Financial Performance Analysis Using Entropy-Based TOPSIS and EDAS Methods: Evidence from the BIST Logistics Sector

Abstract

Purpose – This study aims to comparatively evaluate the financial performance of firms operating in the BIST Logistics sector through Entropy-based TOPSIS and EDAS methods, to identify the effect of criterion weights on firm rankings, and to provide a multidimensional performance assessment framework for decision makers in the transportation and logistics sector. Beyond ranking firms, the study discusses why ranking differences emerge across financial indicators and MCDM assumptions.

Design/methodology/approach – The analysis uses publicly available 2024 and 2025 financial data for 13 listed logistics/transportation firms. Data were compiled from KAP, Matriks, and İş Yatırım platforms. Eight criteria are considered: current ratio, quick ratio, return on equity, change in equity, price-to-earnings ratio, price-to-book ratio, short-term liabilities/total liabilities, and total liabilities/equity. The benefit/cost direction of each criterion is explicitly classified, and negative financial values are interpreted as loss or volatility signals particularly for profitability and valuation multiples. Criterion weights are obtained through Entropy, and firm rankings are produced using TOPSIS and EDAS. Method agreement is additionally assessed by Spearman rank correlation and Kendall Tau coefficients.

Findings – Entropy-based TOPSIS ranks TUREX, GRSEL, and TLMAN as the top three firms in 2024, while GRSEL, PGSUS, and CLEBI lead in 2025. EDAS ranks TUREX, TLMAN, and GRSEL in 2024, and TUREX, TLMAN, and GRSEL in 2025. The findings indicate that performance rankings are sensitive to the selected MCDM method and that outliers in P/E, P/B, and leverage indicators can materially affect ranking results.

Discussion – The study shows that financial performance in the BIST logistics sector cannot be explained only by firm size; liquidity, profitability, valuation multiples, and leverage structure should be evaluated jointly. Differences between TOPSIS and EDAS results demonstrate that distance from the ideal solution and distance from the average solution may lead to different decision outputs. Therefore, the study offers investors, analysts, and managers a decision-support perspective that explicitly considers method sensitivity.