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A Study on Predicting Credit Ratings of Korean Companies using TabNet

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(5), pp.11-20
  • DOI : 10.9708/jksci.2024.29.05.011
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 15, 2024
  • Accepted : May 8, 2024
  • Published : May 31, 2024

Hyeokjin Choi 1 Gyeongho Jung 1 Hyunchul Ahn 1

1국민대학교

Accredited

ABSTRACT

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.

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