2018
DOI: 10.1002/ijfe.1680
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Bank competition, stability, and intervention quality

Abstract: We use a flexible semi‐parametric estimation approach and a sample of 7,227 U.S., U.K., and Canadian banks for 2009–2015 to provide evidence that banking stability is non‐linearly determined by competition. We show that stability is not monotonic against competition, and may increase and decrease at high competition, has a mixed behaviour at medium competition, and increases at low competition. This non‐monotonic stability behaviour at different competition levels is attributed to the intervention quality, whi… Show more

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Cited by 22 publications
(21 citation statements)
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References 85 publications
(160 reference statements)
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“…The Lerner index indicates that there is a positive and then negative link with NPL, and the opposite is true with the LZScore. NPL and the LZScore results confirm both competition‐stability and competition‐fragility for the GCC economies or a U‐shaped non‐linear relationship, similar to González et al (2017), Noman et al (2017) and Albaity et al (2019) and Kanas et al (2019). On the other hand, the Boone indicator suggested that with the LZScore, that the higher the competition, the lower the bank stability, confirming competition‐fragility in the GCC countries.…”
Section: Discussionsupporting
confidence: 81%
See 1 more Smart Citation
“…The Lerner index indicates that there is a positive and then negative link with NPL, and the opposite is true with the LZScore. NPL and the LZScore results confirm both competition‐stability and competition‐fragility for the GCC economies or a U‐shaped non‐linear relationship, similar to González et al (2017), Noman et al (2017) and Albaity et al (2019) and Kanas et al (2019). On the other hand, the Boone indicator suggested that with the LZScore, that the higher the competition, the lower the bank stability, confirming competition‐fragility in the GCC countries.…”
Section: Discussionsupporting
confidence: 81%
“…The bank stability measures BS i,j,t included in this study are; the Non‐Performing Loan (hereafter NPL) ratio and the LZScore (the natural logarithm of the Zscore). We have used these two proxies to measure bank stability, following previous literature, such as Kasman and Kasman (2015), Shijaku (2017), Noman et al (2018), Kanas, Al‐Tamimi, Albaity, and Mallek (2019) and Albaity et al (2019). The lag coefficient corresponds to how the rate of bank stability in the previous period influences the current period.…”
Section: Econometric Methodologymentioning
confidence: 99%
“…The semi-parametric GAM analysis was implemented to capture non-linear relationships among the study variables. This model, unlike linear analysis, offers non-restrictive flexible variables specifications that improve the estimation process and better reflect market fluctuations (Kanas et al , 2019), especially for stock returns in emerging markets. This model applies a non-restrictive flexible function for f 1 ( INFOQ ), while other control variables are entered into the equation using the linear format.…”
Section: Methodsmentioning
confidence: 99%
“…ROA measures a firm's success in using its assets to generate earnings (Selling and Stickney, 1989). Previous studies have found a significant negative relationship between the ROA and NPLs (Kanas et al, 2019). Therefore, ROA was controlled for in this study.…”
Section: Npl Ratio ¼ Npl Gross Loansmentioning
confidence: 98%