria weight coefficients: A modification of the CRITIC method. Decision Making: Applications in Management and Engineering, 3(2), 149-161. https://doi.org/10.31181/dmame2003149z