Learning from trees: a mixed approach to building early warning systems for systemic banking crises (notice n° 235179)
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| fixed length control field | 01902cam a2200253 4500500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250112063744.0 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | fre |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | dc |
| 100 10 - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gabriele, Carmine |
| Relator term | author |
| 245 00 - TITLE STATEMENT | |
| Title | Learning from trees: a mixed approach to building early warning systems for systemic banking crises |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2021.<br/> |
| 500 ## - GENERAL NOTE | |
| General note | 69 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Banking 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 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | early warning system |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | regression tree |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | banking crises |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | ensemble methods |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | early warning system |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | regression tree |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | banking crises |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | ensemble methods |
| 786 0# - DATA SOURCE ENTRY | |
| Note | Vie & sciences de l'entreprise | o 211-212 | 1 | 2021-07-29 | p. 37-69 | 2262-5321 |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://shs.cairn.info/journal-vie-et-sciences-de-l-entreprise-2021-1-page-37?lang=en">https://shs.cairn.info/journal-vie-et-sciences-de-l-entreprise-2021-1-page-37?lang=en</a> |
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