^{}
Full
Length Research Paper
Analysing the impact of
NonPerforming Assets on Banking Performance
Ms. Dnyaneshwari Gadge^{1}; Dr.
Pushparaj Kulkarni^{2}[*] and Dr. Vivek Pimplapure^{3}
^{1}Student
(MBA), Dr. Ambedkar Institute of Management studies & Research, Nagpur,
India.
^{2}Assistant
Professor, Dr. Ambedkar Institute of Management studies & Research, Nagpur,
India.
^{3}Associat
Professor, Dr. Ambedkar Institute of Management studies & Research, Nagpur,
India.
ARTICLE
DETAILS ABSTRACT
1. Introduction:
Good banking
performance generally means that a bank is making money, keeping a solid
balance sheet, and managing its resources well. On the other hand, subpar
banking performance could be an indication of problems with low profitability,
excessive risk, or insufficient capitalization. Banks frequently keep a careful
eye on their performance indicators and, where needed, take action to enhance
performance. Banking performance is influenced by both external
(macrofinancial and macroeconomic) factors that represent the regulatory and
economic landscape in which the bank works and internal factors that are unique
to banks. Numerous researches attempted to explain the impact of one element on
another on the banks.
The idea
behind banking performance
Performance
is often understood to mean meeting the goals set by the bank within the
predetermined time structure, utilizing the resources at hand, and incurring
the fewest expenses possible. The ability of a bank or other financial
organization to efficiently manage its operations, obligations, and assets in
order to meet its financial objectives is referred to as banking performance.
This comprises metrics for risk management, asset quality, profitability,
efficiency, and liquidity. Metrics like return on equity (ROE), return on
assets (ROA), net interest margin, loantodeposit ratio, nonperforming loan
ratio, and capital adequacy ratio are examples of key indicators of banking
performance. Measuring bank performance is an essential part of evaluating the
stability and health of financial institutions. In order to assess a bank’s
profitability, liquidity, efficiency, and overall risk management, a number of
measures and ratios must be examined. Stakeholders, including investors,
regulators, and consumers, can learn more about a bank’s financial health and
make wise decisions by examining these important signs. It also entails
assessing how a bank manages its risks. By comparing a bank’s nonperforming
loans to its tangible equity and reserves, the commonly used Texas Ratio
measures the credit risk of the institution. Given that problematic loans make
up a sizable amount of a bank’s capital, a greater Texas Ratio is indicative of
a larger danger of insolvency. Analysing a variety of quantitative and
qualitative indicators is part of measuring banking performance since it allows
one to assess various facets of a bank’s operations, financial standing, and
efficiency in accomplishing goals. These metrics assist interested parties in
evaluating the bank’s overall performance in providing customer service and
fostering economic growth as well as its stability, profitability, efficiency,
and asset quality.
Banks are the
primary drivers of economic growth and have considerable influence over the
amount of money in circulation, they play crucial roles in the economic
development of countries. Economic development is a dynamic, ongoing process
that heavily depends on investment, resource mobilization, and the operational
effectiveness of the many economic sectors. Thus, a healthy banking industry is
essential for economic expansion, wealth creation, job creation, poverty
eradication, entrepreneurship, and GDP growth.
NPA is used
by financial institutions that refer to loans that are in jeopardy of default
the so called NPL. Once the borrower has failed to make interest or principal
payments for 90 days the loan is considered to be a non performing asset depend
on interest payments for income. Troublesome pressure from the economy can lead
to sharp increase in NPLs and often results in massive writedowns. The banking
industry has undergone a sea change after the first phase of economic
liberalization in 1991 and hence credit management. While the primary function
of bank is lead to lend funds as loans to various sectors such as agriculture,
industry, personal loans, housing loans, etc.., The banks have become very
cautions in extending loans in recent times. The reason being the mounting
nonperforming assets.
1)
To Study the impact of
NPA in banks.
2)
To analyze the financial
health of a bank.
3)
To analyze various
financial ratios of a bank.
Null hypothesis: There is no significant impact of NPA on banking
performance
Alternate hypothesis: There is a significant impact of NPA on banking
performance
2. Review of literature:
(Gayakwad,
2021)  NPA not only impact the profitability of the banks but also affect
their operational efficiency, increasing operating costs, and low loan
sanctioning power because of a mismatch in liquidity and cash flows. when we
compare the NPA level of private banks and public sector banks, the private
sector banks have good control over managing NPA as compared to public sector
banks. (Gaba, 2018) A high level of
NPAs suggests a large number of credit defaults that besides liquidity,
adversely affects the profitability and net worth of a bank. The present study
intended to assess the impact of NPAs on the profitability of the Indian
banking industry with special reference to private sector banks.
(Jha,
20112018) The problem of NPAs could be a major hurdle and live danger faced
by banking industry, as a result of it destroys the healthy financial condition
of the banks. Its right time to require appropriate and stringent measures to
get rid of NPA problem. (Goel, 2017) NPAs are point of no return as they do
not generate any income, whereas, the banks are required to make provisions
such as assets. (Ramaswami, 2020) NPA (NonPerforming Asset) is a critical
factor which has adversely impacted the development and growth of the economy.
NPA is a critical factor which has adversely impacted the financial sector in
India. NPAs affect the flow of credit which in turn affect the development and
growth of the economy.
(Jones,
2016) NonPerforming Assets affects not only the banking industry but the
total financial system and there by the economy of the country. The economic
growth of every country depends on the proper functioning of financial system
of the country. (Roy, 2017) the overall NPA position of all the banks is
deteriorating over the years. Since there is a negative high degree correlation
between GNPA and NNP, the profit gradually decreases as the GNPA grows which
has become a serious concern right now. Provisioning can act as cushion for NPA
losses but it can’t be regarded as a solution for growing NPAs. (Hussain,
2021) A high degree of NPAs signals a high risk of a significant number of
loan defaults impacting bank’s performance and net worth and even eroding asset
value. NPAs in relative terms to get a better picture about their asset
selection.
Research
Question: “Are NPA Ratios affecting banking performance?”
Bank
performance is greatly impacted by nonperforming assets (NPAs), and examining
this impact offers important insights into trends and patterns. NPAs are
advances or loans that have ceased to be profitable for the lender, usually as
a result of the borrower’s failure to make interest or principal payments on
time. NPA ratios significantly impact banking performance by indicating asset
quality and risk exposure. Understanding this relationship is crucial for
research to assess the health of financial institutions and ensure stability in
the banking sector. By evaluating the effects of nonperforming assets (NPAs)
on banking performance may evaluate the efficacy of the risk management
mechanisms that banks have put in place. This could involve actions like
reorganizing loans, enhancing credit risk assessment or stepping up recovery
efforts. More protective rules for NPA categorization and provisioning have
been put in place by numerous regulatory authorities in recent times. The
success of these adjustments in controlling and containing nonperforming
assets (NPAs) can be evaluated by examining their effects over the previous
three years. A threeyear trend analysis makes it possible to assess how well
NPA recovery efforts are working. Legal actions, asset sales, and loan
restructure are all included.
Due to a number
of continuing and new causes that are affecting the financial landscape,
research on the effects of nonperforming assets (NPAs) on banking performance
is still essential. Financial institutions, corporations, and individuals are
all being impacted by the COVID19 pandemic’s outcome, which is still having an
impact on the world economy. Because of the pandemiccaused economic slowdown,
there were many job losses, company closures, and supply chain disruptions,
which eventually affected the capacity of borrowers to repay their loans.
Consequently, there has been a rise in nonperforming assets (NPAs) for banks,
potentially harming their overall financial stability, profitability, and
strength of capital. To effectively manage risks and promote economic recovery,
it is crucial to comprehend how nonperforming assets (NPAs) impact banking
performance during the postpandemic recovery phase. The management of
nonperforming assets (NPAs) and banking performance are still impacted by
regulatory reforms and shifts in macroeconomic policy. Increasingly, regulators
are concentrating on increasing risk governance, improving transparency, and
guaranteeing the stability of the financial system. Complying with regulatory
obligations, evaluating systemic risks, and preserving investor trust in this
context all depend on an understanding of how nonperforming assets (NPAs)
affect banking performance. Furthermore, by affecting variables like interest
rates, inflation, and economic development, macroeconomic policies like fiscal
and monetary stimulus plans can affect the frequency and severity of
nonperforming assets.
Development
and prosperity of the economy depend on a strong and healthy banking industry.
NPAs can seriously impair a bank’s capacity to lend, which limits the amount of
credit available to both individuals and enterprises. Determine any hazards to
the financial system and take the appropriate action to mitigate them by
analysing the impact of nonperforming assets (NPAs). Following the
interruptions caused by the COVID19 epidemic, the world economy is still
recuperating. Hidden difficulties in loan repayment may affect individuals and
businesses and may have an effect on nonperforming assets. Examining how NPAs
affect the banking industry in this particular setting helps in determining how
strong the industry is and where assistance may be required to ensure a smooth
recovery.
3. Method
The study is conducted
in India w.r.t., banking sector stocks which are listed on NSE, India. The study focuses on analyzing the impact of nonperforming asset on banking performance ratios. The study has used a quantitative research design
to collect data.
3.1 Sample design
Population All listed banks on National Stock
Exchange (NSE) of India (36), Sample size (36) banks,
sampling technique  Entire
Population is studied
3.3
Data collection
3.4
Collection NPA and banking performance data from
financial sides and financial statements. That the data is an important source
of time in order to effectively capture trends and fluctuations. The data for
the study will be collected from financial websites and articles. Secondary
data will be collected from various sources such as company annual reports and
stock market reports.
4. Results and Data
Analysis
Table
1: Correlation Matrix
Correlations 


Return on Capital Employed 
Return on Assets 
Return on Equity 
Gross NPA 
Net NPA 

Return on Capital Employed 
Pearson Correlation 
1 
.460^{**} 
.318 
.216 
.291 
Sig. (2tailed) 

.005 
.059 
.205 
.085 

N 
36 
36 
36 
36 
36 

Return on Assets 
Pearson Correlation 
.460^{**} 
1 
.801^{**} 
.682^{**} 
.752^{**} 
Sig. (2tailed) 
.005 

.000 
.000 
.000 

N 
36 
36 
36 
36 
36 

Return on Equity 
Pearson Correlation 
.318 
.801^{**} 
1 
.604^{**} 
.663^{**} 
Sig. (2tailed) 
.059 
.000 

.000 
.000 

N 
36 
36 
36 
36 
36 

Gross NPA 
Pearson Correlation 
.216 
.682^{**} 
.604^{**} 
1 
.730^{**} 
Sig. (2tailed) 
.205 
.000 
.000 

.000 

N 
36 
36 
36 
36 
36 

Net NPA 
Pearson Correlation 
.291 
.752^{**} 
.663^{**} 
.730^{**} 
1 
Sig. (2tailed) 
.085 
.000 
.000 
.000 


N 
36 
36 
36 
36 
36 
**. Correlation is significant at
the 0.01 level (2tailed)
The positive
correlation between ROCE and Return on Assets (ROA) is 0.460, significant at the level. The correlation between
ROCE and ROE is 0.318,
which is positive,
but not significant at the 0.01 level. The negative correlation between ROCE and
Gross NonPerforming Assets (GNPA) is
0.216, but it is not significant at the 0.01 level. The negative correlation
between ROCE and Net NonPerforming Assets (NNPA) is 0.291, but it is not significant at the 0.01 level. Every
other correlation shows a
negative correlation and is
significant at the 0.01 level.
Hypothesis
testing:
the null hypothesis of this research is “There
is no significant impact
of NPA on banking performance”. The Net NPA is taken as a
measure of NPA in this regards.
ROE is
independent of Net NPA (ROE is not explained by Net NPA
Model Summary
Model 
R 
R Square 
Adjusted R Square 
Std. Error
of the Estimate 
1 
.663^{a} 
.440 
.423 
3.67186 
a. Predictors: (Constant), Net NPA
ANOVA
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 


Regression 
359.655 
1 
359.655 
26.676 
.000^{b} 
1 
Residual 
458.407 
34 
13.483 


Total 
818.061 
35 

a.
Dependent Variable: Return on Equity
b.
Predictors: (Constant), Net NPA
Result: We do not
accept null hypothesis, here p value is 0.00 this value is less than 0.05
therefore we reject null hypothesis. The p value is 0.00 which indicates that
there is a significant impact of NNPA on ROE. Approximately 44.0% of the
variability in the dependent variable (ROE) is explained by Net NPA and the remaining
56% can be explained by the other variable, according to the Rsquared value of
0.440 (Net NPA). The Rsquared number is slightly higher than the adjusted
Rsquared value, which is 0.423. This could mean that, when taking into account
the total number of predictors included, the Net NPA variable doesn't
substantially increase the explanatory power of the model
ROA is
independent of Net NPA (ROA is not explained by Net NPA)
Model Summary
Model 
R 
R Square 
Adjusted R Square 
Std. Error
of the Estimate 
1 
.752^{a} 
.565 
.552 
.41725 
a. Predictors: (Constant), Net NPA
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 


Regression 
7.688 
1 
7.688 
44.158 
.000^{b} 
1 
Residual 
5.919 
34 
.174 


Total 
13.607 
35 

a.
Dependent Variable: Return on Assets
b.
Predictors: (Constant), Net NPA
Result: We do not
accept null hypothesis, here p value is 0.00 this value is less than 0.05
therefore we reject null hypothesis. The p value is 0.00 which indicates that
there is a significant impact of NNPA on ROA. Approximately 56.0% of the
variability in the dependent variable and the remaining 44% can be explained by
the independent variable, according to the Rsquared value of 0.565 (Net NPA).
The Rsquared number is slightly higher than the adjusted Rsquared value,
which is 0.552. This could mean that, when taking into account the total number
of predictors included, the Net NPA variable doesn't substantially increase the
explanatory power of the model.
ROCE is
independent of Net NPA (ROCE is not explained by Net NPA
Model Summary
Model 
R 
R Square 
Adjusted R Square 
Std. Error
of the Estimate 
1 
.291^{a} 
.085 
.058 
2.31674 
a. Predictors: (Constant), Net NPA
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 


Regression 
16.856 
1 
16.856 
3.141 
.085^{b} 
1 
Residual 
182.487 
34 
5.367 


Total 
199.343 
35 

a.
Dependent Variable: Return on Capital
Equity
b.
Predictors: (Constant), Net NPA
Result: We will
accept null hypothesis, here p value is 0.085 this value is greater than 0.05
therefore we accept null hypothesis. The p value is 0.085 which indicates that
there is a no significant impact of NNPA on ROCE. Approximately 85% of the
variability in the dependent variable and the remaining 15% can be explained by
the independent variable, according to the Rsquared value of 0.085(Net NPA).
The Rsquared number is slightly higher than the adjusted Rsquared value,
which is 0.58. This could mean that, when taking into account the total number
of predictors included, the Net NPA variable doesn't substantially increase the
explanatory power of the model.
5.
Conclusion
The analysis
reveals that while ROCE is positively correlated with ROA and ROE, the
relationships are not uniformly significant. Additionally, while there are
negative correlations with nonperforming assets, they lack statistical
significance. Thus, while these correlations provide insights into potential
relationships, further investigation and consideration of other factors are
necessary for informed decisionmaking and strategic planning. While Net NPA
appears to have some explanatory power for ROE, its contribution to the model's
overall predictive ability seems limited. Therefore, it might be prudent to
reconsider its inclusion or explore ways to enhance its impact within the
model. Additionally, further investigation into alternative variables or
refining the model structure could lead to a better understanding of the
factors influencing ROE.
The
statistical analysis strongly suggests a noteworthy relationship between NNPA
and ROE. It underscores the importance for businesses to address and mitigate
nonperforming assets to improve their return on equity metrics and overall
financial performance. The finding underscores the importance of prudent credit
risk management practices in maintaining a favorable Return on Equity. By
addressing and minimizing Net NPA, the company can potentially enhance its
financial performance and strengthen investor confidence.
6. References
Gaba, M. A. (2018). Non  Performing Assets and Profitability: A Study of Private
Sector Banks in India.
Gayakwad, A. (2021). A study of npa and its impact on banking performance. NLDIMSR Innovation Journal Of Management Research.
Goel, S. (2017).
Measurement of correlations (NPA and ROA) of different
banks and trend analysis of
NPAs in Indian bsnks.
Hussain, m. (2021).
A Study Of The Impact Of NonPerforming Assets Of Indian
Banking System On Selected Public Sector Banks.
Jha, p. (20112018). Analysis of NPA among SBI bank and ICICI bank for operating
(2011 2018).
Jones,P.K. (2016). Effect Of NonPerforming Assets On The Profitability Of Banks – A Selective study.
Krishnudu, c. (2021).
Analysis of NPAS in banking
sector  A case study
of public and private sector.
Patel, m. R. (2013). A study on nonperforming assets
management with reference
to public sector
banks, private sector banks and foreign banks in India .
Ramaswami, D. R. (2020). Impact of NPA of
profitability of bank.
Roy, P. (2017). Analysis of nonperforming assets in public sector banks
of India.
Yadav, P. s. (2019). Impact of NonPerforming Assets (NPAs) on Assets Turnover
Ratios of Punjab National Bank Limited.
[*]
Author can be contacted at: ^{1}Department of Agricultural
Economics, Federal University of Technology Owerri, Imo State, Nigeria
Received:
1552024; Sent for Review on: 19052024; Draft sent to Author for
corrections: 10062024; Accepted on: 26062024; Online Available from 2906 2024
DOI : 10.13140/RG.2.2.24648.53761
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