BIST Leasing–Faktoring Endeksi (XFINK) Kapsamındaki Şirketlerin Finansal Performansının Entropi Yöntemiyle Ağırlıklandırılarak TOPSIS Yöntemiyle İncelenmesi

Öz

Bu çalışma, sekiz XFINK şirketinin 2023 yılı finansal performansını entropi temelli ağırlıklandırma ve TOPSIS yöntemiyle değerlendirmektedir. 2023 verileri, tüm firmalar için en güncel ve tam karşılaştırılabilir finansal tablolar olduğu için kullanılmıştır. 2024 verileri ise raporlamalar tamamlanmadığı ve firmalar arasında tutarlılık sağlanmadığı için dâhil edilmemiştir. Veri seti, KAP ve Finnet’ten elde edilen yıl sonu mali tablolarından oluşmakta; cari oran, F/K, PD/DD, hisse başına kâr ve özkaynak kârlılığı kriterleri kullanılmaktadır. Öncelikle Entropi yöntemiyle kriter ağırlıkları nesnel olarak belirlenmiş, ardından ağırlıklandırılmış karar matrisi TOPSIS yaklaşımıyla analiz edilerek firmaların ideale yakınlık katsayıları hesaplanmıştır. Bulgular, değerleme odaklı göstergelerin (özellikle F/K ve PD/DD) ağırlık yapısına hâkim olduğunu, likidite ve kârlılık oranlarının ise göreli olarak daha düşük ayırt ediciliğe sahip bulunduğunu göstermektedir. Elde edilen sıralama, CRDFA, GARFA ve ISFIN’in üst segmentte konumlandığını, LIDFA’nın ise son sırada yer aldığını ortaya koymaktadır. Çalışma, çok kriterli karar verme temelli bütüncül performans ölçümünün leasing–faktoring alt sektöründe uygulanabilir ve politika yapıcılar için yol gösterici olduğunu ortaya sermektedir.

Anahtar Kelimeler

• Çok kriterli karar verme yöntemleri (ÇKKV)
• Entropi Yöntemi
• TOPSİS
• BIST Leasing Factoring Endeksi

Examination of the Financial Performance of Companies in the BIST Leasing-Factoring Index (XFINK) Weighted with The Entropy Method Using the TOPSIS Method

Abstract

This study evaluates the financial performance of eight XFINK companies for the 2023 fiscal year using entropy-based weighting and the TOPSIS method. The 2023 data were used because they represent the latest complete and comparable financial statements for all firms. The 2024 data were excluded since reporting for that year was not yet finalized or consistent across companies. The data set consists of year-end financial statements obtained from the Public Disclosure Platform (KAP) and the Finnet database. Five indicators are employed to capture the multidimensional structure of performance: current ratio, price-to-earnings (P/E) ratio, market-to-book (M/B) ratio, earnings per share (EPS) and return on equity (ROE). EPS, ROE, and current ratio are modelled as benefit criteria, while P/E and M/B are treated as cost criteria. In the first stage, the entropy method is used to derive objective weights based on the information content and dispersion of each criterion. In the second stage, the weighted normalized decision matrix is analyzed with TOPSIS to compute the relative closeness coefficients of firms to the ideal financial profile. The findings show that valuation-based indicators, particularly P/E and M/B, dominate the weighting structure, whereas liquidity and profitability ratios exhibit lower discriminatory power. The resulting ranking places CRDFA, GARFA and ISFIN in the upper performance tier, while LIDFA occupies the lowest position. Overall, the study demonstrates that entropy–TOPSIS provides a consistent and applicable framework for performance benchmarking in the Turkish leasing–factoring sub-sector.

Keywords

• Multi-criteria decision-making methods (MCDM)
• Entropy Method
• TOPSIS
• BIST Leasing Factoring Index

Kaynakça

1. Acemoglu, D., Ozdaglar, A. & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105 (2), 564–608.
2. Alsanousi, A. T., Alqahtani, A. Y., Makki, A. A. ve 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.
3. Bakhoum, E. S. ve Brown, D. C. (2013). A hybrid approach using AHP–TOPSIS–entropy methods for sustainable ranking of structural materials. International Journal of Sustainable Engineering, 6 (3), 212–224.
4. Basel Committee on Banking Supervision. (2022). CRE34: IRB approach-RWA for purchased receivables. Bank for International Settlements.
5. Barasin, A. M., Alqahtani, A. Y. ve Makki, A. A. (2024). Performance evaluation of retail warehouses: A combined MCDM approach using G-BWM and RATMI. Logistics, 8 (1), 10.
6. Bocok, V., Hinke, J. ve Abrham, J. (2023). Leasing from the perspective of environmental management and its influence on business performance. Frontiers in Environmental Science, 11, 1272816.
7. Cantero-Saiz, M., Sanfilippo-Azofra, S., Torre-Olmo, B., Bringas-Fernández, V. et al. (2025). ESG and bank profitability: The moderating role of country sustainability in developing and developed economies. GF, 7 (2), 288–331.
8. Chakraborty, S. (2022). TOPSIS and modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2, 100021.

9. Chatterjee, S. ve Chakraborty, S. (2024). A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes. Decision Analytics Journal, 11, 100451.
10. Černevičienė, J. ve Kabašinskas, A. (2022). Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Frontiers in Artificial Intelligence, 5. Erişim adresi: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.827584/full
11. Fitch Ratings. (2025a). Turkish leasing and factoring to cope with slowdown in 2025.
12. Fitch Ratings. (2025b). Turkish market volatility could disrupt FIs’ rating momentum. Erişim adresi: https://www.fitchratings.com/research/banks/turkish-market-volatility-could-disrupt-fis-rating-momentum-22-04-2025
13. ING. (2025). Turkish Central Bank raises policy rate and keeps options open for further tightening. ING Think. Erişim adresi: https://think.ing.com/snaps/the-turkish-central-bank-raised-policy-rate-and-kept-flexibility-to-tighten-further/
14. International Monetary Fund, Monetary and Capital Markets Department. (2023). Republic of Türkiye: Financial system stability assessment. Erişim adresi: https://www.elibrary.imf.org/view/journals/002/2023/304/article-A001-en.xml
15. Işık, Ö., Çalık, A. ve Shabir, M. (2025). A consolidated MCDM framework for overall performance assessment of listed insurance companies based on ranking strategies. Comput Econ, 65 (1), 271–312.
16. Karagiannis, R. ve Karagiannis, G. (2020). Constructing composite indicators with Shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.
17. Kaya, A., Pamucar, D., Gürler, H. E. ve Ozcalici, M. (2024). Determining the financial performance of the firms in the Borsa Istanbul sustainability index: Integrating multi criteria decision making methods with simulation. Financial Innovation, 10 (1), 21.
18. Kefe, İ., Evci, S. ve Kefe, İ. (2024). Data on the financial performance of companies on BIST Sustainability 25 Index: An entropy-based TOPSIS approach. Data in Brief, 57, 110959.
19. Khoza, F. (2025). The impact of liquidity and leverage on the financial performance of the Johannesburg Stock Exchange-listed consumer goods firms. Journal of Risk and Financial Management, 18 (9), 510.
20. Lam, W. H., Lam, W. S., Liew, K. F. ve Lee, P. F. (2023). Decision analysis on the financial performance of companies using integrated entropy–fuzzy TOPSIS model. Mathematics, 11 (2), 397.
21. Lim, B., Sotes-Paladino, J., Wang, G. J. ve Yao, Y. (2024). The value of growth: Changes in profitability and future stock returns. Journal of Banking & Finance, 158, 107036.
22. Linh, N. T. D., Son, N. H. ve Thao, D. X. (2025). Evaluating the impact of weighting methods on the stability of scores for alternatives in multi-criteria decision-making problems. Engineering, Technology & Applied Science Research, 15 (1), 19998–20004.
23. Negkakis, I. C. (2025). How capital leases affect firm performance: An analysis in the shipping industry. Journal of Risk and Financial Management, 18 (7), 371.
24. Ogundele, O. S. ve Nzama, L. (2025). Risk management practices and financial performance: Analysing credit and liquidity risk management and disclosures by Nigerian banks. Journal of Risk and Financial Management, 18 (4), 198.
25. Park, C., Son, M., Kim, J., Kim, B., Ahn, Y. ve Kwon, N. (2025). TOPSIS and AHP-based multi-criteria decision-making approach for evaluating redevelopment in old residential projects. Sustainability, 17 (15), 7072.
26. Philippon, T. (2015). Has the US finance industry become less efficient? On the theory and measurement of financial intermediation. American Economic Review, 105 (4), 1408–38.
27. Sabaei, D., Erkoyuncu, J. ve Roy, R. (2015). A review of multi-criteria decision-making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30–35.
28. Sahoo, S. K. ve Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) methods: Advancements, applications, and future directions. Decision Making Advances, 1 (1), 25–48.
29. Shen, Z., Chen, X. ve Garibaldi, J. M. (2020). A novel weighted combination method for feature selection using fuzzy sets. arXiv e-prints. Erişim adresi: https://arxiv.org/abs/2005.05003
30. Sristi, N. A., Zaman, P. B. ve Dhar, N. R. (2024). Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability. Heliyon, 10 (19). Erişim adresi: https://www.cell.com/heliyon/abstract/S2405-8440(24)14330-7
31. Taherdoost, H. ve Madanchian, M. (2024). A comprehensive survey and literature review on TOPSIS. IJSSMET, 15 (1), 1–65.
32. Trading Economics. (2025). Türkiye inflation rate. Erişim adresi: https://tradingeconomics.com/turkey/inflation-cpi
33. Yıldırım, H. (2024). Factoring sector in Türkiye: General overview and evaluation of factoring companies’ financial performance by multi-criteria decision-making techniques. Politik Ekonomik Kuram, 8 (2), 277–302.