Development the Novel FCF-SIWEC-RBNAR Hybrid Method for
Financial Performance Evaluation
Hamide Özyürek
1a
, Galip Cihan Yalçın
1b
and Karahan Kara
2c
1
Department of Business, Faculty of Economics and Administrative Sciences, OSTIM Technical University, Ankara, Turkey
2
Department of Business, Faculty of Economics and Administrative Sciences, OSTIM Technical University, İzmir, Turkey
Keywords: Financial Performance Analysis, Fermatean Cubic Fuzzy Sets, Simple Weight Calculation, Reference-Based
Normalization Alternative Ranking.
Abstract: Financial performance analyses are fundamental tools that provide insights into companies' financial
conditions. The primary aim of this study is to develop a financial performance analysis method as a decision
support system. In this context, the FCF-SIWEC-RBNAR (Fermatean Cubic Fuzzy- Simple Weight
Calculation- Reference-Based Normalization Alternative Ranking) hybrid method was developed. In this
method, expert weights are determined using FCF sets, while the weights of criteria are calculated using the
FCF-SIWEC approach based on expert evaluations. Companies are then ranked according to their financial
performance using the RBNAR method. To demonstrate the applicability of the proposed hybrid method, four
case studies were conducted using data from 50 companies operating on Borsa Istanbul for the years 2020,
2021, 2022, and 2023. As a result of the research, the "Debt-to-Equity Ratio" was identified as the most
significant financial criterion. Additionally, the financial performance rankings of companies were
determined for each year. These findings support that the FCF-SIWEC-RBNAR hybrid method is a robust
and applicable approach for financial performance evaluation.
1 INTRODUCTION
The most important activities of managers are
planning (Snyder & Glueck, 2019), implementation,
and control (Alipour et al., 2013; Wu et al., 2005). In
carrying out these activities, managers rely on
performance reports generated by management
accountants. Management accountants, in turn,
analyse the financial statements produced by the
accounting information system and provide
information to users by making these reports
applicable to management activities (Hadid & Al-
Sayed, 2021; Zhao & Yu, 2025). The standard
preparation of the generated information allows for
internal comparisons within firms over time and
external comparisons with other firms. According to
IFRS, which ensures this standard, financial
statements include the balance sheet, income
statement, and cash flow statement (Lopes & Penela,
2025). These financial statements enable the
a
https://orcid.org/0000-0002-2574-954X
b
https://orcid.org/0000-0001-9348-0709
c
https://orcid.org/0000-0002-1359-0244
measurement of a company's liquidity, profitability,
debt repayment capacity, and asset efficiency.
The financial statements produced by the
accounting information system are used by both
internal and external stakeholders in the decision-
making process (Tran Thanh Thuy, 2025). The
accuracy of decisions relies on making plans with
forecasts that ensure the sustainability of firms and on
company comparisons (Farshadfar et al., 2025), which
are facilitated by reports that are accurate, timely, and
tailored to needs. However, simply preparing reports
does not help in making accurate decisions; it is also
essential to analyse the reports with the correct
indicators. Financial ratios are the most important
tools in firm performance analysis.
Financial ratios are essential tools used to measure
key performance indicators such as liquidity,
profitability, debt levels, and operational efficiency of
companies. These ratios provide investors, creditors,
and company managers with insights into a company's
Özyürek, H., Yalçın, G. C., Kara and K.
Development the Novel FCF-SIWEC-RBNAR Hybrid Method for Financial Performance Evaluation.
DOI: 10.5220/0013462100003967
In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 53-63
ISBN: 978-989-758-758-0; ISSN: 2184-285X
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
53
financial health while also revealing potential risks
and opportunities (Llorent-Jurado et al., 2024).
Liquidity ratios assess a company's ability to meet
short-term debt obligations, while profitability ratios
indicate the efficiency with which a company
generates income. Ratios that evaluate debt repayment
capacity help in understanding the extent to which a
company can sustainably meet its debt obligations.
The accurate calculation and interpretation of
financial ratios are crucial for reflecting the true
financial condition of a company. However,
misinterpretation or manipulation of these ratios can
lead to misleading results and conceal the company's
actual financial situation. Therefore, financial
analyses must be conducted meticulously, and all
ratios should be evaluated within their proper context.
Financial ratios play a critical role in evaluating
financial performance. In this context, the ratios
selected in this study, which are commonly used in the
literature, serve as significant indicators for assessing
a firm's financial performance. The financial
indicators added to the decision-making model in this
study include widely used ratios such as Return on
Equity (ROE) (Alsanousi et al., 2024; Qureshi et al.,
2021; Gutiérrez-Ponce & Wibowo, 2023; Rocha et al.,
2024), Return on Assets (ROA) (Loan et al., 2024;
Deb et al., 2024; Veeravel et al., 2024), Leverage
(Giannopoulos et al., 2022), Debt-to-Equity (Kara et
al., 2024; Lam et al., 2023; Abdel-Basset et al., 2020),
Operating Profit Margin (Mao, 2024), Pre-tax Profit
Margin (Kaya et al., 2024), and Net Profit Margin
(Çetin et al., 2024), all of which are instrumental in
evaluating a company's profitability, efficiency, and
debt repayment capacity.
These indicators enable a comprehensive
evaluation in financial analysis, helping us better
understand the performance of companies.
Accurately analyzing a company's financial
performance is not only crucial for understanding its
current financial position but also plays a significant
role in evaluating its future growth potential. Financial
performance reflects a company's efficiency in
obtaining, managing, and utilizing capital, which is
one of the key indicators of its financial health during
a specific period (Chrysafis, 2024). Regardless of the
sector, size, or geographical location, companies must
develop strategies based on effective financial
performance analysis to sustain long-term success and
maintain competitive advantages. In this context,
financial analyses are indispensable tools for
examining a company's financial condition in-depth
and formulating sound strategies for the future. It is
also important to remember that financial performance
is not limited to profitability; it is deeply intertwined
with organizational structures, strategic objectives,
and external environmental factors (Khizar et al.,
2024). Properly analysing financial performance
enables companies to establish a solid foundation
when making strategic decisions and contributes to
their success in competitive markets. Financial
performance analysis is of great importance not only
for internal management but also for investors,
creditors, and other external stakeholders (Akisik &
Gal, 2017).
Financial performance analysis is not only
essential for understanding a company’s current
position but also holds significant value in facilitating
the formulation of strategic decisions for the future. In
this regard, multi-criteria decision-making (MCDM)
methods, employed to assess the financial
performance of companies across diverse sectors,
enable a comprehensive and detailed analysis of
financial data. Tan et al. (2025) employed the SOCP-
MCDM method to assess the financial performance of
companies, utilizing a robust multi-criteria decision-
making framework that accounts for various
performance indicators. In a similar vein, Işık et al.
(2025) utilized the F-LBWAF-LMAW-MARCOS
method, integrating fuzzy logic with multiple
decision-making approaches to provide a nuanced
analysis of financial health across different firms.
These methodologies demonstrate the growing
application of advanced techniques in evaluating
financial performance, highlighting the need for
comprehensive approaches in contemporary business
analysis.Özekenci (2024) conducted financial
performance analyses using LBWA, MEREC, and
CRADIS methods on the BIST Sustainability 25
Index. Alsanousi et al. (2024) performed a financial
performance evaluation using BWM and TOPSIS in
the Saudi Stock Market. Kaya et al. (2024) ranked the
performance of companies in the Borsa Istanbul
Sustainability Index by applying FUCOM and
Copeland methods. Barutbas et al. (2024) examined
the financial performance of companies in the retail
sector using DF TOPSIS and fuzzy clustering
methods. Ghaemi-Zadeh et al. (2024) utilized D-
CRITIC, TOPSIS, and VIKOR methods for financial
performance analysis in the Tehran Stock Exchange.
Isık et al. (2024) performed financial performance
assessments in the insurance sector using PFAHP and
MAIRCA methods. Sharma & Kumar (2024)
conducted financial performance analysis in the
banking sector by employing entropy, TOPSIS, and
VIKOR methods. Liou et al. (2024) investigated the
effects of COVID-19 in the aviation sector using
DEMATEL. Ergülen & Çalik (2024) evaluated
financial performance changes during and before the
DATA 2025 - 14th International Conference on Data Science, Technology and Applications
54
pandemic in the Turkish industrial sector using F-
BWM and MARCOS methods. Kara et al. (2024)
applied SVN-CIMAS-CRITIC-RBNAR methods for
financial performance evaluations in the Borsa
Istanbul Technology sector.
The main motivation of this study is to develop a
decision support system for financial performance
evaluation and to demonstrate its applicability. In this
context, the FCF-SIWEC-RBNAR (Fermatean Cubic
Fuzzy- Simple Weight Calculation- Reference-Based
Normalization Alternative Ranking) hybrid method
has been developed. This hybrid method facilitates the
identification of experts' influence levels in the
decision-making process using FCF (Wang et al.,
2024).). It also enables the determination of the
weights of criteria (financial ratios) through the FCF-
SIWEC method (Puška et al., 2024). Furthermore, it
allows for the ranking of companies based on their
financial performance using the RBNAR method
(Kara et al., 2024). In this study, the financial
performance levels of 50 companies operating on
Borsa Istanbul for the years 2020, 2021, 2022, and
2023 were determined using the FCF-SIWEC-
RBNAR hybrid method.
2 METHODOLOGY
In this study, a hybrid method combining the FCF-
SIWEW-RBNAR approach has been developed to
evaluate the financial performance of companies.
This hybrid method is implemented in three stages. In
Stage 1, the weights of the experts determining the
importance levels of the criteria are calculated. In
Stage 2, the weights of the criteria are determined.
Finally, in Stage 3, the companies are ranked based
on their financial performance. In the methodology
section, the fundamental definitions of FCF are first
presented. Subsequently, the steps of the FCF-
SIWEC-RBNAR hybrid method are outlined.
2.1 Preliminaries of Fermatean Cubic
Fuzzy (FCF) Sets
Definition 1: In the context of the discourse denoted
as 𝔇 , 𝔅
is defined as the set
𝔅
=
〈
𝔡,𝛾
𝔅
(
𝔡
)
,𝛾
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
,𝛾
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
| 𝔡∈
𝔇
〉
. This formulation represents a systematically
structured collection of FCF sets associated with each
element 𝔡 within the set 𝔇. It is crucial to highlight
that this function definition is governed by the
following constraints:
0≤𝛾
𝔅
(
𝔡
)
+𝛾
𝔅
(
𝔡
)
≤1
and 0≤
𝛿
𝔅
(
𝔡
)
+𝛿
𝔅
(
𝔡
)
≤1 and 0≤𝛾
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
≤1 and 0≤
𝛾
𝔅
(
𝔡
)
+
𝛿
𝔅
(
𝔡
)
≤1 and 0≤
𝛾
𝔅
(
𝔡
)
+
𝛿
𝔅
(
𝔡
)
≤1 and for each element 𝓆 in the set 𝒬
(Wang et al., 2024).
Definition 2: Consider
𝔅
=
〈
𝔡,𝛾
𝔅
(
𝔡
)
,𝛾
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
,𝛾
𝔅
(
𝔡
)
,𝛿
𝔅
(
𝔡
)
| 𝔡∈𝔇
〉
is FCF set related to each element 𝔡 within set 𝔇. The
score function 𝑆𝑐𝔅
can be calculated by
employing Eq. (1) (Wang et al., 2024).
𝑆
𝑐𝔅
=
𝛾
𝔅
(
𝔡
)
+
𝛾
𝔅
(
𝔡
)
−
𝛿
𝔅
(
𝔡
)
−
𝛿
𝔅
(
𝔡
)
+
𝛾
𝔅
(
𝔡
)
−
𝛿
𝔅
(
𝔡
)
+1.
(1
)
2.2 The FCF-SIWEC-RBNAR Hybrid
Method
The FCF-SIWEC-RBNAR hybrid method is
developed to evaluate the financial performance of
companies. Let
∃=
∃
,∃
,…,∃
,…,∃
(𝑘=
1,2,…,𝐾)
shows the experts, ℘=
℘
,℘
,…,℘
,…,℘
 (𝑗=1,2,…,𝐽) shows the criteria
and
Å=Å
,Å
,…,Å
,…,Å
 (𝑖=1,2,…,𝐼) shows the
alternatives (companies). The FCF-SIWEC-RBNAR
hybrid method consists of three stages, with the steps
of the method detailed as follows:
Stage 1: Establishing the expert weighting matrix
using FCF sets:
Step 1-1: The expertise levels of the experts are
determined using linguistics variables (LVs) shown
in Table 1. Subsequently, LVs are converted to FCF
numbers. Therefore, the experts assessment matrix
𝒮
=𝒮
can be determined.
Table 1: The LVs for expertise level (Wang et al., 2024).
L
Vs FC
F
umbers
Very High (VH)
0.80,0.85
,
0.20,0.25
,
0.75,0.25
Hi
g
h
(
H
)
0.70,0.75
,
0.30,0.35
,
0.65,0.35
Medium
(
M
)
0.50,0.55
,
0.40,0.45
,
0.50,0.45
Low
(
L
)
0.30,0.35
,
0.70,0.75
,
0.35,0.65
Very Low (VH)
0.20,0.25
,
0.80,0.85
,
0.25,0.75
Step 1-2: FCF numbers are transformed to crisp
values using the score function shown in Eq. (2).
Therefore, the score function matrix
(
𝒮=
𝒮
)
can
be calculated.
𝒮
=𝑆𝑐𝒮
=
𝛾
𝒮
(
𝔡
)
+
𝛾
𝒮
(
𝔡
)
−
𝛿
𝒮
(
𝔡
)
−
𝛿
𝒮
(
𝔡
)
+
𝛾
𝒮
(
𝔡
)
−
𝛿
𝒮
(
𝔡
)
+1.
(2
)
Development the Novel FCF-SIWEC-RBNAR Hybrid Method for Financial Performance Evaluation
55
Step 1-3: The weighting of the experts is
computed using Eq. (3). Therefore, the experts
weighting matrix
(
𝑤=
𝑤
)
can be determined.
𝑤
=
𝒮
∑
𝒮

;
(
𝑘=1,2, …,𝐾
)
.
(3)
herein,
𝑤
∈
0,1
and
∑
𝑤

=1.
Stage 2: Establishing the criteria weighting
matrix using the FCF-SIWEC method (Puška et al.,
2024):
Step 2-1: Each expert assesses each criterion using
the LVs provided in Table 2. Subsequently, LVs are
converted to FCF numbers. Therefore, the criteria
assessment matrix
𝒬
=𝒬


can be determined.
Herein,
𝒬

=
〈
𝔡,𝛾
𝒬

(
𝔡
)
,𝛾
𝒬

(
𝔡
)
,
𝛿
𝒬

(
𝔡
)
,𝛿
𝒬

(
𝔡
)
,𝛾
𝒬

(
𝔡
)
,𝛿
𝒬

(
𝔡
)
| 𝔡∈𝔇
〉
.
Table 2: The LVs for criteria evaluation (Wang et al., 2024).
L
Vs FC
F
umbers
Extremely High (EH)
0.90,0.95
,
0.10,0.15
,
0.85,0.15
Very High (VH)
0.80,0.85
,
0.20,0.25
,
0.75,0.25
High (H)
0.70,0.75
,
0.30,0.35
,
0.65,0.35
Medium (M)
0.50,0.55
,
0.40,0.45
,
0.50,0.45
Low (L)
0.30,0.35
,
0.70,0.75
,
0.35,0.65
Very Low (VH)
0.20,0.25
,
0.80,0.85
,
0.25,0.75
Extremely Low (EL)
0.10,0.15
,
0.90,0.95
,
0.15,0.85
Step 2-2: The criteria assessment matrix is
multiplied by the experts' weights using Eq. (4).
Therefore, the weighted criteria assessment matrix
𝒫
=𝒫


can be determined.
𝒫

=𝑤
𝒬

=
⎩
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎧
〈
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎡
1−
1 −
𝛾
𝒬

(
𝔡
)
,
1−
1 −
𝛾
𝒬

(
𝔡
)
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎤
,
𝛿
𝒬

(
𝔡
)
,
𝛿
𝒬

(
𝔡
)
,
⎣
⎢
⎢
⎢
⎡
𝛾
𝒬

(
𝔡
)
,
1−
1−
𝛿
𝒬

(
𝔡
)
⎦
⎥
⎥
⎥
⎤
〉
⎭
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎫
.
(4)
Step 2-3: FCF numbers are transformed to crisp
values using score function shown in Eq. (2).
Therefore, the crisp criteria assessment matrix
𝒫=𝒫


can be calculated.
Step 2-4: The normalized criteria - matrix
𝒪=𝒪


can be calculated using Eq. (5).
𝒪

=
𝒫



𝒫

;
(
𝑗
=1,…,𝐽; 𝑘=1,…,𝐾
)
.
(5)
Step 2-5: The standardized criteria assessment
matrix
𝒩=𝒩


can be calculated using Eq.
(6).
𝒩

=𝒪

𝜎
;
(
𝑗
=1,…,𝐽; 𝑘=1,…,𝐾
)
.
(6
)
herein, 𝜎
refers the standard deviation of each
criterion.
Step 2-6: The sum of the weighting matrix
ℳ=ℳ
can be calculated using Eq. (7).
ℳ
=
∑
𝒩


;
(
𝑗
=1,…,𝐽;𝑘=1,…,𝐾
)
.
(7
)
Step 2-7: The criteria weighting matrix
𝕨=
𝕨
can be computed using Eq. (8).
𝕨
=
ℳ
∑
ℳ

;
(
𝑗
=1,…,𝐽
)
.
(8
)
herein, 𝕨=𝕨
,𝕨
,…,𝕨
,…,𝕨
for 𝕨
ϵ
0,1
with the
∑
𝕨

=1.
Stage 3: Evaluating the financial performance of
companies using the RBNAR method:
Step 3-1: The RBNAR method consist of two
distinct normalization processes (Kara et al., 2024).
These are the Z-score normalization technique (Shih
et al., 2007) and Aytekin’s reference-based
normalization technique (Aytekin, 2020). Then, these
two normalization can be aggregated with Heron
Mean (Zhu, 2022). Initially, the initial decision
matrix
ℒ=𝐿


is constructed. In this step, there
are three sub-steps:
Step 3-1a: The first normalized matrix
𝒦=
𝒦


is computed using Eq. (9).
𝒦

=𝑒



;
(
𝑖=1,…,𝐼;
𝑗
=1,…,𝐽
)
.
(9
)
herein, 𝑅
indicates reference value matrix and 𝜎
refers the standard deviation of each criterion.
Step 3-1b: The second normalized matrix
ℋ=
ℋ


is computed using Eq. (10).
𝒦

=1−



;
(
𝑖=1,…,𝐼;
𝑗
=1,…,𝐽
)
.
(10
)
herein, 𝑅
indicates reference value matrix and 𝛼
refers a positive value.
Step 3-1c: The aggregated normalized matrix
𝒢=𝒢


is computed using Eq. (11).
𝒢

=𝜉
𝒦

𝒢

+
(
1−𝜉
)
𝒦

𝒢

;
(
𝑖=1,…,𝐼;
𝑗
=
1,…,𝐽
)
.
(11
)
herein, 𝜉 indicates trade-off parameter for
determining weighting of first normalization
technique.
Step 3-2: The weighted normalized matrix
ℱ=ℱ


is computed using Eq. (11).
ℱ

=𝕨
𝒢

;
(
𝑖=1,…,𝐼;
𝑗
=1,…,𝐽
)
.
(11
)
DATA 2025 - 14th International Conference on Data Science, Technology and Applications
56
Step 3-3: The alternative ranking matrix
(
ℛ=
ℛ
)
is computed using Eq. (12).
ℛ
=
∑
ℱ


;
(
𝑖=1,…,𝐼;
𝑗
=1,…,𝐽
)
.
(12)
3 APPLICATIONS
In this study, the financial performance of 50
companies was evaluated for the years 2020, 2021,
2022, and 2023 using the FCF-SIWEC-RBNAR
hybrid method. In the application section,
information about the experts is first presented, and
the criteria are explained. Subsequently, the
applications of the FCF-SIWEC-RBNAR hybrid
method for each year are demonstrated.
3.1 Experts and Criteria
3.1.1 Experts
To determine the importance levels of the criteria, the
opinions of 8 experts were sought. These experts
consist of financial managers from companies and
academics conducting research in the field of finance.
Information about the experts is provided in Table 3.
Face-to-face interviews were conducted to gather
expert evaluations. During this process, the expert
evaluation matrix, which reflects the expertise levels
of the experts, and the criteria evaluation matrix,
which indicates the importance levels of the criteria,
were obtained.
Table 3: Experts Group.
Ex
p
erts Ex
p
ertise
L
evel Pro
f
essions
∃
High (H)
Financial Manager
∃
High (H)
Financial Manager
∃
Very High (VH)
Financial Manager
∃
High (H)
Financial Manager
∃
Medium (M)
Professor of Finance
∃
Very High (VH)
Professor of Finance
∃
Very High (VH)
Professor of Finance
∃
Medium (M)
Professor of Finance
3.1.2 Criteria
Seven criteria were identified to evaluate the financial
performance of companies. These criteria consist of
financial ratios, which serve as key indicators of a
company's financial condition. The financial ratios
used in the study are presented in Table 4. The
financial ratio values of the companies were
calculated based on data obtained from financial
reports. Consequently, the initial decision matrices
for financial performance evaluation were established
for each year.
Table 4: Financial Ratios as Criteria.
Criteria Identification
℘
Equity Profitability Ratio
℘
Return on Assets (ROA) Ratio
℘
Leverage Ratio
℘
Debt-to-Equity Ratio
℘
Operating Profit Margin
℘
Pre-tax Profit Margin
℘
Net Profit Margin
3.2 Application-1: Assessment of
Financial Performance for 2020
The steps of the FCF-SIWEC-RBNAR hybrid
method for calculating the financial performance of
the selected companies were applied in the following
sequence:
Application-1 Stage 1: Establishing the expert
weighting matrix using FCF sets:
Step 1-1: The experts assessment matrix
𝒮
=
𝒮
were determined. It is shown in Table 5.
Table 5: The experts assessment matrix.
Expert LVs FCF Numbers
𝒮
∃
H
0.70,0.75
,
0.30,0.35
,
0.65,0.35
0.6448
∃
H
0.70,0.75
,
0.30,0.35
,
0.65,0.35
0.6448
∃
VH
0.80,0.85
,
0.20,0.25
,
0.75,0.25
0.7394
∃
H
0.70,0.75
,
0.30,0.35
,
0.65,0.35
0.6448
∃
M
0.50,0.55
,
0.40,0.45
,
0.50,0.45
0.5255
∃
VH
0.80,0.85
,
0.20,0.25
,
0.75,0.25
0.7394
∃
VH
0.80,0.85
,
0.20,0.25
,
0.75,0.25
0.7394
∃
M
0.50,0.55
,
0.40,0.45
,
0.50,0.45
0.5255
Step 1-2: The score function matrix
(
𝒮=
𝒮
)
were calculated using Eq. (2). It is shown in Table 5.
Step 1-3: The experts weighting matrix
(
𝑤=
𝑤
)
were calculated using Eq. (3). It is shown in
Table 6.
Table 6: The experts weighting matrix.
∃
∃
∃
∃
∃
∃
∃
∃
𝑤
0.123
9
0.123
9
0.142
1
0.123
9
0.101
0
0.142
1
0.142
1
0.101
0
Application-1 Stage 2: Establishing the criteria
weighting matrix using the FCF-SIWEC method:
Step 2-1: The criteria assessment matrix
𝒬
=
𝒬


were determined. It is shown in Table 7.
Step 2-2: The weighted criteria assessment matrix
𝒫
=𝒫


were determined using Eq. (4).
Step 2-3: The crisp criteria assessment matrix
𝒫=𝒫


can be computed by employing Eq. (2).
Development the Novel FCF-SIWEC-RBNAR Hybrid Method for Financial Performance Evaluation
57
Table 7: The criteria assessment matrix.
℘
℘
℘
℘
℘
℘
℘
∃
M L M H M H M
∃
H H M H M EH H
∃
M M L H M VH H
∃
H VH H VH H H M
∃
M M L M M M L
∃
EH VH EH EH VH EH H
∃
M H M H VH VH H
∃
VH H VH M VH VH H
Step 2-4: The normalized criteria assessment
matrix
𝒪=𝒪


were computed using Eq. (5).
Step 2-5: The standardized criteria assessment
matrix
𝒩=𝒩


were computed using Eq. (6).
Step 2-6: The sum of the weighting matrix
ℳ=ℳ
were computed using Eq. (7).
Step 2-7: The criteria weighting matrix
𝕨=
𝕨
were computed using Eq. (8). It is shown in
Table 8.
Table 8: The criteria weighting matrix.
℘
℘
℘
℘
℘
℘
℘
𝕨
0.1426 0.1362 0.1453 0.1464 0.1429 0.1428 0.1438
Application-1 Stage 3: Evaluating the financial
performance of companies using the RBNAR method:
Step 3-1: The initial decision matrix
ℒ=𝐿


was constructed.
Step 3-1a: The first normalized matrix
𝒦=
𝒦


were computed using Eq. (9).
Step 3-1b: The second normalized matrix
ℋ=
ℋ


were computed using Eq. (10). Herein,
references values for each criterion 𝑅
were
determined depending on sectoral avarage. It is
shown in Table 9. 𝛼 was also determined as 6.
Table 9: The references values for 2020.
℘
℘
℘
℘
℘
℘
℘
𝑅
-0.07 0.07 0.58 11.65 24.37 20.37 17.52
Step 3-1c: The aggregated normalized matrix
𝒢=𝒢


were computed using Eq.(11)
(
𝜉=0.5
)
Step 3-2: The weighted normalized matrix
ℱ=ℱ


were computed by employing Eq. (11).
Step 3-3: The alternative ranking matrix
(
ℛ=
ℛ
)
were computed using Eq. (12). It is shown in
Table 10.
Table 10: The alternative ranking matrix (2020).
A
lt. Company
ℛ
Ran
k
Å
AEFES 0.962562 19
Å
AKCNS 0.984732 10
Å
AKSA 0.992541 3
Å
AKSEN 0.986193 8
Å
ARCLK 0.979713 14
Å
ASELS 0.962279 20
Å
BFREN 0.932081 31
Å
BIMAS 0.970206 17
Å
BRSAN 0.948489 25
Å

BRYAT 0.588302 50
Å

BTCIM 0.814295 46
Å

CCOLA 0.985611 9
Å

CIMSA 0.988697 5
Å

DOAS 0.942514 28
Å

ECILC 0.907017 39
Å

EGEEN 0.829042 44
Å

ENJSA 0.982585 12
Å

ENKAI 0.927217 32
Å

EREGL 0.950851 23
Å

FROTO 0.918203 37
Å

GUBRF 0.991015 4
Å

HEKTS 0.979841 13
Å

IPEKE 0.827259 45
Å

ISMEN 0.925188 33
Å

KONTR 0.997134 1
Å

KONYA 0.923407 34
Å

KOZAA 0.829462 43
Å

KOZAL 0.735799 48
Å

KRDMD 0.963375 18
Å

MGROS 0.666527 49
Å

ODAS 0.867859 42
Å

OTKAR 0.918612 36
Å

OYAKC 0.976441 15
Å

PETKM 0.988252 6
Å

PGSUS 0.759200 47
Å

SASA 0.970442 16
Å

SAYAS 0.954227 22
Å

SISE 0.933951 30
Å

SOKM 0.909599 38
Å

TCELL 0.943898 27
Å

THYAO 0.887813 40
Å

TOASO 0.958634 21
Å

TTKOM 0.946221 26
Å

TTRAK 0.937094 29
Å

TUKAS 0.986323 7
Å

TUPRS 0.874764 41
Å

ULKER 0.994155 2
Å

VESBE 0.948991 24
Å

VESTL 0.982698 11
Å

ZOREN 0.921379 35
DATA 2025 - 14th International Conference on Data Science, Technology and Applications
58
3.3 Application-2: Assessment of
Financial Performance for 2021
In Application 1, the expert weights and criteria
weights calculated are used in the same manner to
determine the financial performance of the selected
companies for the year 2021. Therefore, starting from
Stage 3, this application was completed as
Application 2:
Application-2 Stage 3: Evaluating the financial
performance of companies using the RBNAR method:
Step 3-1: The initial decision matrix
ℒ=𝐿


was constructed.
Step 3-1a: The first normalized matrix
𝒦=
𝒦


were computed using Eq. (9).
Step 3-1b: The second normalized matrix
ℋ=
ℋ


were computed using Eq. (10). Herein,
references values for each criterion 𝑅
were
determined depending on sectoral avarage. It is
shown in Table 11. 𝛼 was also determined as 6.
Table 11: The references values for 2021.
℘
℘
℘
℘
℘
℘
℘
𝑅
0.26 0.10 0.58 2.90 26.16 27.85 25.18
Step 3-1c: The aggregated normalized matrix
𝒢=𝒢


were computed using Eq.(11)
(
𝜉=0.5
)
Step 3-2: The weighted normalized matrix
ℱ=ℱ


were computed by employing Eq. (11).
Step 3-3: The alternative ranking matrix
(
ℛ=
ℛ
)
were computed using Eq. (12). It is shown in
Table 12.
3.4 Application-3: Assessment of
Financial Performance for 2022
In Application 1, the expert weights and criteria
weights calculated are used in the same manner to
determine the financial performance of the selected
companies for the year 2022. Therefore, starting from
Stage 3, this application was completed as
Application 2:
Application-3 Stage 3: Evaluating the financial
performance of companies using the RBNAR method:
Step 3-1: The initial decision matrix (
ℒ=𝐿
𝑖𝑗
𝐼𝑥𝐽
)
was constructed.
Step 3-1a: The first normalized matrix
𝒦=
𝒦


were computed using Eq. (9).
le 12: The alternative ranking matrix (2021).
A
lt. Company
ℛ
Ran
k
Å
AEFES 0.932210 23
Å
AKCNS 0.976393 5
Å
AKSA 0.972999 7
Å
AKSEN 0.967961 8
Å
ARCL
K
0.955702 17
Å
ASELS 0.965635 12
Å
BFREN 0.904064 33
Å
BIMAS 0.965993 11
Å
BRSAN 0.906092 32
Å

BRYAT 0.549874 50
Å

BTCIM 0.745476 47
Å

CCOLA 0.975334 6
Å

CIMSA 0.947578 18
Å

DOAS 0.896664 34
Å

ECILC 0.871806 38
Å

EGEEN 0.761759 44
Å

ENJSA 0.984248 2
Å

ENKAI 0.910959 29
Å

EREGL 0.939049 22
Å

FROTO 0.846560 41
Å

GUBRF 0.966433 10
Å

HEKTS 0.977750 4
Å

IPEKE 0.795278 43
Å

ISMEN 0.909414 30
Å

KONTR 0.966620 9
Å

KONYA 0.918015 26
Å

KOZAA 0.811229 42
Å

KOZAL 0.747146 46
Å

KRDMD 0.963214 13
Å

MGROS 0.739880 48
Å

ODAS 0.923764 25
Å

OTKAR 0.885828 36
Å

OYAKC 0.961425 15
Å

PETKM 0.960577 16
Å

PGSUS 0.739281 49
Å

SASA 0.939096 21
Å

SAYAS 0.849308 40
Å

SISE 0.908288 31
Å

SOKM 0.759649 45
Å

TCELL 0.915811 27
Å

THYAO 0.946889 19
Å

TOASO 0.927396 24
Å

TTKOM 0.940303 20
Å

TTRA
K
0.893256 35
Å

TUKAS 0.990163 1
Å

TUPRS 0.912764 28
Å

ULKER 0.861023 39
Å

VESBE 0.978342 3
Å

VESTL 0.962218 14
Å

ZOREN 0.880782 37
Step 3-1b: The second normalized matrix
ℋ=
ℋ


were computed using Eq. (10). Herein,
references values for each criterion 𝑅
were
determined depending on sectoral avarage. It is
shown in Table 13. 𝛼 was also determined as 6.
Development the Novel FCF-SIWEC-RBNAR Hybrid Method for Financial Performance Evaluation
59
Table 13: The references values for 2022.
℘
℘
℘
℘
℘
℘
℘
𝑅
0.20 0.09 0.51 1.38 22.57 18.68 19.23
Step 3-1c: The aggregated normalized matrix
𝒢=𝒢


were computed using Eq.(11)
(
𝜉=0.5
)
Step 3-2: The weighted normalized matrix
ℱ=ℱ


were computed by employing Eq. (11).
Step 3-3: The alternative ranking matrix
(
ℛ=
ℛ
)
were computed using Eq. (12). It is shown in
Table 14.
Table 14: The alternative ranking matrix (2022).
A
lt. Company
ℛ
Ran
k
Å
AEFES 0.964933 6
Å
AKCNS 0.942071 12
Å
AKSA 0.913490 25
Å
AKSEN 0.958796 8
Å
ARCL
K
0.860952 35
Å
ASELS 0.917481 23
Å
BFREN 0.913014 26
Å
BIMAS 0.970948 2
Å
BRSAN 0.970224 3
Å

BRYAT 0.525771 50
Å

BTCIM 0.945333 11
Å

CCOLA 0.971074 1
Å

CIMSA 0.966093 5
Å

DOAS 0.804489 44
Å

ECILC 0.805849 43
Å

EGEEN 0.954799 9
Å

ENJSA 0.938734 14
Å

ENKAI 0.853206 36
Å

EREGL 0.938701 15
Å

FROTO 0.821838 42
Å

GUBRF 0.920006 22
Å

HEKTS 0.959673 7
Å

IPEKE 0.690894 47
Å

ISMEN 0.786546 46
Å

KONTR 0.936900 16
Å

KONYA 0.931840 18
Å

KOZAA 0.662415 48
Å

KOZAL 0.607810 49
Å

KRDMD 0.906280 28
Å

MGROS 0.941262 13
Å

ODAS 0.913916 24
Å

OTKAR 0.873978 32
Å

OYAKC 0.861374 33
Å

PETKM 0.861179 34
Å

PGSUS 0.800734 45
Å

SASA 0.850716 38
Å

SAYAS 0.890156 30
Å

SISE 0.910982 27
Å

SOKM 0.932444 17
Å

TCELL 0.899292 29
Å

THYAO 0.946571 10
Å

TOASO 0.925703 19
Å

TTKOM 0.968590 4
Å

TTRA
K
0.887566 31
Å

TUKAS 0.849450 39
Å

TUPRS 0.925084 20
Å

ULKER 0.832743 41
Å

VESBE 0.852768 37
Å

VESTL 0.833815 40
Å

ZOREN 0.920313 21
3.5 Application-4: Assessment of
Financial Performance for 2023
In Application 1, the expert weights and criteria
weights calculated are used in the same manner to
determine the financial performance of the selected
companies for the year 2023. Therefore, starting from
Stage 3, this application was completed as
Application 2:
Application-3 Stage 3: Evaluating the financial
performance of companies using the RBNAR method:
Step 3-1: The initial decision matrix
ℒ=𝐿


was constructed.
Step 3-1a: The first normalized matrix
𝒦=
𝒦


were computed using Eq. (9).
Step 3-1b: The second normalized matrix
ℋ=
ℋ


were computed using Eq. (10). Herein,
references values for each criterion 𝑅
were
determined depending on sectoral avarage. It is
shown in Table 15. 𝛼 was also determined as 6.
Table 15: The references values for 2023.
℘
℘
℘
℘
℘
℘
℘
𝑅
0.18 0.09 0.46 1.14 21.62 37.77 39.88
Step 3-1c: The aggregated normalized matrix
𝒢=𝒢


were computed using Eq.(11)
(
𝜉=0.5
)
.
Step 3-2: The weighted normalized matrix
ℱ=ℱ


were computed by employing Eq. (11).
Step 3-3: The alternative ranking matrix (
ℛ=
ℛ
𝑖
𝐼
) were computed using Eq. (12). It is shown in
Table 16.
Table 16: The alternative ranking matrix (2023).
A
lt. Company
ℛ
Ran
k
Å
AEFES 0.965852 10
Å
AKCNS 0.951164 17
Å
AKSA 0.952919 15
Å
AKSEN 0.981123 4
Å
ARCL
K
0.814969 43
Å
ASELS 0.964276 13
Å
BFREN 0.590641 49
Å
BIMAS 0.984929 2
DATA 2025 - 14th International Conference on Data Science, Technology and Applications
60
Table 16: The alternative ranking matrix (2023) (cont.).
A
lt. Company
ℛ
Ran
k
Å
BRSAN 0.983916 3
Å

BRYAT 0.545945 50
Å

BTCIM 0.974750 7
Å

CCOLA 0.864660 37
Å

CIMSA 0.974084 8
Å

DOAS 0.834484 41
Å

ECILC 0.887644 35
Å

EGEEN 0.961861 14
Å

ENJSA 0.952794 16
Å

ENKAI 0.910901 27
Å

EREGL 0.908386 29
Å

FROTO 0.770342 48
Å

GUBRF 0.896613 32
Å

HEKTS 0.771871 47
Å

IPEKE 0.792363 45
Å

ISMEN 0.910019 28
Å

KONTR 0.892299 34
Å

KONYA 0.912007 25
Å

KOZAA 0.798384 44
Å

KOZAL 0.792109 46
Å

KRDMD 0.905363 30
Å

MGROS 0.965707 11
Å

ODAS 0.895092 33
Å

OTKAR 0.858115 38
Å

OYAKC 0.846160 39
Å

PETKM 0.930501 21
Å

PGSUS 0.845413 40
Å

SASA 0.970854 9
Å

SAYAS 0.936964 20
Å

SISE 0.926524 22
Å

SOKM 0.975280 6
Å

TCELL 0.926093 23
Å

THYAO 0.937109 19
Å

TOASO 0.903715 31
Å

TTKOM 0.995678 1
Å

TTRA
K
0.815780 42
Å

TUKAS 0.921587 24
Å

TUPRS 0.964924 12
Å

ULKER 0.911346 26
Å

VESBE 0.977404 5
Å

VESTL 0.879879 36
Å

ZOREN 0.949470 18
4 FINDINGS
In the results of the FCF-SIWEC-RBNAR hybrid
method application, three key findings were
identified. The first finding is the impact levels of the
experts in the decision-making process. The second
finding is the importance levels of the criteria in the
decision-making process. The third finding is the
financial performance rankings of the selected
companies for the years 2020, 2021, 2022, and 2023.
The findings obtained are as follows:
First finding: In the research, the impact levels of
the experts involved in determining the financial
performance of the companies are as follows: "
∃
=∃
=∃
>∃
=∃
=∃
>∃
=∃
." In this case,
the third, sixth, and seventh experts were identified as
the most influential experts in the decision-making
process.
Second finding: The importance ranking of the
financial ratio criteria used in the financial
performance calculation is as follows: "
𝐷𝑒𝑏𝑡 − 𝑡𝑜− 𝐸𝑞𝑢𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜
(
℘
=0.1464
)
>
𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝑅𝑎𝑡𝑖𝑜
(
℘
=0.1453
)
>
𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑟𝑔𝑖𝑛
(
℘
=0.1438
)
>
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑟𝑔𝑖𝑛
(
℘
=0.1429
)
>
𝑃𝑟𝑒 − 𝑡𝑎𝑥 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑟𝑔𝑖𝑛
(
℘
=0.1428
)
>
𝐸𝑞𝑢𝑖𝑡𝑦 𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜
(
℘
=0.1426
)
>
𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐴𝑠𝑠𝑒𝑡𝑠 (𝑅𝑂𝐴) 𝑅𝑎𝑡𝑖𝑜
(
℘
=
0.1362
)
>." According to this ranking, the fourth
criterion, which has very similar importance levels to
the others, is determined to play the most significant
role in the decision-making process.
Third finding: The top three companies with the
highest financial performance for the year 2020 are
ranked as "KONTR, ULKER, AKSA." For the year
2021, the top three companies with the highest
financial performance are ranked as "TUKAS,
ENJSA, VESBE." For the year 2022, the top three
companies with the highest financial performance are
ranked as "CCOLA, BIMAS, BRSAN." Finally, for
the year 2023, the top three companies with the
highest financial performance are ranked as
"“TTKOM, BIMAS, BRSAN”.
5 CONCLUSIONS
This study aimed to develop a novel hybrid method for
financial performance analysis, designed as a decision
support system, and referred to as the FCF-SIWEC-
RBNAR method. The proposed method integrates
FCF sets to calculate expert weights, the FCF-SIWEC
approach to determine criteria weights, and the
RBNAR technique to evaluate and rank companies
based on their financial performance. The
methodology was systematically outlined and applied
to 50 companies listed on Borsa Istanbul across four
different case studies. The findings demonstrate that
the FCF-SIWEC-RBNAR hybrid method effectively
assessed and ranked companies' financial
performance, identifying the Debt-to-Equity Ratio as
the most significant financial criterion. Moreover, the
financial performance of the companies was
calculated for each year, and the top-performing
companies were determined. This research contributes
Development the Novel FCF-SIWEC-RBNAR Hybrid Method for Financial Performance Evaluation
61
to the literature by introducing a new hybrid approach
for financial performance evaluation and
demonstrating its practical applicability. The proposed
method holds potential for application in both
academic research and the financial industry, offering
a reliable tool for decision-making in financial
performance analysis.
The FCF-SIWEC-RBNAR hybrid method has
limitations that warrant consideration. The study's
findings are based on data from 50 companies listed
on Borsa Istanbul, which may limit generalizability to
other markets or industries. The accuracy of the
method depends on the quality of financial data and
the subjective nature of expert evaluations, which may
introduce bias. Additionally, the selected financial
ratios may not be universally applicable across all
sectors. Further research with broader datasets and
diverse economic contexts is needed to validate and
enhance the scalability of the method.
ACKNOWLEDGEMENT
The author gratefully acknowledges the support
provided by the Scientific and Technological
Research Council of Türkiye (TUBITAK), through
the Scientist Support Programs Presidency (BIDEB),
under the 2224-A Grant Program for Participation in
Scientific Meetings Abroad (Application No:
1919B022502253), for the presentation of this study.
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