Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
| Autor: | Wenfang Xue, Zeyu Liu, Bojing Feng, Bindang Xue |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Artificial neural network business.industry Computer science Feature extraction Machine learning computer.software_genre Convolutional neural network Corporation Machine Learning (cs.LG) Data modeling FOS: Economics and business Credit rating Risk Management (q-fin.RM) Feature (machine learning) Artificial intelligence business computer Financial statement Quantitative Finance - Risk Management |
| Zdroj: | 2020 IEEE 6th International Conference on Computer and Communications (ICCC). |
| DOI: | 10.1109/iccc51575.2020.9344973 |
| Popis: | Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently. Comment: 6 pages. arXiv admin note: text overlap with arXiv:2012.01933 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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