Search results for "bank failure"
showing 7 items of 7 documents
Predicting failure in the U.S. banking sector: An extreme gradient boosting approach
2019
Abstract Banks play a central role in developed economies. Consequently, systemic banking crises destabilize financial markets and hamper global economic growth. In this study, extreme gradient boosting was used to predict bank failure in the U.S. banking sector. Key variables were identified to anticipate and prevent bank defaults. The data, which spanned the period 2001 to 2015, consisted of annual series of 30 financial ratios for 156 U.S. national commercial banks. Identifying leading indicators of bank failure is vital to help regulators and bank managers act swiftly before distressed financial institutions reach the point of no return. The findings indicate that lower values for retai…
Banking failure prediction: a boosting classification tree approach
2016
The recent financial crisis shows that failure of some financial institutions can cause other banks to fail and ultimately cause damage to the financial system worldwide. Eurozone banks that experienced either liquidity or solvency problems during the finan- cial markets turmoil were bailed out by their national governments with the financial support and supervision of the European Union. This paper applies the boosted classification tree methodology to predict failure in the banking sector and identifies four key scor- ecard variables that are worth tracking closely in order to anticipate and prevent bank financial distress. The data used in this study comprises 2006-2012 annual series of …
Revisiting bank failure in the United States: a fuzzy-set analysis
2020
Past financial crises have illustrated the importance of recognising the combinations of factors that can cause financial distress in the banking industry. Accordingly, this study uses fuzzy-set qualitative comparative analysis (fsQCA) to identify the combinations of factors that lead to bank failure. The data consist of 30 annual financial ratio series for 156 U.S. banks over a 15-year period (2001–2015). Identifying combinations of conditions that can produce bank failure is crucial to help regulators and bank managers. The fsQCA presented in this paper sheds light on the relationships between combinations of conditions and bank failure, providing a solution comprising two sufficient and …
‘Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk
2012
A small segment of credit default swaps (CDS) on residential mortgage backed securities (RMBS) stand implicated in the 2007 financial crisis. The dominance of a few big players in the chains of insurance and reinsurance for CDS credit risk mitigation for banks' assets has led to the idea of too interconnected to fail (TITF) resulting, as in the case of AIG, of a tax payer bailout. We provide an empirical reconstruction of the US CDS network based on the FDIC Call Reports for off balance sheet bank data for the 4th quarter in 2007 and 2008. The propagation of financial contagion in networks with dense clustering which reflects high concentration or localization of exposures between few parti…
Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach
2019
Abstract The banking sector plays a special role in the economy and has critical functions which are essential for economic stability. Hence, systemic banking crises disrupt financial markets and hinder global economic growth. In this study, Extreme Gradient Boosting, a state of the art machine learning method, is applied to identify a set of key leading indicators that may help predict and prevent bank failure in the Eurozone banking sector. The cross-sectional data used in this study consists of 25 annual financial ratio series for commercial banks in the Eurozone. The sample includes Eurozone listed failed and non-failed banks for the period 2006–2016. A number of early warning systems a…
Network-Based Computational Techniques to Determine the Risk Drivers of Bank Failures During a Systemic Banking Crisis
2018
This paper employs a computational model of solvency and liquidity contagion assessing the vulnerability of banks to systemic risk. We find that the main risk drivers relate to the financial connections a bank has and the market concentration, apart from the size of the bank triggering the contagion, while balance sheets play only a minor role. We also find that market concentration might facilitate banks to withstand liquidity shocks better while exposing them to larger solvency chocks. Our results are validated through an out-of-sample forecasting that shows that both type I and type II prediction errors are reduced if we include network characteristics in our prediction model.
A fuzzy-set qualitative comparative analysis model to predict bank bailouts: a study of the Spanish financial system
2020
This paper examines the restructuring of the Spanish financial system. This study is justified by the massive economic and social impact of this process in Spain. Based on the annual accounts and the annual reports of Spanish credit institutions, a model was created to predict the possibility of bank failure or bailout. The variables were selected following a review of the literature. They included the legal form of the credit institution (savings bank versus bank), leverage, real estate investment, gross operating margin, staff costs and non-performing loans. Two variables that had not previously been used in studies of this type were also included in the model: risk-weighted assets and co…