@article{ART003083993},
author={Hyeokjin Choi and Gyeongho Jung and Ahn, Hyunchul},
title={A Study on Predicting Credit Ratings of Korean Companies using TabNet},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={5},
pages={11-20},
doi={10.9708/jksci.2024.29.05.011}
TY - JOUR
AU - Hyeokjin Choi
AU - Gyeongho Jung
AU - Ahn, Hyunchul
TI - A Study on Predicting Credit Ratings of Korean Companies using TabNet
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 5
PB - The Korean Society Of Computer And Information
SP - 11
EP - 20
SN - 1598-849X
AB - This study presents TabNet, a novel deep learning method, to enhance corporate credit rating accuracy amidst growing financial market uncertainties due to technological advancements. By analyzing data from major Korean stock markets, the research constructs a credit rating prediction model using TabNet. Comparing it with traditional machine learning, TabNet proves superior, achieving a Precision of 0.884 and an F1 score of 0.895. It notably reduces misclassification of high-risk companies as low-risk, emphasizing its potential as a vital tool for financial institutions in credit risk management and decision-making.
KW - Corporate Credit Rating Prediction;TabNet;Machine Learning;Deep Learning;Credit Risk Management
DO - 10.9708/jksci.2024.29.05.011
ER -
Hyeokjin Choi, Gyeongho Jung and Ahn, Hyunchul. (2024). A Study on Predicting Credit Ratings of Korean Companies using TabNet. Journal of The Korea Society of Computer and Information, 29(5), 11-20.
Hyeokjin Choi, Gyeongho Jung and Ahn, Hyunchul. 2024, "A Study on Predicting Credit Ratings of Korean Companies using TabNet", Journal of The Korea Society of Computer and Information, vol.29, no.5 pp.11-20. Available from: doi:10.9708/jksci.2024.29.05.011
Hyeokjin Choi, Gyeongho Jung, Ahn, Hyunchul "A Study on Predicting Credit Ratings of Korean Companies using TabNet" Journal of The Korea Society of Computer and Information 29.5 pp.11-20 (2024) : 11.
Hyeokjin Choi, Gyeongho Jung, Ahn, Hyunchul. A Study on Predicting Credit Ratings of Korean Companies using TabNet. 2024; 29(5), 11-20. Available from: doi:10.9708/jksci.2024.29.05.011
Hyeokjin Choi, Gyeongho Jung and Ahn, Hyunchul. "A Study on Predicting Credit Ratings of Korean Companies using TabNet" Journal of The Korea Society of Computer and Information 29, no.5 (2024) : 11-20.doi: 10.9708/jksci.2024.29.05.011
Hyeokjin Choi; Gyeongho Jung; Ahn, Hyunchul. A Study on Predicting Credit Ratings of Korean Companies using TabNet. Journal of The Korea Society of Computer and Information, 29(5), 11-20. doi: 10.9708/jksci.2024.29.05.011
Hyeokjin Choi; Gyeongho Jung; Ahn, Hyunchul. A Study on Predicting Credit Ratings of Korean Companies using TabNet. Journal of The Korea Society of Computer and Information. 2024; 29(5) 11-20. doi: 10.9708/jksci.2024.29.05.011
Hyeokjin Choi, Gyeongho Jung, Ahn, Hyunchul. A Study on Predicting Credit Ratings of Korean Companies using TabNet. 2024; 29(5), 11-20. Available from: doi:10.9708/jksci.2024.29.05.011
Hyeokjin Choi, Gyeongho Jung and Ahn, Hyunchul. "A Study on Predicting Credit Ratings of Korean Companies using TabNet" Journal of The Korea Society of Computer and Information 29, no.5 (2024) : 11-20.doi: 10.9708/jksci.2024.29.05.011
