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- W345352524 abstract "We develop a model of neural network to study the bankruptcy of banks in the US in 2009.We provide a new model to predict bank defaults some time before the bankruptcy happens,taking into account the specific features of the current financial crisis. Based on data from theFederal Deposit Insurance Corporation our results corroborate the higher credit risk taken bydistressed banks, and their heavier concentration on real estate. Interestingly, distressedbanks do not show lower cost efficiency than their wealthy counterparts, so that bank failuresseem to be a consequence of careless bank strategies rather than low cost efficiency. Afterdrawing the profile of distressed banks, we use our model to predict possible futurebankruptcies and we test the performance of the model by comparing our predictions withthe actual bankruptcies between January and June 2010. Our model shows a highdiscriminant power and is able to differentiate correctly wealthy and distressed banks. Morespecifically, our model would have been able to predict in December 2009around 60% offailures happened in 2010." @default.
- W345352524 created "2016-06-24" @default.
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- W345352524 date "2010-01-01" @default.
- W345352524 modified "2023-09-24" @default.
- W345352524 title "Predicting bankruptcy using neural networks in the current financial crisis: a study for US commercial banks" @default.
- W345352524 hasPublicationYear "2010" @default.
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