scholarly journals Bank Interest Rate Margin, Portfolio Composition and Institutional Constraints

2019 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Liu ◽  
Sathye
Keyword(s):  
Banking Sector ◽  
Panel Data Analysis ◽  
Institutional Constraints ◽  
Institutional Variables ◽  
Credit Quality ◽  
Portfolio Composition ◽  
Gdp Growth Rate ◽  
Interest Margins ◽  
Specific Factors ◽  
Bank Specific Factors

This study empirically examines how the bank specific factors, macro-economic, and institutional variables impact interest margins in China’s banking sector. A panel data analysis of bank data for the period 1988–2015 was carried out. We found a significant association between credit quality, risk aversion, liquidity risk, and the proportion of corporate and industrial loans and the adjusted interest spread (AIS). GDP growth rate, inflation, and the proportion of national savings to the GDP were found to have significant association with the AIS. Furthermore, institutional variables were found to have a significant moderating effect on the AIS. We contribute to the literature by examining a unique context and a more accurate measure of bank interest margin not used in prior studies.

scholarly journals Determinants of Liquidity Considering Role of Market Competition; Evidence from Pakistan’s Banking Sector

2020 ◽  
Vol 2 (2) ◽  
pp. 51-59
Author(s):  
Amir Rafique ◽  
Muhammad Adeel ◽  
Kalsoom Akhtar ◽  
Muhammad Amir Alvi
Keyword(s):  
Credit Risk ◽  
Banking Sector ◽  
Panel Data Analysis ◽  
Market Competition ◽  
Data Set ◽  
Macroeconomic Factors ◽  
Negative Effect ◽  
Specific Factors ◽  
Bank Specific Factors

Current study empirically analyzes bank specific factors and macroeconomic factors that determine the liquidity reserves of banks functioning in Pakistan. To highlight the association, current study performed random effects estimates on a data set of 20 banks from 2006 to 2016.  Bank specific factors include bank size, capital and credit Risk. GDP and Inflation are the macroeconomic factors that were considered. Market competition has been measured through HHI. Based on panel data analysis, current study suggests that bank specific factors (except capital), macroeconomic factors and market competition significantly affect liquidity reserves of banks in Pakistan. These factors include bank size, credit risk, market competition, GDP and inflation. In addition, bank size, credit risk, GDP and Inflation revealed a negative effect on bank liquidity. On the other hand, market competition revealed a positive effect on bank liquidity. Capital showed an insignificant effect on bank liquidity.

scholarly journals Government Ability, Bank-Specific Factors and Profitability - An insight from banking sector of Vietnam

2020 ◽  
Author(s):  
V.C. Nguyen ◽  
Thu Thuy Nguyen ◽  
Huu Tinh Nguyen
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Panel Data Analysis ◽  
Banking System ◽  
Ordinary Least Squares ◽  
Loan Loss ◽  
Bank Efficiency ◽  
Specific Factors ◽  
The Impact ◽  
Bank Specific Factors

To the best of our knowledge, a very few studies have focused on the effects of government ability on bank performance in developing and emerging countries. The aim of this work is to study the impact of government ability, bank-specific factors on profitability in the banking system in Vietnam. Using a panel data analysis in the period over 2014-2018, the study analyzes based on the methods of fixed, random effects, and pooled ordinary least squares. Data were collected from Vietnam’s Stock Exchange, General Statistics Office, and Worldwide Governance Indicators. Our results demonstrate that government ability has negatively affected bank efficiency while economic growth will not affect bank efficiency. In addition, the prime bank-specific factors that can significantly impact on bank efficiency are non-performing loan, loan-to-deposit ratio, loan loss reserves. A bank with a higher loan-to-deposit ratio can positively impact on the probability of a bank. In contrast to the risk, a bank with a greater risk as well as a higher level in non-performing loan in operation will negatively impact on its efficiency.

scholarly journals Modelling Macroeconomic and Bank-Specific Determinants of Credit Risk in the Nigerian Banking Sector: Evidence from Bounds Test Approach to Co-Integration

2021 ◽  
Vol 17 (32) ◽  
pp. 221
Author(s):  
Stanley C. Duruibe ◽  
Nathaniel C. Nwezeaku ◽  
Aghalugbulam B.C. Akujuobi ◽  
Sampson Ikenna Ogoke ◽  
Chidinma Elizabeth Nwabeke
Keyword(s):  
Credit Risk ◽  
Banking Sector ◽  
Negative Relationship ◽  
Long Run ◽  
Gdp Growth Rate ◽  
Short Run ◽  
Specific Factors ◽  
Bounds Test ◽  
Nigerian Banking ◽  
Bank Specific Factors

Credit risk, represented in this study by the ratio of non-performing loans to total loan (NPL), is considered as one of the critical factors that causes bank distress and failure. This study examines the macroeconomic and bankspecific determinants of credit risk in the Nigerian Banking sector from the period 1998Q1 to 2018Q4 using the bounds test approach to co-integration. Literature survey in this subject area using Google Scholar resources reveals that there seems to be a consensus of findings in terms of the negative relationship between credit risk and Gross Domestic Product (GDP) growth rate, while other macroeconomic and bank-specific factors tend to have a random pattern relationship with credit risk attributable to various countries’ economic peculiarities. This study shows that GDP growth rate, return on asset, return on equity, interest rate, unemployment rate, and real exchange rate have a negative relationship with NPL. On the other hand, inflation rate, loan deposit ratio, and ratio of bank capital to asset have positive relationship with NPL. The relationships between the three variables and NPL were found to be individually insignificant to explain credit risk trends in the long run. Moreover, the Wald short-run causality test reveals that the macroeconomic and bank specific indicators jointly influence credit risk in the Nigeria banking sector in the short run. This study, however, recommends that since the macroeconomic and bank specific factors were found to be individually insignificant to explain credit risk trend in the long run, consideration should be accorded to some psychological, political, and socioeconomic factors such as the borrower’s attitude, business climate, social dislocations and distortions, availability of good infrastructural facilities, and the direction of government policies. These factors can affect borrowers’ ability to honor their debt obligations and, thus, determine the level of credit risk in the Nigerian economy.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

Determinants of loan losses of Indian Banks: a panel study

2015 ◽  
Vol 9 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Satyajit Dhar ◽  
Avijit Bakshi
Keyword(s):  
Public Sector ◽  
Panel Data Analysis ◽  
Capital Adequacy ◽  
Content Type ◽  
Specific Factors ◽  
Indian Banks ◽  
Loan Losses ◽  
The Impact ◽  
Bank Specific Factors

Purpose – The purpose of this paper is to examine the factors that influence the variability of loan losses (termed as non-performing advances or NPA in India) of Indian banks in the public sector during the period of five years from 2001 to 2005. Design/methodology/approach – The analysis is based on a panel approach, which considers both spatial and time dimensions of observations. Panel regression was used to explore the impact of different bank-specific factors on NPAs of 27 public sector banks (PSBs). Standard tests were used to find out suitability of different models of panel data analysis. Eight bank-specific factors were identified for analysis on the basis of review of extant literature. Findings – Certain bank-specific factors, in particular, net interest margin and capital adequacy ratio exhibit negative and significant impact on gross non-performing advances (GNPA) ratio of Indian PSBs. The results also suggest that relative quantum of sensitive sector (SEN) (comprised of commercial real estate, commodity and capital market) advances has a positive relationship with NPA ratio, and such a relationship is statistically significant. Research limitations/implications – The sample is restricted to India and may not be reflective of other countries. The study considers bank-level factors, and there are some macro factors (e.g. gross domestic product, interest rate and inflation rate) which could have explained the variability of GNPA ratio. Practical implications – Provisioning against loan losses is a major issue for stability of the banking system. Identification of appropriate causes of variability of such loan losses is important for managing credit portfolio of a bank. A positive and significant relationship between SEN advances and NPA calls for a more cautionary approach toward lending to those sectors. Originality/value – This paper is believed to be the first attempt to empirically examine the role of bank-specific factors. This study attempts to enrich empirical research in the field and provides an insight into the role of various bank-specific factors on loan losses in the context of Indian PSBs. The study provides contrary evidence regarding the role of priority sector advances on a GNPA ratio.

scholarly journals Banking Sector Performance and Political Stability’s Impact on Economic Growth in Pakistan

2018 ◽  
Vol III (I) ◽  
pp. 81-89
Author(s):  
Junaid Khan ◽  
Muhammad Faizan Malik ◽  
Muhammad Ilyas
Keyword(s):  
Economic Growth ◽  
Development Process ◽  
Banking Sector ◽  
Political Stability ◽  
Time Frame ◽  
Long Term Stability ◽  
Specific Factors ◽  
Sector Performance ◽  
Bank Specific Factors

This paper empirically finds the link between the banking sector performance and political stability on Economic growth. Panel data was used encompassing the time frame from 2006 to 2016 for banks operating in Pakistan. This paper main purpose at discovering that the banking sector performance, political stability, and other bank-specific factors have a vital impact on enhancing the procedure of economic growth in Pakistan. “Predictable outcomes suggest that economic growth in Pakistan is in long-term stability relationship; banking sector and political stability have long-term significant impact on economic growth and subsequently, economic growth converge to their longterm stability levels by the means created by Investment. This supports the reality that political certainty or stability is capable of stimulating a country’s development process”. Therefore, revealed significant relationship between banking sector performance and political stability of Pakistan on economic growth.

scholarly journals AN ANALYSIS OF FACTORS AFFECTING NON-PERFORMING ASSETS IN INDIAN BANKING SECTOR

2021 ◽  
pp. 24-29
Author(s):  
Bhabani Mishra
Keyword(s):  
Exchange Rate ◽  
Unemployment Rate ◽  
Banking Sector ◽  
Negative Impact ◽  
Fixed Effect ◽  
Specific Factor ◽  
Macroeconomic Indicators ◽  
Specific Factors ◽  
Bad Assets ◽  
Bank Specific Factors

Deterioration of asset quality destabilizes the financial system by adversely affecting the efficiency, profitability, solvency and liquidity of the banking sector. Both macroeconomic and bank specific factors should be analysed properly to know their strength and direction of impact on the bad assets to have effective NPA resolution mechanism. Unemployment Rate Inflation, Economic Growth, Export rate, Exchange rate, Fiscal Deficit ratio are the macroeconomic indicators and Return on Assets, Credit Deposit ratio, Net Interest Margin are the bank specific factor that are taken from 2003-04 to 2019-20 to explain the variability in Non-performing assets of Public sector and Private sector banks. Fixed effect estimation with robust clustered standard error is used for the panel data regression. Paper found that except unemployment rate all other variables have significant impact on bad assets. Bank specific factor have strong negative impact on the dependent variables. Only exchange rate affects the non-performing loans positively but other macroeconomic variables are negatively associated. KEYWORDS: Non-Performing Assets, Macroeconomic indicators, bank specific factors, Fixed effect

scholarly journals The impact of macroeconomic and bank-specific factors toward non-performing loan: evidence from Indonesian public banks

2017 ◽  
Vol 12 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Hanifan Fajar ◽  
Umanto
Keyword(s):  
Panel Data ◽  
Banking Sector ◽  
Negative Impact ◽  
Good Condition ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Specific Variable ◽  
Specific Factors ◽  
The Impact ◽  
Bank Specific Factors

The present study focuses on the need for banking sector to be more reactive when facing globalization that could bring impact on banking industries complexity. Based on empirical studies, there is a need to analyze non performing loan determinants comprehensively using macroeconomic and bank-specific factors to make a good condition on bank, because combining macroeconomic and bank-specific variable as NPL determinants has made a big improvement to analyze NPL. The object of present study is 20 Banks listed in Indonesia Stock Exchange (IDX) between q12005-q42014. Using dynamic panel data GMM-system method shows that the previous period of NPL (non performing loan), change of PDB (Gross Domestic Product) and inflation rate have a significantly negative impact on NPL. However, BOPO (Operations Expenses to Operations Income) and ROE (Return on Equity) has a significantly positve relationship to NPL. On the other hand, this research does not find any significance on BI rate (interest rate), solvency ratio, and size to NPL. From the result, it can be concluded that combining macroeconomic and bank-specific variable could be an alternative method to analyze NPL determinants on bank. Keywords: nonperforming loans, banks, credit risk, globalization, dynamic panel data, banking industries. JEL Classification: G21, E44, E51, E5, F60

Sign in / Sign up

Export Citation Format

Share Document