Sequence-based clustering applied to long-term credit risk assessment-Reference-Cited by-同舟云学术

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Sequence-based clustering applied to long-term credit risk assessment

Published:2021-03 Issue: Volume:165 Page:113940
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Le Richard,Ku Hyejin,Jun Doobae

Funder

National Research Foundation of Korea

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference29 articles.

1. Clustering and visualization of bankruptcy trajectory using self-organizing map;Chen;Expert Systems with Applications,2013

2. Confidence sets for continuous-time rating transition probabilities;Christensen;Journal of Banking & Finance,2004

3. A review of non-Markovian models for the dynamics of credit ratings;D’Amico;Reports on Economics and Finance,2019

4. Markov Chain model with catastrophe to determine mean time to default of credit risky assets;Dharmaraja;Journal of Statistical Physics,2017

5. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement;Forman;ACM SIGKDD Explorations Newsletter,2010

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1. Ensemble Learning with Feature Optimization for Credit Risk Assessment;2024-07-29

2. Estimating default probabilities for no- and low-default portfolios: parameter specification via floor constraints;Journal of the Royal Statistical Society Series C: Applied Statistics;2023-07-14

3. Inference on the symmetry point-based optimal cut-off point and associated sensitivity and specificity with application to SARS-CoV-2 antibody data;SORT-STAT OPER RES T;2023

4. Financial supply chain analysis with borrower identification in smart lending platform;Expert Systems with Applications;2022-12

5. Credit risk evaluation: a comprehensive study;Multimedia Tools and Applications;2022-10-04

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