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Corporate Credit Rating: A Survey

arXiv 2309.14349 ↗First posted 19 Sep 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.

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References · 11

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2014Distributed Representations of Sentences and Documentscited by 678
  3. 2018Model Agnostic Supervised Local Explanationscited by 83
  4. 2016Classification-based Financial Markets Prediction using Deep Neural Networkscited by 25
  5. 2020Application of Deep Neural Networks to assess corporate Credit Ratingcited by 9
  6. 2020Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networkscited by 6
  7. 2020Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networkscited by 4
  8. 2021Contrastive Pre-training for Imbalanced Corporate Credit Ratingscited by 4
  9. 2021Adversarial Semi-supervised Learning for Corporate Credit Ratingscited by 2
  10. 2019Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Predictioncited by 1
  11. 2021A Sparsity Algorithm with Applications to Corporate Credit Ratingcited by 1

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