EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks-Reference-Cited by-同舟云学术

EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks

Published:2022-10 Issue: Volume:61 Page:101644
ISSN:0275-5319
Container-title:Research in International Business and Finance
language:en
Short-container-title:Research in International Business and Finance

Author:

Kristóf Tamás,Virág Miklós

Publisher

Elsevier BV

Subject

Finance,Business, Management and Accounting (miscellaneous)

Reference54 articles.

1. Is managerial ability a moderator? The effect of credit risk and liquidity risk on the likelihood of bank default;Abdesslem;Int. Rev. Financ. Anal.,2022

2. Bank solvency risk and funding cost interactions: Evidence from Korea;Aldasoro;J. Bank. Financ.,2022

3. Financial distress prediction in an international context: a review and empirical analysis of Altman’s Z-Score model;Altman;J. Int. Financ. Manag. Account.,2017

4. Predicting bank operational efficiency using machine learning algorithm: Comparative study of decision tree, random forest, and neural networks;Appiahene;Adv. Fuzzy Syst.,2020

5. Predicting U.S. bank failures with MIDAS logit models;Audrino;J. Financ. Quant. Anal.,2019

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