000			01902cam a2200253 4500500
005			20250112063744.0
041			_afre
042			_adc
100	1	0	_aGabriele, Carmine _eauthor
245	0	0	_aLearning from trees: a mixed approach to building early warning systems for systemic banking crises
260			_c2021.
500			_a69
520			_aBanking crises can be extremely costly. The early detection of vulnerabilities can help prevent or mitigate those costs. We have developed an early warning model for systemic banking crises that combines regression tree technology with a statistical algorithm (CRAGGING), with the objective of improving accuracy and overcoming the drawbacks of previously used models. Our model has a large set of desirable features. It provides endogenously determined critical thresholds for a set of useful indicators, presented in the intuitive form of a decision tree structure. Our framework accounts for the conditional relations between various indicators when setting early warning thresholds. This facilitates the production of accurate early warning signals as compared to the signals from a logit model and from a standard regression tree. Our model also suggests that high credit aggregates, both in terms of volume and long-term trends, as well as low market-risk perception, are among the most important indicators for predicting the buildup of vulnerabilities in the banking sector.
690			_aearly warning system
690			_aregression tree
690			_abanking crises
690			_aensemble methods
690			_aearly warning system
690			_aregression tree
690			_abanking crises
690			_aensemble methods
786	0		_nVie & sciences de l'entreprise | o 211-212 | 1 | 2021-07-29 | p. 37-69 | 2262-5321
856	4	1	_uhttps://shs.cairn.info/journal-vie-et-sciences-de-l-entreprise-2021-1-page-37?lang=en
999			_c235179 _d235179