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Corporate Ownership & Control / Volume 21, Issue 4, 2024
41
THE IMPACT OF IFRS 9 ON CREDIT RISK
AND PROFITABILITY IN THE EUROPEAN
BANKING SECTOR
Francesco Paolo Ricapito *
* Department of Business Administration, University of Bergamo, Bergamo, Italy
Contact details: Department of Business Administration, University of Bergamo, Via dei Caniana 2, Bergamo 24127, Italy
Abstract
How to cite this paper: Ricapito, F. P.
(2024). The im pact of IFRS 9 on credit risk
and profitability in the European banking
sector. Corporate Ownership & Control,
21(4), 41–48.
https://doi.org/10.22495/cocv21i4art4
Copyright © 2024 The Author
This work is licensed under a Creative
Commons Attribution 4.0 International
License (CC BY 4.0).
https://creativecommons.org/licenses/by/
4.0/
ISSN Online: 1810-3057
ISSN Print: 1727-9232
Received: 11.08.2024
Accepted: 08.12.2024
JEL Classification: B26, G21, G32, G33, M41
DOI: 10.22495/cocv21i4art4
The accounting standard IFRS 9 Financial Instruments of
the International Financial Reporting Standards (IFRS) has
introduced a new model to estimate credit loss, requiring entities
to assess the credit risk associated with financial assets and
recognize impairment losses based on expected credit losses (ECL),
rather than the incurred credit losses (ICL) of the former IAS 39 by
the International Accounting Standards Board (IASB). The adoption
of IFRS 9 has led to various application issues and challenges,
particularly in assessing economic conditions and specific borrower
circumstances that may impact creditworthiness, resulting in
a significant impact on business performance. Specifically, banks
are now required to estimate the future cash flows of their
borrowers and adjust their provisions, considering forward-looking
information. This includes not only an analysis of company
characteristics but also macroeconomic factors to assess credit
losses. Given the aforementioned considerations, our study aims to
investigate the adoption of IFRS 9 in the banking sector industry,
focusing on the effects of the credit risk assessment model and its
impact on banks’ performance. The analysis is based on a sample
of European listed banks spanning the 2014–2021 period.
We compare the period during which the banks adopted IFRS 9 and
the ECL model with the period in which the banks used IAS 39 and
the ICL model to understand the effects on the provisioning costs,
non-performing loans (NPLs) and capital adequacy. In this
perspective, the adoption of IFRS 9 forced European banks to make
more accurate assessments of their credits and associated risks,
leading to significant changes in their risk management and
internal control practices, in order to reduce the impact on
the performance and capital of banks.
Keywords: IFRS 9, Provisioning Costs, Non-Performing Loans, CET 1,
Index of Justice
Authors’ individual contribution: The Author is responsible for all
the contributions to the paper according to CRediT (Contributor
Roles Taxonomy) standards.
Declaration of conflicting interests: The Author declares that there is no
conflict of interest.
1. INTRODUCTION
The standard IFRS 9 of the International Financial
Reporting Standards (IFRS) was issued by
the International Accounting Standards Board
(IASB) in July 2014 and became effective in 2018,
superseding the previous IAS 39 and implementing
substantial modifications to the accounting
regulations governing financial instruments (IASB,
2014). The key feature of IFRS 9 is the measurement
of credit loss allowances in accordance with
the expected credit losses (ECL) model, in contrast
to the incurred credit losses (ICL) model used
in IAS 39.
Corporate Ownership & Control / Volume 21, Issue 4, 2024
42
This new standard adopts a forward-looking
methodology for the assessment and valuation of
financial instruments, wherein the ECL calculation
comprises the sum of discounted future cash flows
adjusted for variations in the likelihood of borrower
default and fluctuations in interest rates
(Novotny-Farkas, 2016). Consequently, the ECL
framework under IFRS 9 mitigates the abrupt spikes
in impairments, often referred to as the “cliff-effect”,
that were prevalent under IAS 39, thereby facilitating
a more timely and gradual recognition of potential
losses by companies, which in turn lessens volatility
in earnings. Furthermore, the standard introduces
a “staging framework” that categorizes financial
assets based on shifts in credit risk from the point
of initial recognition. Specifically, IFRS 9 mandates
the classification of financial assets into a three-
stage impairment model (IASB, 2014). In stage 1,
assets are considered to have low credit risk, with
impairment calculated based on the annual ECL.
In stage 2, assets that have undergone a significant
increase in credit risk are assessed using the lifetime
ECL, which reflects the probability of default over
the entire duration of the exposure. Finally, stage 3
encompasses assets that are classified as credit-
impaired, with ECL also determined on a life-
expiration basis. In this context, IFRS 9 incorporates
both historical and forward-looking data, taking into
account not only the characteristics of the company
but also broader macroeconomic factors to evaluate
credit losses.
A higher probability of default requires banks
to allocate higher credit-loss provisions for their
exposure, resulting in increased costs and decreased
profits. In this way, it serves two essential
objectives: aligning financial exposures with
the underlying risks associated and monitoring
the credit risks to prevent a migration of exposure
from stage 1 to stage 2 and stage 3. From this
perspective, the relationship between IFRS 9 and
risks appears to be quite significant, because its
application incorporates risk considerations into
the reporting process, allowing the probability of
default and the magnitude of potential credit losses
to be estimated.
Many authors argue that credit losses are often
the primary reason behind bank failures (Ahmed
et al., 1999; Gebhardt & Novotny-Farkas, 2011).
In this regard, the application of IFRS 9 has given
rise to major structural changes in the internal
control process because risk management frameworks
now encompass the establishment of robust internal
controls and processes to identify, measure,
monitor, and mitigate credit risk. Companies need to
ensure that their risk management systems align
with the requirements of IFRS 9 to facilitate
proper reporting and provisioning. Effective risk
management frameworks must also include strong
governance structures, risk committees, regular
reporting processes and risk mitigation strategies.
These measures ensure that credit risk exposures
are properly measured, controlled and reported in
accordance with the requirements of the standard.
At the same time, its application has led to various
challenges in assessing economic conditions and
specific borrower circumstances that may impact
creditworthiness, which will significantly affect
business performance. The approach to credit losses
under IFRS 9 is more prudent and the measurement
is highly subjective as it relies on an assessment
with a high level of managerial discretion
(Novotny-Farkas, 2016; Dong & Oberson, 2022;
Kvaal et al., 2024). This element of forecasting can
potentially lead to volatile results. Moreover, during
periods of recession, losses could increase even if
current economic circumstances are positive. In light
of the above considerations, the objective of our
study is to investigate the adoption of IFRS 9 on
the banking sector and, in particular, the impact of
the credit risk assessment model and its impact on
banks’ performance and, in particular, the impact on
provisioning costs, profitability, non-performing
loans (NPLs) and capital adequacy.
The rest of this paper is organised as follows.
Section 2 provides an overview of the literature and
the development research hypothesis. Section 3
describes the methodological approach adopted in
the empirical study, describing the data, variables
and research method. Section 4 presents the results
and discussion of the findings. Finally, Section 5
outlines the concluding remarks.
2. LITERATURE REVIEW AND HYPOTHESES
DEVELOPMENT
Several studies have focused on the impact of
the adoption of IFRS 9, particularly on the banking
sector. One of the most important aspects of IAS 39
lies in how it evaluates and accounts for credit
losses. The ICL model is based on actual losses that
have already been observed and identified.
In other words, under this model, credit losses are
recognized only when there is evidence that
a financial asset is impaired. The ICL model is
defined as a backwards-looking approach because it
relies primarily on historical data, while the ECL
model is based on a forward-looking approach since
it relies primarily on future data (Bernhardt et al.,
2014; Novotny-Farkas, 2016; Abad & Suarez, 2017;
Seitz et al., 2018; Loew et al., 2019, Dong & Oberson,
2022; Kyiu & Tawiah, 2023).
Although these issues were examined from
different perspectives, many scholars stated that
the new standard has led to an increase in
the expense of ECL provisions and has negatively
affected the regulatory levels in banks (Hashim
et al., 2016; Abad & Suarez, 2018; Krüger et al., 2018;
Seitz et al., 2018). Moreover, the introduction of
the stage model (Novotny-Farkas, 2016; Hashim
et al., 2016), more specifically the transition from
stage 1 to stage 2, contributes to a significant increase
in loan-loss allowances. Many authors suggest that
the implementation of IFRS 9 has various impacts on
banks. The new standard leads to increased volatility
of loan-loss allowances in the banking sector and
credit-loss charges, reducing the net profits of
banks, and potentially requiring higher levels of
equity capital (Fatouh et al., 2020, 2023; Lopez-
Espinosa & Penalva, 2023; Eyalsalman et al., 2024).
However, the impact on the cost of funding for
banks in Europe is found to be minor. The market
rate of financial institutions is more affected by
the volume of financial instruments and impairments
under IFRS 9 (Szücs & Márkus, 2020). Finally,
the expected impact of IFRS 9 on the banking system
raises the coverage of non-performing exposures
(NPEs) but has a negative regulatory capital effect
(Salazar et al., 2023). However, some studies suggest
that banks can react to these negative effects by
engaging in asset sales or reducing their loan
Corporate Ownership & Control / Volume 21, Issue 4, 2024
43
offerings (Abad & Suarez, 2017; Zampella & Ferri,
2024). In this perspective, there is a great emphasis
on the relationship between credit risk assessment
and banks’ performance, in order to understand
whether and how the adoption of the ECL model can
affect the banking industry.
Despite valuable studies on IFRS 9, the analysis
of the impact on the banks’ performance remains
largely unexplored. In addition, many empirical
studies have examined the relationship between
accounting standards and the court system. Radcliffe
(1990) discovered that courts view Statements of
Standard Accounting Practice (SSAPs) as influential
evidence of accounting practice. Hassoon et al.
(2021) delved into the impact of judicial accountability
in curbing creative accounting practices, concluding
that it can be instrumental in reducing such
behaviours. Mills (1993) scrutinized how common
law, and the judicial process shape the evolution of
accounting standards, underscoring the significance
of contracts in accounting procedures. Freedman
(2005) discussed the mismatch between taxable and
accounting profit, arguing for a continued role for
the courts in determining taxable profit. In essence,
these papers collectively indicate that the legal
system has a role in interpreting and implementing
accounting standards, addressing creative accounting
practices, and delineating taxable profits. To advance
the understanding of the research area, we intend to
move forward with the current body of knowledge
on the effect of the IFRS 9 application in European
banks, by investigating the following research
questions:
RQ1: What is the impact of IFRS 9 on loan loss
provisions?
RQ2: How does the court system moderate
the relationship between IFRS 9 and loan loss
provisions?
RQ3: Is the impact of IFRS 9 on banks’ Common
Equity Tier 1 (CET 1) positive or negative?
RQ4: How does the court system moderate
the relationship between IFRS 9 and CET 1?
RQ5: Since the introduction of IFRS 9, have
European banks increased or decreased non-
performing loans?
RQ6: How does the court system moderate
the relationship between IFRS 9 and non-performing
loans?
We believe that banks, aware of the negative
impact of the new accounting standard, have
changed their approach to credit management
practices to mitigate the impact on their earnings
and capital. Furthermore, we find that
the effectiveness of the court system may influence
the impact of IFRS 9. In countries where the court
system is less effective, banks are less motivated to
grant loans to avoid incurring additional costs
associated with the protracted legal proceedings,
providing an explanation of why banks in less
efficient judicial systems might be less inclined to
grant loans.
In this perspective, we formulated the following
research hypotheses:
H1: With the introduction of IFRS 9, European
banks reduced their provisions costs for credit
impairment. The efficiency of the judicial system
negatively moderates the above relationship.
H2: With the introduction of IFRS 9, European
banks increased their regulatory capital (CET 1).
Judicial efficiency positively moderates the above
relationship.
H3: With the introduction of IFRS 9, European
banks reduced their non-performing loans.
The efficiency of the judicial system negatively
moderates the above relationship.
3. RESEARCH METHODOLOGY
3.1. Sample selection
The empirical analysis is based on a sample of
European banks covering the period from 2014
to 2021. We compare the period during which banks
adopted IFRS 9 and the ECL model with the period
during which banks used IAS 39 and the ICL model
to identify their impact on provisioning costs, CET 1
and NPL. Methodologically, we use a panel data
model to examine the impact of IFRS 9 adoption
on the above-mentioned dependent variables.
The sample was collected from the Refinitiv Eikon
database (Datastream). In particular, we conducted
a comprehensive search of all European banks, as
well as banks in Switzerland, the United Kingdom
and Russia, collecting data for eight years (2014–2021).
The initial sample consists of 233 banks. However,
after excluding banks with missing values for at
least one year in the time interval and for at least
one of the variables included in the regressions, we
obtained a final sample of 78 individual firms and
624 firm-year observations.
3.2. Empirical model
To test our hypotheses, panel-data regression
analysis was performed. Hence, the empirical
models are the following:
to assess the relationship between IFRS 9 and
provisioning costs H1, we estimate Model 1;
to test H2 on the relationship between IFRS 9
and CET 1, we run Model 2;
to test H3 on the relationship between
the IFRS 9 and NPL, we estimate Model 3.
Model 1
𝑃𝑟𝑜𝑣
𝑇𝑜𝑡
_
𝑙𝑜𝑎𝑛𝑠
,
⁄
=
𝛼
+
𝛼
𝐼𝐹𝑅𝑆
9
,
+
𝛼
𝐼𝑂𝐽
,
+
𝛼
𝐼𝐹𝑅𝑆
9
∗
𝐼𝑂𝐽
,
+
𝛼
𝑆𝑖𝑧𝑒
,
+
𝛼
𝐿𝐸𝑉
,
+
𝛼
𝑅𝑂𝐴
,
+
𝛼
𝐶𝐹𝑂
,
+
𝜀
,
(1)
Model 2
𝐶𝐸𝑇
1
,
=
𝛼
+
𝛼
𝐼𝐹𝑅𝑆
9
,
+
𝛼
𝐼𝑂𝐽
,
+
𝛼
𝐼𝐹𝑅𝑆
9
∗
𝐼𝑂𝐽
,
+
𝛼
𝑆𝑖𝑧𝑒
,
+
𝛼
𝐿𝐸𝑉
,
+
𝛼
𝑅𝑂𝐴
,
+
𝛼
𝐶𝐹𝑂
,
+
𝜀
,
(2)
Model 3
𝑁𝑃𝐿
/
𝑇𝑜𝑡
_
𝑙𝑜𝑎𝑛𝑠
,
=
𝛼
+
𝛼
𝐼𝐹𝑅𝑆
9
,
+
𝛼
𝐼𝑂𝐽
,
+
𝛼
𝐼𝐹𝑅𝑆
9
∗
𝐼𝑂𝐽
,
+
𝛼
𝑆𝑖𝑧𝑒
,
+
𝛼
𝐿𝐸𝑉
,
+
𝛼
𝑅𝑂𝐴
,
+
𝛼
𝐶𝐹𝑂
,
+
𝜀
,
(3)
Corporate Ownership & Control / Volume 21, Issue 4, 2024
44
3.3. Definition of variables
3.3.1. Dependent variables
In terms of provisioning costs, we intend to estimate
the amount of credit losses that banks choose to
recognize in their financial statements before and
after the adoption of IFRS 9. For its calculation,
we selected a specific proxy which is the ratio
computed as annual loan loss provisions, divided
by the total amount of gross loans (Prov/Tot_loans).
For regulatory capital, instead, we use Common
Equity Tier 1 (CET1) as a percentage of risk-weighted
assets. Finally, for NPL, we used the NPL ratio,
obtained by dividing the number of NPLs by the total
number of loans (NPL/Tot_loans).
3.3.2. Independent variable
We considered the adoption of IFRS 9 by European
banks (IFRS9). More in particular, the independent
variable is a dummy variable which equals 1 if IFRS 9
has been adopted and 0 otherwise (i.e., if the previous
IAS 39 was adopted).
3.3.2. Control variables
In line with a previous study, we selected
the following control variables: 1) firm size (Size),
measured by the natural logarithm of total assets;
2) leverage (LEV), calculated as total debt divided by
total assets; 3) return on assets (ROA), measured by
dividing firm’s net income by the average of its
total assets; 4) cash flow from operations (CFO),
calculated as: Net Income + Non-Cash Items – Change
in Working Capital.
3.3.3. Moderating variable
The Index of Justice (IoJ) has been used as
a moderating variable. In particular, the latter is
determined on the basis of European judicial systems
CEPEJ Evaluation Report drafted by the European
Commission for the Efficiency of Justice, which
evaluates the functioning of judicial systems of
44 Council of Europe member states.
4. EMPIRICAL FINDINGS AND DISCUSSION
4.1. Descriptive statistics and correlation analysis
The descriptive statistics of the studied variables are
summarized in Table 1. The mean of the IFRS9
variable is 0.5 because the panel is symmetric,
i.e., four years before and four years during
the implementation of IFRS 9 were considered.
The IOJ, on average, has a mean value of 0.7264,
while the average leverage is 380.819%. In addition,
the average ROA is 4.91% and CET1 has an average
value of 13.03%.
Table 1. Descriptive statistics
Variables N Mean Median Std. dev. Q1 Q3 Min Max
IFRS9
i,t
624 0.5 0.5 0.500 0 1 0 1
IOJ
i,t
624 0.7264 0.73 0.068 0.55 0.87 0.67 0.8
Size
i,t
624 19.294 19.111 1.518 16.304 22.565 18.109 20.942
LEV
i,t
624 380.819 362.35 158.611 43.21 871.11 275.65 461.19
ROA
i,t
624 4.91 0.53 0.661 -4.16 2.62 0.34 0.735
CFO
i,t
624 1337896 2417523 254.019 -8542776 1546258 -2303000 2458724
Prov/Tot_loans
i,t
624 0.786 0.630 1.026 -1.32 11.04 0.25 1.03
CET1
i,t
624 13.030 12.4 1.995 9.75 19.5 11.6 13.91
NPL/Tot_loans
i,t
624 8.570 4.85 9.811 1.06 63.13 3.27 9.56
Table 2 shows the correlations of the variables.
Many coefficients are statistically significant, but
the highest value is equal to 0.506 (correlation
between bank Size and CFO). Therefore, the fact that
all the correlation coefficients are below ±0.8 or ±0.9
suggests that multicollinearity is not an issue in
estimating the models, so the explanatory variables
selected for the analysis are likely to be proxies for
various underlying factors.
Table 2. Correlation matrix (Pearson correlation coefficient)
Variables (1) (2) (3) (4) (5) (6) ( 7) ( 8) (9)
(1) IFRS9
i,t
* IOJ
i,t
1
(2) Prov/Tot_loans
i,t
-0.440*** 1
(3) CFO
i,t
0.091** -0.395*** 1
(4) ROA
i,t
0.203*** -0.379*** 0.077* 1
(5) Size
i,t
0.251*** -0.045 -0.234*** 0.125*** 1
(6) LEV
i,t
-0.018 0.124*** -0.203*** 0.015 0.485*** 1
(7) CFO
i,t
0.206*** -0.015 -0.190*** 0.178*** 0.506*** 0.348*** 1
(8) NPL/Tot_loans
i,t
-0.326*** 0.447*** -0.084** -0.322*** -0.504*** -0.398*** -0.300*** 1
(9) IFRS9
i,t
0.038 -0.229*** 0.289*** -0.108*** 0.064 -0.053 -0.075 -0.425*** 1
Note: *, **, and *** are significantly different from zero at the 0.10, 0.05, and 0.01 levels, respectively.
4.2. Results of regressions and discussion
Table 3 shows the results of the regressions of
the models. However, several diagnostic tests
implemented in both models separately need to be
discussed. First, we determined whether to use fixed
effects (FE), random effects (RE), or pooled data
specification to evaluate the results. Table 3 shows
that the pooling of data is not suitable (p-value of
the Lagrange multiplier (LM test) < 0.01) and that
using FE is preferred to RE (p-value of the Hausman
test < 0.01) in both models. Furthermore, the Pesaran
test and the modified Wooldridge test are both
significant at a value better than 0.01, indicating that
Corporate Ownership & Control / Volume 21, Issue 4, 2024
45
cross-sectional dependence and heteroscedasticity
are a problem in the two models. The LM test for
serial correlation is not significant at a value of 0.1,
suggesting the absence of first-order correlation in
both models. Given these results, they are estimated
using FE and the standard errors are corrected as
per Driscoll and Kraay (1998). To check for potential
multicollinearity issues, a variance inflation factor
(VIF) test was performed in both models; in both
cases, the value was found to be below 2 (two),
indicating that multicollinearity is not an issue in
the analysis.
Turning to the regression results, in Model 1,
we explore the possibility that the adoption of
IFRS 9 has led to a reduction in provisions expenses
for European banks and that the efficiency of
national justice systems negatively moderates this
relationship. The empirical results support our
hypothesis H1, which states that IFRS 9 has a negative
effect on provisioning expenses, while the interaction
IFRS9 * IOJ negatively moderates the impact, thereby
mitigating the effect. This result may appear
incoherent with other studies, which instead argue
that IFRS 9 has led to an increase in provisioning
costs for ECL (Hashim et al., 2016; Abad &
Suarez, 2018; Krüger et al., 2018; Seitz et al., 2018).
However, our findings primarily focus on
the implications of this new standard: European
banks have responded to these negative effects by
managing their granted loans, improving their
quality, or reducing exposures (Abad & Suarez,
2017). Moreover, the results confirm that the Index
of Justice negatively moderates the above-mentioned
relationship. This can be explained by the fact that
in countries where judicial systems are more
efficient, banks can more easily resolve their
disputes with insolvent creditors. Consequently,
they are more inclined to grant loans.
In Model 2, we test the impact of IFRS 9 on
regulatory capital. Our results indicate that
the introduction of the new accounting standard has
led to an increase in CET 1. Once again, we believe
that banks have proactively addressed the potential
negative effects of implementing this accounting
principle by bolstering their regulatory capital.
Nevertheless, it is worth noting that this outcome
has undoubtedly been influenced by the capital
transitional arrangement (CTA). Under the CTA,
banks are given a transition period to adopt IFRS 9,
aimed at mitigating the impact of its adoption on
capital resources or “own funds” (Dong & Oberson,
2022). However, in this case, we observe the absence
of a significant effect of the Index of Justice on
the above relationship.
Lastly, in Model 3, our results demonstrate that
the introduction of IFRS 9 has led to a reduction in
NPLs. This once again highlights how banks have
chosen to manage their loans more effectively
to mitigate any significant impact on their
performance. Additionally, in this case, the Index
of Justice negatively moderates this relationship,
attenuating its effect. An explanation for this
moderating effect can be attributed to the increased
ease in recovering one’s credits. This can occur,
for instance, through a simplified execution of
guarantees in cases where creditors have not
fulfilled their obligations. Moreover, it can be
attributed to the disposal of deteriorated credits
carried out by the majority of European banks over
the last few years. In conclusion, although
the introduction of IFRS 9 was expected to lead
banks to increase provisions for credit losses and
recognize more NPLs, as well as result in a decrease
in regulatory capital, our findings demonstrate
the exact opposite. They indicate that after
the implementation of the new accounting standard,
banks have changed their approach to credit
management, reducing risks and preserving their
financial performance.
Table 3. Main results
Independent variable Model 1 Model 2 Model 3
IFRS9 -2.234*** (0.493) 2.621*** (0.984) -3.136*** (3.777)
IOJ -7.181 (8.608) 4.536*** (5.176) 6.245 (6.911)
IFRS9 * IOJ 2.374*** (0.658) -0.957 (1.313) 3.077*** (5.041)
Size -0.077 (0.240) 0.601 (0.480) -1.022 (1.842)
LEV -0.001 (0.004) 0.002*** (0.008) 0.005** (0.003)
ROA -0.906*** (0.084) -0.015 (0.093) 0.507 (0.360)
CFO 0.001 (0.001) 0.002 (0.003) 0.001 (0.002)
Constant 9.243 (10.916) -6.722* (8.782) 12.071 (13.587)
Mean VIF 1.27 1.28 1.25
LM-poolability test < 0.01 < 0.01 < 0.01
Hausman test < 0.01 < 0.01 < 0.01
Pesaran cross-sectional dependence test < 0.01 < 0.01 < 0.01
Modified Wooldridge test < 0.01 < 0.01 < 0.01
Serial correlation test
0.60 0.48 0.46
F-test for overall significance
< 0.01 < 0.01 < 0.01
N 624 624 624
R2 0.295 0.182 0.235
Note: *, **, and *** are significantly different from zero at the 0.10, 0.05, and 0.01 levels, respectively. T-statistics are presented in
parentheses. LM-poolability is the Breusch-Pagan LM test’s p-value. Hausman is the Hausman test’s p-value. Pesaran is the Pesaran
cross-sectional dependence test’s p-value. Modified Wooldridge is the Modified Wald test’s p-value. Serial correlation is the LM test’s
p-value. The F-test is the p-value for a test of overall significance. R2 is the regression’s coefficient of determination. N is the number of
observations used to estimate the model, using FE.
5. CONCLUSION
This study examines the impact of IFRS 9 on
European banks. In line with previous studies,
we focused on the impact of the new standard on
provisions for credit losses, regulatory capital, and
NPLs (Novotny-Farkas, 2016; Abad & Suarez, 2017).
In addition, our study examines whether the adoption
of IFRS 9 has a different impact on the effectiveness
of the judicial system in the country where
the banks are located. We hypothesize that, in order
to anticipate the negative effects of the new
Corporate Ownership & Control / Volume 21, Issue 4, 2024
46
accounting rules, banks have changed their
approach to credit management to reduce risks and
mitigate the impact on performance. Furthermore, we
find that the impact of IFRS 9 varies depending
on the effectiveness of the legal system in
the respective countries. To test our research
hypotheses, we rely on a sample of 78 European
banks from 2014 to 2021 and 624 firm-year
observations. Our results showed a negative
relationship between IFRS 9 and provisioning
expenses and NPLs and a positive relationship with
CET 1. This suggests that in order to mitigate
the negative effects arising from the stricter rules
set out in IFRS 9 on credit assessment, banks
decided to revise their lending policies. They now
focus on improving the quality of loans issued and
promoting more efficient credit management
practices. This is aimed at avoiding the accumulation
of credit losses and safeguarding their performance.
In addition, we find that the Index of Justice
negatively moderates the relationship between
IFRS 9 and provisioning costs, as well as between
IFRS 9 and NPLs. This result suggests that in
countries where the judicial system is less efficient,
banks are more cautious in credit management,
particularly to avoid delays, inefficiencies, and
additional costs associated with enforcing guarantees
or resolving disputes with risky borrowers. Further
research has shown that IFRS 9 has a negative
impact on financial performance due to the much
more stringent rules provided by the standard.
Despite being aware of the effects stemming
from the application of the new accounting rules,
our aim was to understand how banks managed
these effects, whether they have incurred these
negative effects or managed them passively. From
this perspective, the theoretical implication of our
research improves the current body of knowledge on
the impact of IFRS 9 on banks and how the latter
have managed the effects from its introduction.
Moreover, this study has important implications in
several respects. First, it enables us to understand
how the introduction of an accounting standard can
have a profound effect on the governance and
control systems of a company. In this case, based on
the obtained results, we believe that the introduction
of the new accounting standard had a positive
impact on credit management mechanisms, improving
the company’s performance. However, while from
a governance perspective, by adopting the ECL
model, banks improved their credit analysis
mechanisms and risk management systems for
predicting potential losses, from a social perspective, it
is worth noting how the stricter rules regarding
impairment have led to increased difficulty in
accessing credit for both firms and households.
Second, the empirical evidence could provide
valuable insights for regulators and policymakers to
enhance the efficiency of the judicial system, given
that its inefficiency imposes significant operational
constraints on companies, affecting not only credit
granting (as in this case) but also, more broadly,
investment policies.
This paper has some limitations that could be
addressed in future research. We have only
considered three dependent variables, but for
a comprehensive measurement of business
performance, we could have also included others.
Additionally, we could have taken into account other
variables as potential moderators, such as the size,
operational sector, or legal structure of the bank.
Future research should explore if and how these
variables may have implications in the analysis of
IFRS 9 introduction. Finally, it would be interesting
to examine the impact of the standard after the CTA
period, in order to understand its effects on CET 1
and how banks will be able to manage them.
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