Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios. Le, H., H. & Viviani, J. Research in International Business and Finance, 44:16-25, Elsevier, 4, 2018. ![Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.
@article{
title = {Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios},
type = {article},
year = {2018},
identifiers = {[object Object]},
pages = {16-25},
volume = {44},
websites = {https://www.sciencedirect.com/science/article/pii/S0275531917301241},
month = {4},
publisher = {Elsevier},
day = {1},
id = {3d862bcb-7278-336c-a13d-673bd8d8282f},
created = {2018-06-25T14:54:21.715Z},
accessed = {2018-06-25},
file_attached = {false},
profile_id = {35f49df4-e9cf-391e-a76a-8e9e316c42aa},
group_id = {ee11d323-dd56-3515-8742-9b1e2f0496f7},
last_modified = {2018-07-03T13:00:02.701Z},
read = {false},
starred = {true},
authored = {false},
confirmed = {false},
hidden = {false},
folder_uuids = {0f486643-3d08-4632-8e8e-e53433d903a9,0dc92730-3ada-4a54-aed4-a0a8e38d213a},
private_publication = {false},
abstract = {This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.},
bibtype = {article},
author = {Le, Hong Hanh and Viviani, Jean-Laurent},
journal = {Research in International Business and Finance}
}
Downloads: 0
{"_id":"oJ7wLGE35QZGEwjJK","bibbaseid":"le-viviani-predictingbankfailureanimprovementbyimplementingamachinelearningapproachtoclassicalfinancialratios-2018","downloads":0,"creationDate":"2018-06-30T13:35:33.773Z","title":"Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios","author_short":["Le, H., H.","Viviani, J."],"year":2018,"bibtype":"article","biburl":null,"bibdata":{"title":"Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios","type":"article","year":"2018","identifiers":"[object Object]","pages":"16-25","volume":"44","websites":"https://www.sciencedirect.com/science/article/pii/S0275531917301241","month":"4","publisher":"Elsevier","day":"1","id":"3d862bcb-7278-336c-a13d-673bd8d8282f","created":"2018-06-25T14:54:21.715Z","accessed":"2018-06-25","file_attached":false,"profile_id":"35f49df4-e9cf-391e-a76a-8e9e316c42aa","group_id":"ee11d323-dd56-3515-8742-9b1e2f0496f7","last_modified":"2018-07-03T13:00:02.701Z","read":false,"starred":"true","authored":false,"confirmed":false,"hidden":false,"folder_uuids":"0f486643-3d08-4632-8e8e-e53433d903a9,0dc92730-3ada-4a54-aed4-a0a8e38d213a","private_publication":false,"abstract":"This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.","bibtype":"article","author":"Le, Hong Hanh and Viviani, Jean-Laurent","journal":"Research in International Business and Finance","bibtex":"@article{\n title = {Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios},\n type = {article},\n year = {2018},\n identifiers = {[object Object]},\n pages = {16-25},\n volume = {44},\n websites = {https://www.sciencedirect.com/science/article/pii/S0275531917301241},\n month = {4},\n publisher = {Elsevier},\n day = {1},\n id = {3d862bcb-7278-336c-a13d-673bd8d8282f},\n created = {2018-06-25T14:54:21.715Z},\n accessed = {2018-06-25},\n file_attached = {false},\n profile_id = {35f49df4-e9cf-391e-a76a-8e9e316c42aa},\n group_id = {ee11d323-dd56-3515-8742-9b1e2f0496f7},\n last_modified = {2018-07-03T13:00:02.701Z},\n read = {false},\n starred = {true},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {0f486643-3d08-4632-8e8e-e53433d903a9,0dc92730-3ada-4a54-aed4-a0a8e38d213a},\n private_publication = {false},\n abstract = {This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.},\n bibtype = {article},\n author = {Le, Hong Hanh and Viviani, Jean-Laurent},\n journal = {Research in International Business and Finance}\n}","author_short":["Le, H., H.","Viviani, J."],"urls":{"Website":"https://www.sciencedirect.com/science/article/pii/S0275531917301241"},"bibbaseid":"le-viviani-predictingbankfailureanimprovementbyimplementingamachinelearningapproachtoclassicalfinancialratios-2018","role":"author","downloads":0},"search_terms":["predicting","bank","failure","improvement","implementing","machine","learning","approach","classical","financial","ratios","le","viviani"],"keywords":[],"authorIDs":[]}