Managerial efficiency and failure of U.S. commercial banks during the 2007-2009 financial crisis: was this time different?

Álvarez-Franco, Pilar B.; Restrepo-Tobón, Diego A.

doi:10.17230/ecos.2016.43.1

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The loan quality variable had =-0.06, p-value 0.15, which was a negative relationship but statistically insignificant. Alvarez-Franco and Restrepo-Tobon (2016) observe that loan quality is an important pointer to bank survival and argue that less diversified banks are more likely to fail due to dependence on interest income. Huang et al (2012) study found p-value for ASEAN at -0.156; G8 -0.859, EU at -1.253, NIC at -0.086 and G-20 at -0.258 all being significant at 5% level that is p-value<0.05 and concluded that net interest income predicted the financial distress of global banks best.…”

Section: Discussionmentioning

confidence: 99%

“…These losses normally emanate from long periods of asset quality deterioration due in part to excessive credit expansion. -Franco and Restrepo-Tobon (2016) state that during and immediately after 2007-2009 US financial crisis three hundred twenty-two (322) US Commercial banks failed with an estimated loss of USD 86 billion to the FDIC compared to the period 1980-1989 when one thousand four hundred sixty-seven (1467) banks failed with an estimated cost of $62 billion and to the period 1990-1999, four hundred thirty-six (436) banks failed with estimated loss of $7 billion. Cleary and Hebb (2016) state that the FDIC fund went into the red during 2009 and that is a confirmation of the severity of bank distress.…”

Section: Introductionmentioning

confidence: 99%

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Loan Asset Indicators and Commercial Bank Fragility in Kenya

Bwire

2021

EJMI

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Purpose. To test the predictive ability of loan asset indicators on Commercial bank fragility in Kenya. Design/Method/Research approach. The study adopted positivism research philosophy with exploratory research design. The study population was 42 Commercial banks in operation on 31st December 2015. Secondary data was collected from Central Bank of Kenya and analysed using Stata Statistics/Data analysis. Generalised Linear Model was used to establish the relationship between asset indicators and bank fragility. The concept of credit creation was explored as the genesis of bank fragility. This study is part of early warning systems in detecting bank fragility. Findings. The research found a direct relationship between a lagged dependent variable, loan portfolio growth, loan deposit ratio and bank fragility. Practical implications. Recommendations are followed on the basis of this study. At first, regulator develop a potential solution to control loan portfolio growth, cap loan deposit ratio and limit the level of non-performing loans. Banking practitioners should model monthly reporting requirements to ensure that banks are able to disclose the ratio and explain any significant changes. Secondly, since Non-performing loans can act as an incentive for bank managers to seek deposits and lend more thereby exacerbating the problem, banks with NPL to gross loans greater than an upper threshold determined by the regulator should not be allowed to attract more deposits. Thirdly, set the maximum level of loan deposit ratio to avoid expensive, sensitive and high-risk loan capital. Implementation of these recommendations will lead to secured social welfare. Originality/Value. The study examines the role of certain loan asset indicators on bank fragility and extends the discussion in the area of early warning systems and commercial bank instability in Kenya. Research limitations/Future Research. This research contributes to the discussion on bank fragility and early warning systems. The further research should review evidence from other jurisdiction with high numbers of distressed institutions to determine how many months or years before distress the three significant variables could predict fragility. Besides, there is need for research on insider loans as defined and why there was no statistical significance. Paper type. Empirical.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Loan Asset Indicators and Commercial Bank Fragility in Kenya

Bwire

2021

EJMI

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“…This will help the regulators in fashioning laws and guidelines for the industry players. According to Alvarez-Franco and Restrepo-Tobon (2016), financial performance evaluation is critical to both the regulator and managers of the banks.…”

Section: Introductionmentioning

confidence: 99%

Drivers of Banks’ Financial Performance

Boadi

Dadzie

2022

IJEFI

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A significant number of banks folded up during the Ghanaian banking financial crisis of 2017 to 2018 causing significant social costs as well as impeding economic growth. We take a step backward to present the significant drivers of the profitability of banks in Ghana. Obtaining panel data from the banks’ websites, the Ghana Statistical Service (GSS), and the Ghana Stock Exchange (GSE), the regression analysis was used to assess the drivers of profitability of banks in Ghana. The findings show that the bank-specific variables had no combined effect on profitability. Hence, the study concludes that the bank-specific variables do not have a significant influence on the performance of the Ghanaian listed banks. On the other hand, some of the external factors were observed to have a significant influence on profitability. The findings further showed that the drivers of profitability of the Ghanaian banks were, inflation, capital adequacy, and monetary policy. Since these are all external factors, the banks must learn how to predict and anticipate the external factors and make adjustments in their operations to enable them to improve profitability. While this study recommends to the managers of Ghanaian banks to be cautious in their operational decisions, the economy must also be managed soundly to ensure the growth of banks and hopefully avoid future crises.

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