Early warning indicators of banking crisis and bank related stock returns

Sohn, Bumjean; Park, Heungju

doi:10.1016/j.frl.2016.04.016

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Also Borio and Drehmann (2009) have demonstrated that credit-to-GDP, equity, and property price gaps, in per cent relative to trends, are able to detect the build-up of risks of upcoming banking distress in an economy. Finally, Sohn and Park (2016) have examined the EWS of banking crisis and bank related stock returns and found that the credit growth is more informative in predicting bank sector crisis than the credit-to-GDP gap. Their findings have been confirmed to a large extent by Geršl and Jašová (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive strength of Macroeconomic Imbalance Procedure indicators

Biegun,

Dahl,

Karwowski

2024

EiP

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Motivation: The Macroeconomic Imbalance Procedure (MIP) is a key step in the European Semester, aimed at the coordination of the economic policies of the EU Member States to prevent excessive macroeconomic imbalances in the EU and support structural reforms. The MIP was originally envisaged as a legal tool for crisis prevention, allowing macroeconomic imbalances to be detected and then remedied, but is also used as an Early Warning System. However, the real strength of MIP indicators to predict crises has not been proved in practice and is widely contested in the literature.Aim: Fourteen scoreboard (“main”) and 28 auxiliary MIP indicators are currently in use. This paper is aimed at the assessment of the power of all MIP indicators in predicting crises.Results: The added value of our research is to test the MIP’s ability to predict changes in GDP, which may be considered as a proxy for the deterioration or improvement of the economic situation. Very little investigation has been done in this area so far. In addition, to our knowledge, no research papers have investigated the relevance of auxiliary MIP indicators. Our results show that only four main indicators (house price index, nominal unit labour cost index, general government sector debt, and export market shares) and another four auxiliary indicators (residential construction as percentage of GDP, activity rate, people living in households with very low work intensity, and export performance against advanced economies) seem to be able to predict the upcoming crises.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive strength of Macroeconomic Imbalance Procedure indicators

Biegun,

Dahl,

Karwowski

2024

EiP

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“…Dabrowski et al (2016) advocated for dynamic Bayesian networks for systemic banking crisis early warning systems across eleven developed European economies. Sohn and Park (2016) prioritized credit growth as a more informative indicator of banking sector crises than the credit-to-GDP gap. Antunes et al (2018) identified equity prices, house price growth, and debt service ratio among the most useful indicators for signaling banking crises in European countries.…”

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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“…Also, Borio and Drehmann (2009) demonstrated that credit-to-GDP, equity, and property price gaps, in percent relative to trends, can detect the build-up of risks of upcoming banking distress in an economy. Sohn and Park (2016) examined EWS of the banking crisis and bank-related stock returns and found that credit growth is more informative in predicting bank sector crisis than the credit-to-GDP gap. Their findings have been confirmed to a large extend by Geršl and Jašová (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?

Biegun¹,

Karwowski²,

Luty³

2021

ERSJ

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Purpose:The evaluation of the predictive power of Macroeconomic Imbalance Procedure (MIP) indicators is crucial for coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic crisis prevention procedure. Design/Methodology/Approach: Using the Bayesian model averaging (BMA) framework, we compare different models where lagged MIP indicators try to explain several macroeconomic variables associated with crises. Findings: The results show that the importance of MIP indicators between 2001 and 2017 was diversified. In the case of annual real GDP growth, including a 1-year lagged house price index, nominal unit labor cost, real effective exchange rate (1-year change), and export market share in the model improves the model's explanatory power most. For explaining inflation rate, export market share (again), and house price index is valid. Practical Implications: The construction of the MIP procedure should be simplified, as not all indicators have a fundamental capability of predicting excessive imbalances which result in crisis events. Indicators are relevant to the current economic priorities of the EU, which do not have a significant capacity to anticipate crisis phenomena should be excluded from the Alert Mechanism. Originality/Value: We use the Bayesian model averaging (BMA) framework BMA that directly deals with heterogeneity by finding a combination of regressors that account for it to the greatest extent within a conditioning set of information. Consequently, BMA appears to be ideally suited for finding robust determinants of "crisis" variables.

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Early warning indicators of banking crisis and bank related stock returns

Cited by 9 publications

References 14 publications

Predictive strength of Macroeconomic Imbalance Procedure indicators

Predictive strength of Macroeconomic Imbalance Procedure indicators

Assessing Machine Learning Techniques for Predicting Banking Crises in India

How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?

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