An Empirical Analysis of the Impact of Financial Leverage on the
Performance of Second-Level Banks in Albania
ELTON GUBERAJ1,a, DENISA PIPERO KURTAJ2, ANA KAPAJ3,b
1Department of Finance and Accounting,
Faculty of Economics and Agribusiness, Agricultural University of Tirana,
Rruga Paisi Vodica 1025, Tirana
ALBANIA
2Department of Agri-Business Management,
Agricultural University of Tirana,
Rruga Paisi Vodica 1025, Tirana,
ALBANIA
3Department of Mathematics and Informatics
Faculty of Economy and Agribusiness, Agricultural University of Tirana
Rruga Paisi Vodica 1025, Tirana,
ALBANIA
aORCiD: 0009-0009-9554-5452
bORCiD: 0000-0002-2406-2164
Abstract: - The financial scene in Albania, has seen some significant changes over time, shaped by different
market situations and rules that put specific limits on financial choices. In today's world, where globalization
and competition are running high, making smart financial moves is crucial for a company's success. Most
studies on traditional finance have been mostly focused on analyzing how a company's everyday performance
is connected with the amount of debt. These studies try to investigate the complex relationship between
financial leverage and the factors that make a company successful. Deferent researchers have been looking into
the basic details of financial leverage, focusing on theories like the pecking order, which suggests that
companies Fprefer using their own money rather than borrowing from outside. Despite having an assembly of
theories, there's no one answer for how a company should balance its money or which financial moves
guarantee success. Every business is different, dealing with unique challenges in its way. Understanding how a
company acquires its funding and where it originates plays a crucial role for businesses aiming to economize
while trying to achieve substantial growth. This highlights how important it is to set up a money plan that looks
out for the company's interests. Interestingly, in a market where there's no tax on money moves, the way a
company sets up its money doesn't seem to change how much it's worth. Businesses have room to refine how
much debt they carry, both in the short and long term, based on big investment decisions and the need for quick
cash. The exact effect of leverage depends on several variables, such as the firm's risk appetite, the state of the
economy, and the dynamics of the industry. To summarize, the main goal of this empirical investigation is to
clarify the relationship between financial leverage and the performance curve of Albania's second-level banks
throughout the ten years from 2012 to 2022. This investigation seeks to clarify if financial leverage methods
were modified by Albanian banks, especially second-level banks, in response to changing market dynamics and
regulatory changes, and how these modifications affected the banks' overall performance.
Key-Words: - Financial leverage, Second-level banks, Risk Diversification, Effective Debt Management,
Return on Equity, Return on Assets.
Received: July 11, 2023. Revised: February 13, 2024. Accepted: April 9, 2024. Published: May 2, 2024.
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1 Introduction
As second-level banks keep on playing a
fundamental job in provincial financial turn of
events, evaluating their monetary influence
techniques becomes pivotal not just for scholarly
research yet in addition for policymakers and
monetary foundations, [1], [2]. Subsequently, this
exploration tries to add to the more extensive talk on
monetary influence and bank execution while
offering experiences into the particular elements of
second-level banks in Albania. [3], in synopsis, late
writing proceeds to support the repetitive negative
relationship between monetary influence and bank
or firm execution, underscoring the significance of
considering the more extensive monetary and
administrative setting, [4]. This study means to
broaden our comprehension by zeroing in on
second-level banks in Albania, a district with
particular elements, and investigating the
remarkable factors that shape their influence
execution elements while coordinating experiences
from these essential hypothetical systems. [5]
The perplexing relationship between budgetary
use and bank execution has been a noticeable
subject of monetary inquiry about, captivating
researchers around the globe. Whereas broad
inquiry about has investigated the suggestions of use
on bank benefit, soundness, and chance, the part of
second-level banks remains a progressively
important but less-explored range. [6], [7], a
comprehensive audit synthesized discovery from
various ponders on the effect of monetary use on the
execution of money-related teach. The survey
fortified the agreement that a considerable extent of
these ponders watched a negative affiliation
between use and bank execution. Higher use was by
and large connected to decreased productivity and
increased budgetary chance, adjusting with the
pecking arrange hypothesis, which sets that firms
favor inner financing over the outside obligation to
keep up monetary steadiness, [8].
On this discourse, later inquiries about has dug
into the complicated exchange between monetary
use and bank execution. A striking think about,
conducted in 2023, centered on the European
managing an account segment and scrutinized
information traversing a long time from 2010 to
2020. The examination yielded profitable
experiences into the different impacts of budgetary
use watched over unmistakable European nations
and managing an account teach. The discoveries
underscored that the relationship between use and
execution is exceedingly subordinate to the one
of a kind financial and administrative conditions
winning in each nation, [9], [10]. Besides, an
examination carried out by analysts in 2020
inspected the execution of Chinese commercial
banks all through the period from 2010 to 2019. The
ponder uncovered that, even though budgetary use
might support benefits within the brief term, it
might subject banks to raised budgetary chance over
an amplified period. This underscores the worldly
elements and persevering results related to the effect
of the use on bank execution, [11]. Within the
setting of territorial or second-level banks, more
lately have too investigated how these teachers
explore the complexities of monetary use in
particular geographic and administrative situations.
[12], address this hole, the display inquiries about
centers on second-level banks in Albania, an
energetic advertise with interesting characteristics.
2 Research Methodology
The information utilized for this study is optional
information gathered from Opencorporates, a solid
hotspot for monetary data on organizations working
in Albania. The dataset incorporates key monetary
factors like aggregate obligation, capital, resources,
benefits, and total compensation for second-level
banks working in the Albanian economy. These
monetary measurements give fundamental bits of
knowledge into the monetary well-being and
execution of the chosen banks. [13], the choice of
banks for this study was made fastidiously to
guarantee variety furthermore, portrayal inside the
Albanian banking area. In particular, information
from 11 second-level banks was picked for
examination, covering the broad period from 2012
to 2022. These chosen banks incorporate both
homegrown and worldwide monetary organizations,
offering a far-reaching view of the monetary scene
in Albania. The decision of these particular banks
was driven by the point of directing a strong
experimental investigation of the connection
between monetary influence pointers and monetary
execution measurements over the assigned enough
said. By zeroing in on this assorted arrangement of
banks, this study looks to give important
experiences into how monetary influence techniques
have affected the monetary results of different
financial establishments in Albania.
This study plans to uncover the importance of
monetary influence on the presentation of
organizations in Albania. By deciding the focal
point of the review, we distinguish the free factor,
which is monetary influence. Then again, we will
quantify an organization's exhibition through ROA
(Return on Resources), ROE (Return on Value),
also, net revenue. The information from the chosen
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banks will serve as the establishment for the
examination in this research, empowering an
assessment of the connection between monetary
influence markers and monetary execution
measurements throughout the predetermined period.
2.1 Hypothesis of the Study
Hypothesis 1 (ROA): There is a significant
relationship between the financial leverage
indicators and ROA.
Hypothesis 2 (ROE): There is a significant
association between the financial leverage indicators
and the Return on Equity (ROE).
Hypothesis 3 (NPM - Net Profit Margin): There is
a significant correlation between the financial
leverage indicators and the Net Profit Margin
(NPM).
2.2 Statistical Summary of Studied Variables
Table 1 provides a statistical summary of the
variables under study. The mean value of the net
profit margin stands at 97.3%. However, the average
return on assets is 0.83%, indicating that
approximately 8% of second-level banks' total
revenue is generated from the utilization of their
total assets. The average return on debt is 8.6%,
implying that nearly 9% of the bank's revenue is
derived from debt usage. The mean debt-to-equity
ratio is 90.4%. Simultaneously, the debt ratio has an
average of 93.4%, signifying that 93.4% of the
assets are financed through total liabilities.
This statistical summary provides an initial
glimpse into the financial characteristics of the
selected banks and sets the stage for further
analysis. The figures reveal key indicators of
financial leverage and performance, offering
insights into the financial strategies and structures of
these banking institutions.
Table 1. Statistical Summary of the Studied
Variables
Variable
Mean
Median
ROA
0.00831
0.0070
ROE
0.08623
0.0640
DE
9.0404
8.3637
DEB
9.3971
63.874
NPM
0.97368
1.000
Source: Author, 2023
Correlation analysis serves as a fundamental
tool to gauge the strength and direction of linear
relationships between variables, [14], [15]. The
correlation coefficient, which ranges between -1 and
1, is employed to measure these relationships. In
Table 3, we present the correlation matrix, shedding
light on the interplay among the studied variables.
One critical aspect to consider in correlation
analysis is the presence of multicollinearity, where
independent variables are highly correlated with
each other. Multicollinearity issues typically
become significant when correlation coefficients
exceed 0.80, [16]. In our analysis, we do not
observe such high correlations among the variables,
suggesting that multicollinearity is not a major
concern in this study.
Furthermore, it's essential to contextualize these
findings within the broader landscape of financial
research to echo the importance of assessing
correlations between financial leverage indicators,
such as debt-to-equity ratios, and financial
performance metrics like return on assets and return
on equity. The negative correlation observed
between return on assets (ROA) and the debt-to-
equity ratio (DE), as well as the debt-to-EBITDA
ratio (DEB), is consistent with the findings of these
prior studies. This negative correlation suggests that
as the reliance on debt financing increases, the
return on assets tends to decrease, aligning with the
risk-return trade-off theory, [17].
Conversely, the positive correlation between
return on equity (ROE) and these leverage
indicators aligns with the insights that suggest an
optimal mix of debt and equity in a firm's capital
structure could enhance return on equity. Therefore,
our correlation analysis not only corroborates the
existing body of literature but also underscores the
importance of assessing these relationships in the
specific context of Albanian second-level banks.
Overall, this correlation analysis not only
provides valuable insights into the relationships
among the variables under examination but also
highlights their alignment with established financial
theories and empirical findings from prior research
(Table 2).
Table 2. Correlation analysis between variables
ROA
ROE
DE
DEB
NPM
ROA
1.0000
0.7425
-0.008
-0.019
0.0405
ROE
1.0000
0.1867
0.0138
0.0483
DE
1.0000
0.0728
0.0144
DEB
1.0000
0.0514
NPM
1.0000
Source: Author, 2023
3 Methodology
In this paper, we have examined two econometric
models, namely the classic linear model and the log-
linear model, to determine which of these models
better suits our study. Initially, Table 4 presents the
first regression model with a linear equation:
y=a+bx+cx+u, where specifically
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ROA=0.00791093+2.62464005DE+1.76081006
DEB. As we can observe, the model is not
statistically significant, as none of the variables
appear to be significant
However, none of the variables in the model
appear to be statistically significant, as evidenced by
the p-values exceeding the typical significance level
of 0.05.
Additionally, the R-squared value, which
measures the proportion of variation in ROA
explained by the model, is extremely low at
0.000422, indicating that only 0.0422% of the
variation in ROA is accounted for by DE and DEB.
These findings suggest that the linear model
may not adequately capture the relationship between
the variables, prompting further exploration using
alternative models, such as the log-linear model, to
potentially yield more meaningful results. It's
important to note that the lack of statistical
significance in this model may be due to various
factors, and further analysis is warranted to better
understand the dynamics at play in the context of
Albanian second-level banks.
Table 3. The first two-factor linear regression model
Model 1: Regression Analysis
Dependent Variable ROA
Coefficient
St. Error
t-ratio
p-value
Const
0.0079
0.0042
1.844
0.0676
DE
2.6246
0.0003
0.073
0.9415
DEB
1.7608
8.5915
0.205
0.838
Mean dependent var
0.0083
SD dependent var
0.03
Sum squared residual
0.11
SE of regression
0.03
R squared
0.0004
Adjusted R squared
-0.01
F (2,118)
251.96
P-value
0.97
Log-likelihood
251.9
Akaike criterion
-497.9
Schwarz
-489.5
Hannan-Quinn
-494.5
rho
0.13
DW
1.687
Source: Author, 2023
Table 4. Second Linear Two-Factorial Regression
model Model 2: Regression Analysis
Dependent Variable ROE
Coefficient
St. Error
t-ratio
p-value
Const
0.028
0.037
0.78
0.436
DE
0.006
0.003
2.059
0.0417
DEB
1.336
7.412
0.001
0.998
Mean depend. var
0.086
SD depend. var
0.265
SS residual
8.191
SE of regression
0.263
R squared
0.034
Adjusted R sq
0.018
F (2,118)
2.13
P-value
0.123
Log-likelihood
-8.78
Akaike criterion
23.56
Schwarz
31.95
Hannan-Quinn
26.97
rho
0.212
DW
1.516
Source: Author, 2023
Within Table 4, we present the outcomes of the
regression analysis, where ROE (Return on Equity)
serves as the dependent variable. The model
specification is articulated as follows:
ROE = 0.0288906 + 0.00634188DE +
1.33649007DEB (1)
Employing a significance level of α = 0.01, we
observe that the variable DE attains statistical
significance. This finding substantiates the overall
significance of the model. The coefficient of
determination (R-squared) is computed at 3.4865%,
implying that 3.4865% of the variation in ROE is
explicable through the influence of these two key
factors. Interpretation of Model Coefficients:
The coefficient 0.0288906 elucidates the
anticipated value of ROE when both DE and DEB
are held at zero.
The coefficient 0.00634188 conveys how
alterations in ROE are expected to manifest when
DE undergoes a unitary change, all while
maintaining DEB at a constant level.
The coefficient 1.33649007 signifies the
anticipated magnitude of variation in ROE when
DEB experiences a unitary alteration, under the
condition that DE remains unaltered.
These coefficients furnish a quantitative
comprehension of the intricate interplay between
DE, DEB, and ROE. They provide empirical
evidence that DE emerges as a statistically
significant predictor, underscoring its pivotal role in
elucidating fluctuations in ROE among the chosen
banks. Since the model demonstrated significance
only in the second case, and both instances resulted
in low R-squared (R^2) values, it implies that this
model may not be suitable for explaining the
variation and relationship between the dependent
and independent variables adequately.
Consequently, driven by these outcomes, we
proceeded to assess the log-linear model with the
primary equation lny = a + bx + u.
lnROA = −3.73379 − 0.00731562DE −
0.00276226DEB − 0.0753977t (2)
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Table 5. Log-linear Model with the Dependent
Variable as the Natural Logarithm of ROA
Model 3: Log Lin
Dependent Variable ROA
Coeff
St. Error
t-ratio
p-value
Const
-3.73
0.24
-15.18
˂0.0001
***
DE
-0-007
0.01
-0.713
0.0477
*
DEB
-0.002
0.0003
-7.411
˂0.0001
***
time
-0.075
0.029
-2.539
0.01
**
Mean depen. var
-4.69
SD depend. var
1.078
SS residual
64.73
SE of regression
0.857
R squared
0.388
Adjusted R sq
0.367
F (3,88)
18.6
P-value (F)
1.91e.09
Log-likelihood
-114.3
Akaike criterion
236.7
Schwarz
246.8
Hannan-Quinn
240.81
rho
0.258
DW
1.148
Source: Author, 2023
Model Interpretation:
The coefficient −3.73379 signifies the expected
value of ROA when both DE and DEB are set equal
to 1.
The coefficient−0.00731562 illustrates that for a
1% increase in DE, ROA is expected to decrease by
0.731562%, assuming DEB remains constant.
The coefficient −0.00276226 indicates that for a
1% increase in DEB, ROA is expected to decrease
by 0.276226%, provided DE remains unchanged.
The coefficient−0.0753977 represents the
anticipated reduction in the value of ROA when all
other factors remain unchanged (time effect).
Furthermore, the R-squared (R^2) value for this
log-linear model is calculated at 38.8%, signifying
that 38.8% of the variation in ROA can be
elucidated by the selected factors.
In light of these findings, it becomes evident
that the log-linear model provides a more suitable
framework for explaining the variation in ROA and
the relationships between the variables, as
evidenced by the significantly improved R^2 value
compared to the linear:
lnROE = −1.79574 + 0.0268620DE +
0.00274405DEB − 0.0740850t (3)
Table 6. Log-linear Model with the Dependent
Variable as the Natural Logarithm of ROE
Model 4 Log Lin
Dependent variable ROE
Coeff.
St. Error
t-ratio
p-value
Const
-1.79
0.25
-7.101
˂0.0001
***
DE
0.026
0.01
2.548
0.012
**
DEB
0.002
0.0003
-7.161
˂0.0001
***
time
-0.074
0.03
-2.427
0.017
**
Mean depen. var
-2.41
SD depend. var
1.117
SS residual
68.42
SE of regression
0.88
R squared
0.86
Adjusted R sq
0.929
F (3,88)
19.35
P-value (F)
1.01e.09
Log-likelihood
-116.9
Akaike criterion
241.8
Schwarz
251.9
Hannan-Quinn
245.9
rho
0.334
DW
1.045
Source: Author, 2023
Model Interpretation:
The coefficient −1.79574: represents the expected
value of ROE when both DE and DEB are set equal
to 1.
The coefficient +0.0268620: signifies that for a
1% increase in DE, ROE is expected to increase by
2.6862%, with the condition that DEB remains
constant.
The coefficient 0.00274405 indicates that for a
1% increase in DEB, ROE is expected to increase
by 0.274405%, provided DE remains unchanged.
The coefficient −0.0740850 denotes the
anticipated decrease in the value of ROE when all
other factors remain unchanged (time effect).
Additionally, the coefficient of determination
(R-squared, R^2) for this log-linear model is
calculated at 86.46%, signifying that 86.46% of the
variation in ROE can be elucidated by the selected
factors.
These findings underscore the suitability of the log-
linear model for explaining variations in ROE and
the interrelationships among the variables, as
exemplified by the notably higher R^2 value in
comparison to the linear model.
lnNPM=−0.538852+0.00119217DE−0.00031169D
EB+0.0749829t (4)
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Table 7. Log-linear Model with the Dependent
Variable as the Natural Logarithm of NPM
Model 5 Log Lin
Dependent variable NPM
Coefficient
St. Error
t-ratio
p-value
Const
-0.53
0.138
-3.883
0.0002
***
DE
0.0011
0.007
0.1606
0.872
DEB
-0.0003
0.0001
-1.714
0.089
*
time
0.0749
0.018
4.002
0.0001
***
Mean depend. var
-0.112
SD depend. var
0.674
SS residual
44.15
SE of regression
0.63
R squared
0.148
Adjusted R sq
0.125
F (3,111)
6.43
P-value (F)
0.0004
Log-likelihood
-108.1
Akaike criterion
224.28
Schwarz
235.2
Hannan-Quinn
228.7
rho
0.254
DW
1.174
Source: Author, 2023
Model Interpretation:
The coefficient -0.538852 indicates the expected
value of NPM when DE and DEB are both equal to
1.
The coefficient +0.00119217 signifies that if DE
increases by 1%, NPM is expected to increase by
0.119217%, assuming DEB remains unchanged.
The coefficient -0.00031169 suggests that if
DEB increases by 1%, NPM is expected to decrease
by 0.031169%, with DE remaining unchanged.
The coefficient +0.0749829 represents the
anticipated increase in NPM when all other factors
remain unchanged (the time effect).
The R-squared value of 14.80% indicates that
14.80% of the variation in NPM is explained by the
selected factors.
The specified model in Table 7 provides
insights into the relationship between NPM (Net
Profit Margin), DE (Debt to Equity ratio), DEB
(Debt to EBITDA ratio), and time. Notably, the
positive coefficient for DE suggests that an increase
in the Debt to Equity ratio is associated with a
higher Net Profit Margin, assuming DEB remains
constant. Conversely, the negative coefficient for
DEB suggests that an increase in the Debt to
EBITDA ratio is associated with a decrease in the
Net Profit Margin, assuming DE remains constant.
The time effect, represented by the coefficient
+0.0749829, indicates that over time, there is an
expected increase in the Net Profit Margin, while
other factors remain unchanged. However, it's
important to note that the R-squared value of
14.80% suggests that the model explains only a
modest portion of the variation in NPM, indicating
that there may be other unaccounted-for factors
influencing Net Profit Margin.
Further analysis and investigation may be needed to
uncover additional variables or external factors that
could contribute to variations in NPM, thus
providing a more comprehensive understanding of
the determinants of profitability in the context of the
study.
In summarizing the findings of the analysis
conducted using the statistical software Gretl, we
aim to consolidate the key outcomes of this study
and ascertain whether the hypotheses proposed are
supported or not. Initially, the appropriate model for
studying these variables was identified as the log-
linear model, where the dependent variable is
logarithmically transformed. In each table of this
model, it becomes apparent that the variables exhibit
varying levels of significance, indicating that each
of them must be examined to understand the impact
of financial leverage on the financial performance of
second-level banks.
Based on Table 5, Table 6 and Table 7, which
correspond to the analysis of the log-linear model
for lnROA, lnROE, and lnNPM, it is evident that the
coefficients of DE and DEB are statistically
significant. This means that H_0 is rejected, and
Hypothesis 1 is accepted, implying that the
relationship between performance indicators and
financial leverage is significant. Additionally, from
these tables, it is also apparent that another
significant factor is time, which either diminishes or
enhances performance indicators.
To sum it up, from the analysis we can conclude
that financial leverage, particularly the debt-to-
equity ratio, has significant effects in explaining
ROA and ROE. All the summary analysis are given
in Table 8.
Table 8. Summary of Analysis Results
DE = -
0.00731562
p = 0.04774 (<0.05, α=95%) – debt-to-equity
ratio has a significant effect on ROA
DEB =
0.00276226
p = 0.0001 (<0.01, α=99%) – debt-to-equity
ratio has a significant effect on ROA
t = -0.0753977
p = 0.0129 (<0.05, α=95%) – debt-to-equity
ratio has a significant effect on ROA
DE =
0.0268620
p = 0.0126 (<0.05, α=95%) – debt-to-equity
ratio has a significant effect on ROE
DEB =
0.00274405
p = 0.0001 (<0.01, α=99%) – debt-to-equity
ratio has a significant effect on ROE
t = -0.0740850
p = 0.0173 (<0.05, α=95%) – debt-to-equity
ratio has a significant effect on ROE
DE =
0.00119217
p = 0.8727 (>0.05, α=95%) – debt-to-equity
ratio doesn’t have a significant effect on net
profit margin
DEB = -
0.00031169
p = 0.0893 (<0.09, α=90%) – debt-to-equity
ratio has a significant effect on net prifit
margin
t = 0.0749829
p = 0.0001 (<0.01, α=99%) – debt-to-equity
ratio has a significant effect on net prifit
margin
Source: Author, 2023
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3.1 Model Selection Significance
The choice of the log-linear model over linear
deserves a closer look. Since the log-linear model is
better than linear models, it implies that there is
some nonlinearity about financial leverage and bank
performance. This shows that the effect of changes
in leverage on financial performance is not
homogeneous, but different at various levels of
leverages requiring a logarithmic transformation to
include these discrepancies adequately.
Significance of Financial Leverage Metrics: The
coefficient’s DE (debt-to-equity ratio) and
DEB(debt-to-equity ratio before tax in the log linear
models ROA and ROE are significant statistical
findings. It means that these leverage metrics are
critical in influencing the financial results of
second-level banks. Still, their influence is not
equal. This means that the management of debt and
equity ratios is a complex process, and banks
require multiple variables to determine their ideal
capital structure.
Temporal Dynamics: The temporal effect
observed in the analysis is an important finding. The
coefficient “t” in the log-linear models for ROA and
ROE suggests time dependency on bank
performance. These changes may be attributed to
economic trends, regulatory developments, market
dynamics, or banking strategies. As a result, banks
should change to dynamic financial management to
better cope with the ever-growing and changing
environment.
Net Profit Margin (NPM) Distinction: The
analysis emphasized the unique effect that DEB has
on NPM as compared to DE. DEB showed a strong
impact on NPM while the same was not true in
respect to DE. Such a differential effect highlights
the relevance of disaggregating leverage metrics in
assessing profitability. The composition of debt and
the schedule for interest payments can have
different impacts on net profit margins, which
should be understood by financial institutions.
Hypotheses Confirmation Relevance: The
confirmation of Hypothesis 1 emphasizes the
practical significance of research results. The
described significant relationship between financial
leverage indicators and bank performance measures
supports the premise that effective management of
financial leverage is an important predictor variable
that influences a bank’s profitability, as well as its
equity returns. These findings have practical
implications for banks seeking to improve financial
performance.
Overall, this in-depth analysis shows that the
link between financial leverage and bank
performance is complex. The above complexity of
this relationship can be seen in the observation that
includes choosing a log-linear model, varying
importance for different leverage metrics over time
dynamics, and variable impact on profitability.
Banks operating in the same context should embrace
a comprehensive and dynamic attitude to manage
financial leverage, especially due to the nature of
changeable circumstances within which it is
necessary The study brings significant depth to the
understanding of financial management in the
banking sector and important managerial insights
for decisions that would lead to sustained finical
prosperity.
4 Conclusions
The analysis shows that every bank in Albania uses
financial leverage as an essential element of its
financing framework. This result supports the
widespread use of leverage as a financial business
strategy by banks in the country to attain efficiency
in capital structure and reach such goals.
Financial leverage is an effective tool for risk
diversification. Leverage can serve as a strategic
approach for banks to diversify their financial risk,
which can maintain the firm’s capital structure and
may contribute positively to financial stability.
A good debt-to-equity ratio is crucial for
positive financial performance. In its analysis, the
study points out that efficient debt-to-equity ratio
management can bring success to the financial
performance of second-level banks. Banks should
be aware of their debt levels and ensure that they are
in line with the overall financial plans.
Through the study, it is evident that
performance indicators have a direct and significant
relationship with leverage indicators. The debt-to-
equity ratios affect the return on asset (ROA) and
ROE of banks positively. This brings out the
importance of financial leverage management in
shaping bank profitability and shareholder returns.
The analysis also highlights the temporal
dynamics involved. The performance indicators may
improve or worsen with time. However, banks must
be on guard and dynamic in dealing with these
seasonal changes.
5 Recommendations
A detailed analysis of financial leverage in every
bank operating in Albania is one of the
recommendations we can deliver from this study.
This would enable us to determine whether the
financial leverage has a positive or negative impact
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.91
Elton Guberaj, Denisa Pipero Kurtaj, Ana Kapaj
E-ISSN: 2224-2899
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Volume 21, 2024
on each particular financial institution. Banks
should develop a customized leverage strategies, in
accordance with their specific financial situation.
Banks should establish a system of continuous
monitoring and adjustment in their leverage
strategies. This forward-looking strategy will ensure
that banks react appropriately when the market
conditions, regulatory environment, and economic
setting change.
In parallel with financial resource leverage,
banks should also create effective risk mitigation
strategies. These approaches should incorporate all-
around risk estimation, stress testing, and
contingency planning to maintain financial stability
in case of unfavourable situations.
Another recommendation will be related to Data
Analytics. Data analytics and modeling capabilities
is an important point in which banks need to invest,
especially when they want to understand how
financial leverage affects their performance. This
will allow data-based decision making and accurate
adjustment of leverage strategy.
To summarize, this study reveals the critical
function of financial leverage in influencing the
behavioral performance of second-level banks in
Albania. It offers valuable information that not only
helps banks and policymakers in defining their
financial strategies but also impacts positively on
banking stability, efficiency, and profitability.
Through a proactive and data-driven financial
leverage management approach, banks can help
maintain the stability of the open economic system
across this country in view of changed dynamics
within finances.
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DOI: 10.37394/23207.2024.21.91
Elton Guberaj, Denisa Pipero Kurtaj, Ana Kapaj
E-ISSN: 2224-2899
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Volume 21, 2024
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Dr. Elton Guberaj, as the first author, carried out
conceptualization, data curation, formal analysis
Investigation, methodology, project
administration, supervision, validation, writing -
original draft, writing - review & editing.
- Dr. Denisa Pipero Kurtaj, as 2nd author, carried
out data curation, investigation, methodology,
writing - review & editing.
- Prof. Dr. Ana Kapaj, as 3rd author, carried out data
curation, investigation, methodology, writing -
review & editing.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.91
Elton Guberaj, Denisa Pipero Kurtaj, Ana Kapaj
E-ISSN: 2224-2899
1103
Volume 21, 2024