







































S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 
234-241 

234 

 

 

 

Article 

Integrating panel data regression and fuzzy 

decision-making approaches to evaluate the impact 

of currency-hedged deposits on participating banks 
Selman Duran1, Serkan Eti2*, Serhat Yüksel1, Hasan Dinçer1 

1The School of Business, İstanbul Medipol University, İstanbul, Turkey 
2IMU Vocational School, İstanbul Medipol University, İstanbul, Turkey 

A R T I C L E   I N F O 
 

Article history: 
Received 29 August 2025  
Received in revised form 
20 October 2025 
Accepted 25 November 2025 
 
Keywords:  
Artificial Intelligence, Internal control audit, 
Trust issues, Decision-making model 
 
*Corresponding author 
Email address: 
serhatyuksel@medipol.edu.tr 
 
DOI: 10.55670/fpll.futech.5.1.20 

A B S T R A C T 
 

Currency-Hedged Deposits (CHD) were introduced in Türkiye to hedge the 
currency risk. Hence, it is aimed to provide macroeconomic stability in this 
country. Nevertheless, the impact of this implication on banks' participation is 
unclear. This study analyzes the impact of the foreign exchange hedge deposit 
(CHD) mechanism on the financial performance of participation banks in 
Türkiye. This study integrates fuzzy multi-criteria decision-making analysis 
with panel data regression. In this framework, data from these banks for 2021-
2023 is considered. First, panel regression analysis is conducted for six 
participating banks. Second, a Euclidean distance-based CIMAS technique is 
used to find the most critical criteria. For this purpose, Fermatean fuzzy 
numbers are considered in this modelling process to handle uncertainties more 
effectively. The main contribution of this research is the hybrid consideration 
of panel data regression and fuzzy decision-making analysis. Owing to this 
combination, the impact of this new implication on bank participation can be 
more effectively identified. Econometric results indicate that CHD has a positive 
impact on profitability. On the other hand, risk management and compatibility 
with interest-free financing are the most critical factors.  

1. Introduction 

This study examines the effects of this implementation 
on the financial performance of participation banks in Turkey 
[1]. In the studies, the factors affecting banks' financial 
performance are evaluated along two dimensions [2]. The 
first group includes macro-level factors such as inflation rate, 
interest rate, gross national product, and industrial 
production, while internal factors include total loans/total 
assets, asset size, equity/total assets, non-performing 
loans/total loans, personnel expenses/total revenues, off-
balance sheet activities/total assets, and bank type. In 
addition to these variables, this study predicts that currency-
hedged deposits, as a macro factor, also affect financial 
performance [3]. The primary aim of this study is to 
comprehensively examine the impact of the Currency-Hedged 
Deposit (CHD) practice on the financial performance and 
strategic sustainability of participation banks in Turkey by 
integrating econometric modeling with fuzzy decision-
making techniques. Existing studies mostly focus on the 
macroeconomic impacts of the CHD. However, there are few 
studies in the literature that address which factors are most 
critical. This study integrates panel data econometric analysis 
with a fuzzy decision-making model to address this issue. The 
hybrid approach provides a more comprehensive 

understanding of the dual impact of the CHD. Furthermore, it 
also provides a replicable methodological framework for 
future analyses of Islamic financial innovations. This study's 
analysis process involves two distinct stages. First, a panel 
regression analysis is conducted for six different participation 
banks. Data from 2021 to 2023 are considered. In this 
framework, return on assets (ROA) and return on equity 
(ROE) are used as dependent variables. Furthermore, factors 
such as exchange rate volatility and inflation rates are 
integrated into the model as independent variables. Second, a 
new fuzzy decision-making model aims to identify the most 
critical criteria. In this process, the CIMAS technique, based on 
Fermatean fuzzy numbers and Euclidean distance, is 
integrated. 

2. Literature review 

The scope of the study conducted by Kaya [4] comprises 
legal entities that benefit from the Currency Protected TL 
Time Deposit Account. In the study, it is aimed to reveal the 
accounting records that should be made at the end of the 
period, at the opening and closing of the account at the end of 
the period by the enterprises that convert their foreign 
currencies in US Dollars, Euros and British Pounds in their 
balance sheet as of 31.12.2021 until the date of submission of 

 

 Open Access Journal 

ISSN 2832-0379 

February 2026| Volume 05 | Issue 01 | Pages 234-241 

https://doi.org/10.55670/fpll.futech.5.1.20 

 

 

 

 

 

 

 

 

 

 

 

 

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mailto:serhatyuksel@medipol.edu.tr
https://doi.org/10.55670/fpll.futech.5.1.20
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S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

235 

 

the declaration regarding the fourth provisional tax period, 
with a comprehensive sample application. It is expected that 
the study will contribute to the businesses and accounting 
professionals who open or want to open Foreign Currency 
Conversion Currency Hedged TL Time Deposit Account. In the 
survey conducted by Yayman [5], the question is examined 
whether the tax privileges granted to the exchange rate-
protected deposit system cost the state budget. As a result, it 
was found that the conditional obligation undertaken by the 
public sector gives confidence to residents to protect the 
financial value of the TL, it is not yet possible to determine the 
net return guarantee provided to Turks living abroad, budget 
expenditures have increased over the period analyzed, but 
budget revenues have increased almost at the same level, the 
primary surplus has increased, so its impact on the budget has 
not yet been low, the cost of the increase in exchange rate-
protected deposit accounts is gradually increasing and the 
main risk is unforeseen jumps in the exchange rate. 

In the study by Akgemci [6], it is stated in this 
announcement that the currency-hedged deposit account is a 
financial asset and should be measured at fair value through 
profit or loss within the scope of TFRS 9 Financial 
Instruments Standard. In this regard, it is discussed how to 
classify related deposit accounts within the scope of TFRS 9, 
how to measure the end-of-period, and how to recognize 
income or expenses arising after a company subject to an 
independent audit converts its forward foreign currency 
accounts into PPDCs. Kaldırım and Selvi [7] explained the 
legal structure of FX-hedged time deposit accounts. The 
accounting and reporting principles within the framework of 
the Uniform Accounting System, TFRS-9, and US GAAP ASC 
815 are examined in different exchange rate scenarios using 
an example, and financial reports are compared. In addition, 
the differences in the provisions of US GAAP ASC-815 and 
TFRS-9 related to embedded derivatives are presented. 
Álvarez-Díez et al. [8] considered a multi-currency cross-
hedging strategy that minimizes currency risk. They 
measured the reduction in foreign exchange risk carried using 
natural multi-currency cross hedging, using the Conditional 
Value at Risk (CVaR) and Value at Risk (VaR) to measure 
market risk rather than variance. CVaR is minimized using 
linear programming, while a multi-objective genetic 
algorithm is designed to mitigate VaR across two scenarios for 
each currency. The results show that the optimal hedging 
strategy that minimizes VaR is different from the minimum 
CVaR hedging strategy. Another point is that significant 
reductions in VaR and CVaR can be achieved by investing only 
in other currencies.  

Du et al. [9] investigated differences between fully 
hedged and unhedged portfolios comprising 10 different 
risky asset datasets from 2006 to 2014. Empirical results 
show that fully hedged portfolios have significantly higher 
Sharpe ratios than unhedged portfolios. In terms of economic 
utility, a risk-averse investor would be willing to pay more per 
year to build a fully hedged portfolio. For example, investors 
using the equal-weighted portfolio strategy are willing to pay 
more than 7.2% and 3.3% per annum to hedge RMB exchange 
rate risk in CNY and CNH, respectively. Moreover, based on 
the results in sub-periods and time-varying rolling forecasts, 
we conclude that hedging currency risk in portfolio 
management will become increasingly important during RMB 
internationalization. Bag and Omrane [10] tested the 
statistical relationship between CSR and corporate financial 
performance (CFP) of the top 100 companies listed on the 
National Stock Exchange (NSE) in India. Factor analysis and 
multivariate regression were conducted, yielding conclusive 

findings on the CSR-CFP relationship. Overall, the existing 
literature on Currency-Hedged Deposits (CHD) can be 
grouped into three main strands: (1) studies emphasizing 
accounting and regulatory issues, (2) macroeconomic 
assessments focusing on budgetary and fiscal effects, and (3) 
international works examining currency risk and hedging 
efficiency. However, there are limited studies that have 
focused on the most effective determinants.  

3. Proposed methodology 
In the first stage, we conducted a panel data regression 

analysis to examine the relationship among the variables. 
After that, a decision-making model is generated to find the 
most significant criteria.  

3.1 Panel data regression methodology 
Panel data is a data structure created by systematically 

observing units (horizontal cross-section) such as people, 
firms, and countries within a specified period (vertical cross-
section) [11]. With this data group, the number of 
observations increases by including both time series of 
periods and variables of units in the models [12]. Thus, panel 
data provides richer explanatory data, more variability, more 
degrees of freedom, and more efficiency, while reducing the 
linearity between variables [13]. Panel data regression, unlike 
time series, can be shown as [14]:   

yi,t= α+X′i, tβ+ ui,t          i=1, …, N, t=1, …, T                 (1) 

ui,t= μi+ vi, tui,t= 𝜇i+ vi,t           (2) 

In formula (2) μi denotes the unit-specific unobservable 
effect and vi,t is the residual distortion. For example, in a 
performance analysis equation in finance, yi,t measures the 
profitability of the business, while X′i,t may include several 
variables such as firm size, age, sector, region, etc. It is worth 
noting that μi is time-invariant and considers any unit-specific 
effects not included in the regression. The remaining 
distortion vi,t, varies by unit and time and can be considered 
as the usual distortion in the regression. Alternatively, for a 
production function using data on firms over time, yi,t will 
measure output, and X′i,t will measure inputs. Unobserved 
firm-specific effects will be captured by μi, which can be 
thought of as unobserved entrepreneurial or managerial 
skills of firm managers. 

As a result, while panel data can address some of the 
problems faced by time-series or cross-sectional studies, it is 
not sufficient to eliminate all difficulties. Panel data analyses 
fall into two main categories of models, depending on the 
assumptions about the period and individual effects in the 
error term structure: the one-way error regression model and 
the two-way random effects model. These models include the 
fixed-effects and random-effects models. The correlation 
between individual effects and independent variables is 
important in the decision process of choosing a model. If there 
is a correlation between the independent variables and the 
individual's error term, and if there is a specific sample 
treatment, the fixed effects model may be preferred. 
Otherwise, a random effects model would be more 
appropriate. These models represent the fundamental 
structures used in panel data analysis. The correct choice of 
the model is important for the accuracy and reliability of the 
analysis. 

Although the panel data framework helps control for 
unobservable heterogeneity across banks, potential 
endogeneity between the currency-hedged deposit variable 
and financial performance indicators cannot be ruled out 
entirely. For instance, higher-performing participation banks 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

236 

 

might attract larger CHD inflows, while CHD growth itself 
could influence performance through liquidity and 
profitability channels. To mitigate this possible simultaneity 
bias, the models were estimated using lagged explanatory 
variables as robustness checks. In future research, 
instrumental variable approaches could further enhance 
causal inference and address remaining endogeneity 
concerns. 

3.2 Proposed fuzzy decision-making model 
This section concerns the formulation of Euclidean 

distance-based CIMAS using Fermatian fuzzy sets. Thus, while 
uncertainty is minimized by using fuzzy sets, expert weights 
are obtained with Euclidean distance-based expert weighting 
to make the criteria weights more realistic with CIMAS. 
Prioritizing experts solely by years of experience is 
unrealistic. Therefore, prioritizing experts requires analyzing 
other information beyond only their years of experience. For 
this purpose, the Euclidean distance-based experts’ weighting 
method with CIMAS, as described in the literature [15], is 
used in this manuscript. The method's steps are as follows. To 
obtain the important priorities of the experts, the matrix in 
Equation (3) is constructed with some information from the 
experts [16]. 

𝑋 = [𝑥𝑖𝑗]
𝑒×𝑣

                             (3) 

where the columns are 𝑣 variables containing the age, years 
of total experience, industry experience, manager experience, 
and information on patents or certificates 𝑒 experts. Then, the 
arithmetic means and standard deviation values of columns 
are established for computing z-score values using Equations 
(4) and (5), respectively. 

�̅�𝑗 =
1

𝑒
∑ 𝑥𝑖𝑗

𝑒
𝑖=1               (4) 

𝜎𝑗 = √∑ (𝑥𝑖𝑗−�̅�𝑗)
2𝑒

𝑖=1

𝑒
             (5) 

With the help of these statistical values, a standardized matrix 
is obtained by Equation (6). 

𝑧𝑖𝑗 =
𝑥𝑖𝑗−�̅�𝑗

𝜎𝑗
             (6)  

Afterwards, the negative values are determined via Equation 
(7). 

𝑛𝑒𝑔𝑗 = min
𝑖

𝑧𝑖𝑗             (7)  

Euclidean distances between experts and negative values are 
calculated with the help of Equation (8). 

𝐷𝑖 = √∑ (𝑧𝑖𝑗 − 𝑛𝑒𝑔𝑗)
2𝑣

𝑗=1             (8) 

Finally, the scores of experts are defined by normalizing the 
distances via Equation (9). 

𝐸𝑖 =
𝐷𝑖

∑ 𝐷𝑖
𝑒
𝑖=1

            (9)  

A Fermatean fuzzy set (𝐴) is described as Equation (10). 

𝐴 = {𝑢, 〈𝜇𝐴(𝑢), 𝜈𝐴(𝑢)〉: 𝑢 ∈ 𝑈}        (10)  

Where U is the universe of discourse. 𝜇 and 𝜈 are named as 
membership and non-membership degrees, respectively, and 
between zero and one. These degrees have the condition in 
Equation (11). 

0 ≤ (𝜇𝐴(𝑢))
3

+ (𝜈𝐴(𝑢))
3

≤ 1        (11)  

Moreover, the degree of indeterminacy is identified as 
Equation (12). 

𝜋𝐴(𝑢) = √1 − ((𝜇𝐴(𝑢))
3

+ (𝜈𝐴(𝑢))
3

)
3

         (12) 

Consider that A and B are two Fermatean fuzzy sets and β is a 
positive real number. Then, basic operators are defined in 
Equations (13) – (16). 

𝐴 + 𝐵 = (√𝜇𝐴
3 + 𝜇𝐵

3 − 𝜇𝐴
3𝜇𝐵

33
, 𝜈𝐴𝜈𝐵)         (13) 

𝐴 × 𝐵 = (𝜇𝐴𝜇𝐵, √𝜈𝐴
3 + 𝜈𝐵

3 − 𝜈𝐴
3𝜈𝐵

33
)         (14) 

β𝐴 = (√1 − (1 − 𝜇𝐴
3)β3

, 𝜈𝐴
β

)         (15) 

𝐴β = (𝜇𝐴
β

, √1 − (1 − 𝜈𝐴
3)β3

)         (16) 

For example, (. 9, .1) + (. 7, .2) = (√. 93 +. 73 −. 93 ∗. 733
, .1 ∗

.2); (. 9, .1) × (. 7, .2) = (. 9 ∗

.7, √. 13 +. 23 −. 13 ∗. 233
); 2 (. 9, .1) = (√1 − (1 −. 93)23

, . 12) 

The score and accuracy functions are defined using Equations 
(17) and (18), respectively. 

𝑠𝑐𝑜𝑟𝑒(𝐴) =
1+(𝜇𝐴)3−(𝜈𝐴)3

2
          (17) 

𝑎𝑐𝑐(𝐴) = (𝜇𝐴)3 + (𝜈𝐴)3          (18) 

The most significant advantage of using the CIMAS 
method to determine the importance of the criteria is that it 
enables reliability testing. Another feature of the method is 
that it allows weighting evaluations based on experts' scores. 
The method's steps are as follows [17]. To facilitate 
understanding of the proposed Euclidean distance–based 
CIMAS model integrated with Fermatean fuzzy sets, a 
conceptual diagram has been added (Figure 1). The figure 
summarizes the six main stages of the approach: (1) collecting 
expert information, (2) calculating expert weights using 
Euclidean distance, (3) transforming linguistic evaluations 
into Fermatean fuzzy numbers, (4) aggregating and 
normalizing decision matrices, (5) defuzzifying and 
determining criteria differences, and (6) computing 
normalized criteria weights and reliability indices. Overall, 
Equations (19)–(27) sequentially transform subjective expert 
inputs into objective, reliability-tested weights. Each 
computational step progressively enhances the model’s 
accuracy by reducing bias and validating consistency. The 
input decision-making matrix is defined as Equation (19). 

𝐷 = [𝑑𝑖𝑗]
𝑒×𝑛

           (19) 

where d is the Fermatean fuzzy number for n criteria of e 
experts. Next, the weighted matrix is obtained by Equation 
(20). 

𝑦𝑖𝑗 = 𝑑𝑖𝑗𝐸𝑖           (20) 

Afterwards, the weighted matrix’s values are defuzzified via 
Equation (21). 

𝑐𝑖𝑗 = 𝑠𝑐𝑜𝑟𝑒(𝑦𝑖𝑗)          (21) 

Then, the normalized matrix’s values are calculated with the 
help of Equation (22). 

𝑓𝑖𝑗 =
𝑐𝑖𝑗

∑ 𝑐𝑖𝑗
𝑒
𝑖=1

           (22) 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

237 

 

After that, the maximum value of each criterion is 
selected using Equation (23), and the minimum value of each 
criterion is determined using Equation (24). Then, the 
difference between these values of each criterion is calculated 
by Equation (25) [18]. 

𝑚𝑎𝑥𝑗 = max
𝑖

𝑓𝑖𝑗            (23) 

𝑚𝑖𝑛𝑗 = min
𝑖

𝑓𝑖𝑗            (24) 

𝑑𝑖𝑓𝑓𝑗 = 𝑚𝑎𝑥𝑗 − 𝑚𝑖𝑛𝑗           (25) 

The weights of criteria are defined by normalizing the 
difference values with the help of Equation (26). 

𝑤𝑗 =
𝑑𝑖𝑓𝑓𝑗

∑ 𝑑𝑖𝑓𝑓𝑗
𝑛
𝑗=1

            (26) 

Finally, the reliability index is computed for the 
reliability test. For this, the second evaluations are collected 
using a score of 0 to 100 for each criterion by experts. Next, 
scores for each criterion are averaged. Then, Equation (27) is 
estimated. 

𝑅𝐼 =
∑ |100𝑤𝑗−𝑠𝑗|𝑛

𝑗=1

100
          (27) 

Where s refers to the average score. The fermatean fuzzy 
numbers are used for all processes [19]. 

4. Analysis results 

4.1 Results of panel data regression 

In this study, three public and three private participation 
banks that were active during the period from 2021 to 2023, 
when the exchange rate-protected deposit practice was 
implemented in Turkey, were considered. Quarterly data, 
which is the most common, harmonized, and accessible form 
of data for the period in question, are used by drawing from 
the CBRT, the PBAT, the BIST, TurkStat, and the data banks of 
the participating banks. Since exchange rate-protected 
deposits have been in place for only a short time and are still 
ongoing, it may be necessary to wait for more data to support 
a more effective analysis. However, the available data is 
substantial for an analysis in this scope. The findings and 
recommendations to be put forward within the scope of this 
analysis will be binding on the participating banks that 
constitute the study's sample. The model developed through 
this study can be adapted for other banking sectors and more 
general firms in the future.   

Return on assets (ROA) is one of the leading indicators of 

enterprise profitability. Return on assets is calculated as 

profit divided by total assets. It shows the extent to which 

enterprises use their assets rationally and the extent to which 

they make a profit in return for the value of the assets they 

use. Another profitability indicator is return on equity (ROE), 

which is calculated as profit divided by equity. Return on 

equity is important for showing the extent to which the 

owners or shareholders of an enterprise earn a profit in 

return for the capital they invest. Independent variables are 

divided into two groups. The first macro-level variable in the 

first group is CHD, the main subject of the study. The CHD 

refers to the amount of TL deposits in banks as per the 

practice that started at the end of 2021 in Turkey. Based on 

the 1st-quarter 2021 data, the change values in other periods 

(CHDΔ) are used as the main independent variable in the 

study. This variable is calculated by dividing the difference 

between two different periods by the previous period. Other 

macro variables are the change value of GNP (GNPΔ), the 

logarithm values of CPI (lnCPI), the logarithm values of 

industrial production index (lnIPI), the change value of 

exchange rate (CURΔ), and interest rate (INT) based on Q1 

2021 data. The second group of independent variables 

consists of micro-level variables within the firm. The second 

group of independent variables consists of the logarithm 

values of banks' total assets (lnGR), total loans/total assets 

values (TLTA) indicating the proportion of assets provided by 

loans, total equity/total assets values (ETA) indicating the 

proportion of assets provided by equity, personnel 

expenses/total expenses values (PETE) indicating the share 

of personnel expenses in total expenses and off-balance sheet 

activities/total assets values (OBATA) comparing off-balance 

sheet activities and total assets.   

The research's analysis outputs have been organized into 

combined tables, where all relevant panel data models and 

tests are presented together, although in separate tables for 

the two independent variables for easier use and evaluation. 

Thus, all possible models can be easily compared, and the 

tests can be used to determine which model is more 

appropriate. For this purpose, in addition to the three main 

models of pooled least squares, fixed effects, and random 

effects methods, robust models are also included. Thus, the 

analysis results and tests for five models are summarized in 

Table 1. 

Table 1 presents the pooled least-squares, fixed-effects, 
random-effects, robust fixed-effects, and robust random-
effects models for return on assets, along with their tests. In 
this framework, the Arellano robust test is considered. The 
inclusion of pooled, fixed, random, and robust models aims to 
assess the robustness of the empirical results across different 
assumptions about unobserved heterogeneity and error 
variance. Therefore, the fixed effects estimator was chosen as 
it provides consistent results by controlling for these 
unobservable bank-specific attributes. The use of robust 
models further ensures that the estimates remain stable 
against heteroskedasticity or serial correlation. All models 
produced significant results. The F statistic value (7.98, 7.76, 
5.68, respectively), which tests for the presence of unit effect, 
is greater than the table value, and the null hypothesis of no 
unit effect is rejected, indicating the presence of unit effect. 
Thus, despite the pooled model, alternative models that allow 
for a unit effect are presented for consideration.  

Secondly, the Hausman test was applied to investigate 
the efficiency of fixed- and random-effects models; the test 
statistic was 9.84, and the p-value was 0.00. Hence, it is 
concluded that this analysis is statistically significant. 
Therefore, the H0 hypothesis is rejected. Upon rejecting the 
H0 hypothesis, it is concluded that the fixed effects estimator 
is consistent.  To assess the suitability of the panel data 
estimators and to justify the use of robust standard errors, a 
set of diagnostic tests is conducted. First, serial correlation in 
the idiosyncratic errors is examined by the Wooldridge test 
for autocorrelation in panel data. The results indicate the 
presence of first-order serial correlation (p<.05), suggesting 
that the standard fixed-effects estimators may produce 
inefficient and biased standard errors.  Moreover, 
heteroskedasticity is analyzed using the Breusch–Pagan test. 
The BP statistics are confirmed to have heteroskedasticity 
across panels (p<.05). These findings provide strong 
justification for adopting robust standard errors in all 
estimated models. Potential endogeneity can arise due to 
reverse causality and omitted-variable bias.  

 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

238 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
To address these concerns, additional models are 

established with lagged independent variables as robustness 
checks. In addition, the variables are lagged by one period, as 
this lag length is commonly used in panel-data settings to 
mitigate simultaneity without excessively reducing sample 
size. Alternative lag structures are also tested, but these do 
not materially change the results. The analysis shows that the 
increase in TL deposits resulting from the CHD practice has a 
positive impact on the financial performance of participating 
banks. Undoubtedly, this situation can be considered a 
success in terms of financial performance and profitability of 
participation banks. Here, by guaranteeing a margin equal to 
the increase in the exchange rate instead of the decrease in 
interest rates, the participation shares to be paid by the banks 
was reduced and transferred to the responsibility of the 
treasury initially and then to the CBRT. Thus, as the analysis 
indicates, the profitability of participating banks increased 
significantly during the implementation period. 

4.2 Results of fuzzy decision-making model 

In the fuzzy evaluation phase, ten experts were selected 

using purposive sampling to ensure both academic and 

practical representation. The panel included five senior 

executives from participation banks, three academics 

specializing in Islamic finance and risk management, and two 

policy experts from regulatory institutions. The main 

selection criteria were a minimum of 15 years of professional 

experience, direct involvement in participation banking 

operations or regulation, and recognized expertise 

demonstrated by certifications or publications in related 

fields.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Based on the experts' importance priorities, the matrix is 

constructed using age, total years of experience, industry 

experience, manager experience, and the number of patents 

or certificates held by the experts. Then, the arithmetic means 

and standard deviations of the columns are computed to 

obtain z-score values. It is identified that the average age of 

experts is 51.2. Moreover, the minimum total experience is 20 

years. Similarly, the minimum manager experience of the ten 

experts is 9.7 years. Next, using these statistical values, a 

standardized matrix is obtained. Afterwards, the negative 

values are determined. Euclidean distances between experts 

and negative values are calculated. Finally, the experts' scores 

are defined by normalizing the distances. The details of the 

experts' scores are shown in Figure 1. 

Figure 1 shows that the most important evaluation is 

Expert-6 with 0.210. This expert has the maximum age, total 

experience years, industry experience years, and manager 

experience years. Expert-6 received the highest total weight 

in the Fermatean fuzzy expert-weighting process.  It is crucial 

to remember that this outcome does not suggest priority 

based only on age or years of experience.  Instead, following 

standardization, a number of factors are combined to 

establish the final weight.  Although the standardization 

procedure guarantees comparability across several scales, it 

can potentially magnify relative disparities for specific 

features.  Because of this, even though the weighting 

algorithm considers all factors simultaneously, age and 

experience seem to have a greater impact.   

 

Table 1. Panel data analysis results of models for return on assets (ROA) 

Variabl
e 

Pooled Fixed Effects 
Random Effects Robust Fixed Effects Robust Random Effects 

 Coef. t Coef. t Coef. z Coef. T Coef. Z 
CHDΔ 0.001* 2.44 0.001* 1.69 0.001* 2.44 0.001* 1.9 0.001** 2.62 
TLTA -0.041* -2.57 -0.011* -0.53 -0.041* -2.57 -0.0078 -0.34 -0.041* -2.43 
ETA 0.060 1.69 0.279** 2.71 0.060 1.69 0.249 1.95 0.060 1.58 

PETE -0.012 -0.79 0.015 0.56 -0.012 -0.79 0.015 0.53 -0.010 -0.58 
OBATA 0.001* 2.4 0.001* 0.13 0.001* 2.4 0.000 0.24 0.001* 2.21 
GNPΔ -0.001 -0.6 -0.001 -0.69 -0.001 -0.6 -0.001 0.02 -0.001 -0.58 
lnCPI -0.019 -2.37 -0.022* -2.59 -0.019* -2.37 -.0223* -2.32 -0.021* -2.52 
INT -0.001 -0.93 0.001 0.21 -0.001 -0.93 0.001 0.02 -0.001 -0.81 

lnGR 0.000 -0.22 0.010 1.97 0.000 -0.22 0.008 1.39 0.000 -0.23 
lnIPI 0.054**

* 
4.47 

0.069**
* 

5.44 
0.054**

* 
4.47 0.072*** 5.13 0.055*** 4.66 

CURΔ -0.016 -0.69 -0.017 -0.76 -0.016 -0.69 -0.005 -0.18 -0.014 -0.59 
_cons 

-0.118 -1.63 
-

0.389** 
-3.38 -0.118 -1.63 -0.322* -2.69 -0.118 -1.62 

r2_w   0.635  0.574  0.585  0.576  
sigma_

u 
  0.007  0  0.006  0  

sigma_e   0.005  0.005  0.005  0.005  
Rho   0.686  0      

F 7.98***  7.76***    5.52***    
LR 

  
63,69**

* 
       

Wald 
chi2 

    87.7***    80.6***  

e(lm)     0      
Hausm

an 
  9.84*        

DB   2.15        
LBI   2.31        

 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

239 

 

The empirical result and our conceptual argument that 

assigning weights solely on the basis of experience would be 

impractical are reconciled by this explanation. The criteria set 

is financial performance (FNPRF), risk management 

(RSKMNG), interest-free finance compliance (INFNNCMP), 

macroeconomic impact (MCRIMP), and policy impact 

(PLCIMP) for the evaluation process. Ten experts evaluate the 

importance of these criteria. These linguistic evaluations are 

converted to Fermatean fuzzy numbers. Thus, the input 

decision-making matrix is defined. Next, the weighted matrix 

is obtained.  

The E values are used as weight values. Afterwards, the 

weighted matrix’s values are defuzzified. Then, the 

normalized matrix’s values are calculated. After that, the 

maximum value of each criterion is selected, and the 

minimum value of each criterion is determined. Then, the 

difference between these values of each criterion is 

calculated. The weights of the criteria are defined by 

normalizing the difference values. Finally, the reliability index 

is computed for the reliability test. For this, the second 

evaluations are collected using a score of 0 to 100 for each 

criterion by experts. Next, scores for each criterion are 

averaged. The details of the weighting results are given in 

Table 2. 

Table 2 identifies that RI is .085. In other words, this 

value is smaller than 0.1 [20]. Thus, the result is reliable.  In 

this case, the most important criterion is interest-free finance 

compliance with .294. The second important criterion is risk 

management with .223. In addition, sensitivity analysis is 

performed. For this, scenarios are constructed with minimal 

changes to each expert's score value.  

 

 

 

Figure 1. Scores of the experts 

 

 

 That is, for the first scenario, the score of the first expert 

is increased by 10% and CIMAS is applied by normalizing the 

score values. This tests the robustness of the results against 

expert input. The results are shared in Table 3. As shown in 

Table 3, the criteria's priorities are the same across scenarios. 

This demonstrates how robust the results are to expert input. 

As the research reveals, compliance with interest-free finance 

principles and effective risk management are the two primary 

factors determining the integration of currency-hedged 

deposit products into the systems of participation banks in 

Turkey.  

Participation banking, by its very nature, rejects interest-

based transactions and prioritizes asset-based, real-economy 

financial activities. Therefore, the use of products such as 

foreign exchange-hedged deposits within the framework of 

participation finance is only possible if they are designed in 

full compliance with the principles of interest-free finance. 

The inclusion of interest-like return mechanisms or 

speculative currency risk within the product poses a serious 

risk of non-compliance for participation banking. Therefore, 

compliance with interest-free terms is a prerequisite for the 

ethical and legal acceptance of these products. 

Risk management for participation banks is much more 

complex than for conventional banks, as interest-free finance 

principles are based on risk sharing but reject speculative 

risk-taking (gharar). Factors such as exchange rate volatility, 

market risk, and liquidity risk are inherently high in foreign 

exchange-protected deposit products. Therefore, it becomes 

critical for participating banks to integrate both Sharia-

compliant risk management tools and modern financial 

protection mechanisms. 

 

 

 

 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

240 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. Conclusion  
This study aimed to examine the financial and ethical 

implications of the Currency-Hedged Deposit (CHD) system 
for participating banks in Turkey, employing an integrated 
analytical framework combining econometric modeling and 
fuzzy decision-making techniques. The motivation for this 
research stemmed from the growing need to assess how 
macro-level stabilization policies, such as the CHD 
mechanism, influence financial performance, risk exposure, 
and compliance with interest-free principles of participation 
banks. In the first stage of the analysis, a panel data regression 
is conducted using quarterly data from six participating banks 
from 2021 to 2023. The findings demonstrated that the CHD 
variable has a positive, statistically significant effect on the 
profitability indicators—particularly return on assets 
(ROA)—of participating banks. This suggests that the CHD 
mechanism contributed to short-term improvements in 
profitability and liquidity stability by mitigating currency risk 
and transferring part of the risk burden to public financial 
institutions. However, the study also emphasizes that the 
sustainability of such improvements depends on maintaining 
compliance with Islamic financial ethics and effective long-
term risk management strategies. In the second stage, the 
study introduced a hybrid decision-making approach using a 
Euclidean distance-based CIMAS model with Fermatean fuzzy 
sets to incorporate expert judgments. The fuzzy analysis 
revealed that interest-free finance compliance and risk 
management are the most critical criteria for the ethical 
legitimacy and systemic sustainability of participation banks 
under the CHD framework. Sensitivity analysis confirmed the 
robustness and internal consistency of these results, 
highlighting that adherence to Sharia principles and effective 
risk governance must coexist to ensure sustainable 
innovation in Islamic finance. This study contributes to the 
literature in several ways. First, it integrates quantitative 
econometric evidence with fuzzy logic-based decision 
modeling, providing a multidimensional understanding of 
financial innovation in Islamic banking. Second, it introduces 
a novel methodological framework that bridges the empirical 
rigor of econometrics with the flexibility and uncertainty-
handling capabilities of fuzzy systems.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Third, it offers valuable insights for regulators and 
policymakers aiming to balance financial innovation, ethical 
compliance, and systemic stability in developing countries. 
The two stages of the analysis are conceptually and 
empirically interconnected. The econometric results obtained 
in the first stage provided a quantitative foundation for the 
fuzzy evaluation by identifying which financial and 
macroeconomic variables—such as CHD growth, equity 
ratios, and personnel expenses—significantly affect 
profitability. These statistically significant factors were then 
translated into expert-assessed criteria within the fuzzy 
model, in which experts evaluated their relative importance 
for ethical compliance, risk management, and macroeconomic 
relevance. Conversely, the fuzzy stage contextualized and 
validated the econometric outcomes by highlighting that 
profitability gains from CHD are sustainable only when 
supported by sound governance and compliance with the 
principles of interest-free finance. In this way, the fuzzy 
decision-making model not only complements but also 
interprets the econometric findings, creating a feedback loop 
that enhances both the analytical rigor and the practical 
implications of the study. Nevertheless, this research is 
subject to certain limitations. The dataset covers a relatively 
short time period (2021–2023), given the recent introduction 
of the CHD system, which may restrict the generalizability of 
long-term effects. Moreover, the fuzzy decision-making model 
relies on expert-based judgments, which, while statistically 
validated, may contain subjective biases. Future research 
could extend this work by adopting longer time horizons, 
incorporating additional macroeconomic variables, and 
conducting cross-country comparisons to understand how 
similar mechanisms operate across different Islamic banking 
ecosystems. Furthermore, integrating machine learning 
algorithms with fuzzy inference systems could enhance 
predictive accuracy and enable dynamic decision-support 
models for policy analysis. In conclusion, the study 
underscores that while the CHD system can temporarily 
strengthen the financial performance of participating banks, 
its sustainable success requires a careful balance between 
economic efficiency, ethical compliance, and robust risk 
management—an equilibrium that hybrid analytical 
frameworks such as the one proposed here are particularly 
well-suited to evaluate. 

Table 2. Significance weights of the criteria 

 FNPRF RSKMNG INFNNCMP MCRIMP PLCIMP 

Maximum .250 .306 .386 .199 .203 
Minimum .020 .019 .009 .007 .005 
Difference .230 .286 .377 .192 .198 

Weight .179 .223 .294 .150 .154 
Second  17 21.2 27.9 14.2 19.7 

RI .085 
 

Table 3. Sensitivity analysis results 

 SC-1 SC-2 SC-3 SC-4 SC-5 SC-6 SC-7 SC-8 SC-9 SC-10 SIWEC 

FNPRF 3 3 3 3 3 3 3 3 3 3 3 
RSKMNG 2 2 2 2 2 2 2 2 2 2 2 

INFNNCMP 1 1 1 1 1 1 1 1 1 1 1 
MCRIMP 5 5 5 5 5 5 5 5 5 5 5 
PLCIMP 4 4 4 4 4 4 4 4 4 4 4 

 

 



S. Duranet al. /Future Technology                                                                                     February 2026| Volume 05 | Issue 01 | Pages 234-241 

241 

 

 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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