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

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