Does Bank Size and Funding Risk Effect Banks’ Stability? A Lesson from Pakistan

Ali, Muhammad; Puah, Chin‐Hong

doi:10.1177/0972150918788745

Cited by 46 publications

(59 citation statements)

References 62 publications

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“…All these suggest that high credit concentration is one of the important reasons behind the growth of NPAs. The positive relationship between the bank size and NPAs also supports the findings of Ali and Puah (2018) who report that the bank size has a negative effect on the stability of banks.…”

Section: Determinants Of Non-performing Assets: Results and Discussionsupporting

confidence: 86%

Corporate Distress and Non-performing Assets in India

Dhananjaya

2019

Global Business Review

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The article examines the increasing twin balance sheet problem in India in the post-global financial crisis (GFC). Twin balance sheet problem, the combination of corporate distress and banking sector crisis, is considered to be devastating for economic growth since it creates a vicious cycle wherein a weak corporate balance sheet leads to increased stressed assets in the banking sector, which in turn seriously impairs the ability of the banking sector to lend to even healthy companies, holding back the growth further. In this perspective, this study is an attempt to understand the extent of corporate distress in the post-GFC and its impact on the asset quality of public sector banks (PSBs). The study reports that the corporate distress has increased in the post-GFC, resulting in significant debt at risk. The article also finds that increasing corporate fragility has adversely affected the balance sheet of PSBs in India. In a panel regression analysis, it is found that corporate profit is an important determinant of non-performing assets (NPAs) in PSBs along with other factors such as the efficiency of the bank, corporate sales growth, bank size, lending rate and lending to sensitive sectors.

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Section: Determinants Of Non-performing Assets: Results and Discussionsupporting

confidence: 86%

Corporate Distress and Non-performing Assets in India

Dhananjaya

2019

Global Business Review

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“…This study performed a correlation analysis to test the strength of the relationship between independent variables. Ali and Puah (2018) suggest that the correlation among independent variables should be analyzed to examine the regression model's multicollinearity problem. This implies that if the multicollinearity problem occurs if the correlation coefficient is greater than 0.80.…”

Section: Estimations and Resultsmentioning

confidence: 99%

How financial development and economic growth influence human capital in low-income countries

Ali

Raza

Puah

et al. 2021

IJSE

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PurposeThis research aims to explain the effect of financial indicators and economic growth on human capital in low-income countries.Design/methodology/approachWe gathered balanced panel data from 1980 to 2016 over a sample of 12 low-income countries categorized by World Development Indicators. The data stationary properties were analyzed by unit root test while the existence of a long-run relationship among the variables was confirmed by cointegration test. We performed Hausman test to differentiate between the fixed effect and random effect model. The sensitivity analysis confirmed the robustness of the results.FindingsOur findings indicated that broad money supply and private sector credit has a positive and significant impact on human capital. Interestingly, bank credit showed a negative and significant effect on human capital. We also found a significant positive relationship between human capital and economic growth in the study sample.Originality/valueThis is a preliminary study using financial development and human capital in low-income countries with panel econometric techniques as an analysis tool. Overall, we suggest a policy to focus on the financial sector development and economic growth to produce sustainable human capital.

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“…The definitions of the above variables and their expected signs with respect to the dependent variables are provided in Table A1 (Appendix 1). The Z-score as a measure of bank stability accounts for profitability, the leverage ratio and the volatility or the standard deviation of profit ratio (Azmi, et al , 2019); and it is computed as: where BSTAB i, t denotes the stability based on Z-score of bank i in year t , ROA i,t is the return on assets of bank i , E i,t /A i,t indicates the equity to asset ratio and σ ( ROA i , p ) indicates the standard deviation of return on assets (ROA) of bank i over the sample period (Ali and Puah, 2018). The stability measure indicates the number of standard deviations a bank’s ROA has to fall for the bank to become insolvent, hence the Z-score is an indicator of insolvency risk.…”

Section: Data Variable Definition and Methodsmentioning

confidence: 99%

“…The models are then re-estimated with macro-finance and structural variables to check the robustness of the results and to gain additional insights. The suitability of the estimation method is verified from the Hausman test (Ali and Puah, 2018).…”

Section: Data Variable Definition and Methodsmentioning

confidence: 99%

Determinants of bank stability in a small island economy: a study of Fiji

Chand

Kumar

Stauvermann

2021

ARJ

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Purpose This study aims to examine the determinants of bank stability based on three measures of bank stability while accounting for key bank-specific, macro-finance and structural variables. The aim is to underscore key indicators of stability that can be tracked by analysts, bank managers and regulators, especially in small economies such as Fiji. Design/methodology/approach The sample comprises a balanced panel of seven banking and financial institutions over the period 2000-2018. For consistency of data and similar functions in terms of deposit and loans, this paper considers five commercial banks and two credit institutions in Fiji. A fixed-effect method of regression is applied, to control for bank heterogeneity. The dependent variable is bank stability, which is based on three measures – the Z-score, the risk-adjusted return on assets and the risk-adjusted equity to assets ratio. Findings It is noted that bank size, funding risk, credit risk and Herfindahl-Hirschman index are positively associated with bank stability. In the extended model, both inflation and economic growth are positively associated with bank stability, although only inflation is statistically significant. Moreover, factors having a negative association with bank stability are the liquidity risk, the net interest margin and the remittances inflow. Additionally, the domestically generated political crises of the years 2000 and 2006 and the global financial crisis of 2007–2008 are negatively associated with bank stability. Originality/value This study empirically examines the determinants of bank stability in Fiji’s banking sector. Unlike previous studies, this study considers three measures of stability, with z-score as the dominant measure and as explanatory variables, bank-specific, macro-finance and structural variables. The bank-specific data used in the study were hand-picked from the disclosure statements of banks and macro-finance data were extracted from the World Bank Indicators. The study underscores pertinent factors associated with bank stability in the small island economy of Fiji, which can be of interest to analysts, bankers, regulators and researchers in this domain.

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Does Bank Size and Funding Risk Effect Banks’ Stability? A Lesson from Pakistan

Cited by 46 publications

References 62 publications

Corporate Distress and Non-performing Assets in India

Corporate Distress and Non-performing Assets in India

How financial development and economic growth influence human capital in low-income countries

Determinants of bank stability in a small island economy: a study of Fiji

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