Does the risk on banks’ balance sheets predict banking crises? New evidence for developing countries

Haan, Jakob de; Fang, Yuan; Jing, Zhongbo

doi:10.1016/j.iref.2020.03.013

Cited by 12 publications

(6 citation statements)

References 51 publications

(51 reference statements)

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“…In the banking literature, the variables that reflect bank fundamentals are CAMEL variables, which relate to management, capital adequacy, efficiency, management, and earnings. Indeed, a comprehensive set of recent studies confirmed the early findings that these indicators are good predictors of bank failure (Liu et al, 2021;De Haan et al, 2020;Bongini et al, 2018). Accordingly, we suggest our second hypothesis: Hypothesis 2: BETA depends significantly on the CAMEL variables.…”

Section: Literature Review and Hypotheses Developmentsupporting

confidence: 78%

Equity Holders' Market Discipline in GCC Countries

2022

ijafr

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This paper aims to study Equity holders'(EHs) discipline in three GCC countries, Bahrain, the UAE, and KSA for the period (2004-2014). We test bank risk monitoring in the 2008 crisis period by comparing the perceived risk proxied by BETA during the pre-and post-crisis episodes. We find significant differences in BETA values in Bahrain and the UAE. Then, we use the fixed effect model and the random effect model to regress BETA on CAMEL variables measuring bank risk. We study bank reaction by regression relevant CAMEL variables to EH monitoring on BETA of a lagged period. Our results show that in Bahrain, the coefficients of the variables Liquidity and Size are significant. Thus, EHs, in Bahrain, monitor their bank using financial information on Liquidity and Size. However, we find no responsiveness of the banks to this monitoring. Thus, there is no evidence of EHs' discipline in GCC countries. We contribute first, to fulfilling the gap in market discipline literature in Emerging countries, specifically, Bahrain, KSA, and the UAE. Second, we shed light on the market discipline of a special type of banking-IBs. Third, we innovate a new monitoring signal, BETA values assessed by EHs.

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Section: Literature Review and Hypotheses Developmentsupporting

confidence: 78%

Equity Holders' Market Discipline in GCC Countries

2022

ijafr

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“…To identify the effect of macroeconomic conditions, the commonly used macroeconomic variables are the annual percent change of the GDP and the annual percent change of inflation. Thus, based on previous studies in this area it can be concluded that economic growth (GDP) has a positive effect on a bank's performance (Kosmidou et al, 2005;Pasiouras and Kosmidou, 2007;Athanasoglou et al, 2008;Anbar and Alper, 2011;Derbali, 2011;Lainà et al, 2015;Shijaku, 2016;Pedro et al, 2018;de Haan et al, 2020). On the other hand, the impact of inflation on a bank's performance has been divided into two aspects: a positive relationship (Kosmidou et al, 2005;Athanasoglou et al, 2006;Pasiouras and Kosmidou, 2007;Athanasoglou et al, 2008;Pedro et al, 2018) or a negative relationship (Kosmidou, 2008;de Haan et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, based on previous studies in this area it can be concluded that economic growth (GDP) has a positive effect on a bank's performance (Kosmidou et al, 2005;Pasiouras and Kosmidou, 2007;Athanasoglou et al, 2008;Anbar and Alper, 2011;Derbali, 2011;Lainà et al, 2015;Shijaku, 2016;Pedro et al, 2018;de Haan et al, 2020). On the other hand, the impact of inflation on a bank's performance has been divided into two aspects: a positive relationship (Kosmidou et al, 2005;Athanasoglou et al, 2006;Pasiouras and Kosmidou, 2007;Athanasoglou et al, 2008;Pedro et al, 2018) or a negative relationship (Kosmidou, 2008;de Haan et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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Alternative measures for predicting financial distress in the case of Malaysian Islamic banks: assessing the impact of global financial crisis

Asutay

Othman

2020

JIABR

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Purpose The global financial crisis of 2008 still has an impact on the financial systems around the world, for which funding liquidity has been mentioned as one of the main concerns during that period. This study aims to consider the impact of and extent to which the funding structure of Islamic banks along with deposit structure, macroeconomic variables, other bank-specific variables, including alternative funding mix variables (in terms of funding structure measured as financing/deposit ratio), could play a part in explaining the financial conditions and predicting the failures and performances of Islamic banks in the case of Malaysia under the distress created by the global financial crisis. Design/methodology/approach Multivariate logit model was used with a sample including 17 full-fledged Islamic banks in Malaysia for the period from December 2005 to September 2010 by using quarterly data. Findings This study found that the funding mix variable (financing/deposit ratio), the composition of deposits, alternative bank-specific variables and alternative funding mix variables are statistically significant. In contrast, none of the macroeconomic variables is found to have a significant impact on bank liquidity. In the final models, the variables that showed significant performance were selected as explanatory variables. The results of McFadden R-squared for both selected models showed an excellent fit to predict the Islamic banks’ performance. Originality/value This empirical study contributes to the literature in two ways: to the best of the authors’ knowledge, this is the first study to examine the role of the funding structures of Islamic banks in determining their performance; and it also examines the effect of deposit composition (the mudharabah and non-mudharabah deposits) on Islamic banks’ performance.

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“…Petropoulos et al (2020) identified random forests as superior in out-of-sample and out-oftime predictive performance among US-based banks. Shrivastav and Ramudu (2020) achieved 93 percent accuracy in predicting Indian bank defaults using support vector machine methodology on a dataset spanning 2000 to 2017. de Haan et al (2020) emphasized the role of bank balance sheet information in predicting future banking crises, highlighting the significance of low bank liquid assets and high financial leverage. Filippopoulou et al (2020) evaluated risk indicators from the European Central Bank and European Systemic Risk Board, emphasizing the importance of specific banking variables over macroeconomic variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Machine Learning Techniques for Predicting Banking Crises in India

Puli,

Thota,

Subrahmanyam

2024

JRFM

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The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies.

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Does the risk on banks’ balance sheets predict banking crises? New evidence for developing countries

Cited by 12 publications

References 51 publications

Equity Holders' Market Discipline in GCC Countries

Equity Holders' Market Discipline in GCC Countries

Alternative measures for predicting financial distress in the case of Malaysian Islamic banks: assessing the impact of global financial crisis

Assessing Machine Learning Techniques for Predicting Banking Crises in India

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