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Credit Default Prediction Based on Multivariate Regression

In: Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)

Author

Listed:
  • Yingzi Sun

    (University of Arizona)

  • Lirui Yang

    (Guangzhou Foreign Language School ISA Wenhua IB Programme)

  • Ruonan Zhao

    (Tianjin University of Finance and Economics, Pearl River College)

Abstract

Credit default is a wide-spread credit derivative instrument. As it becomes more and more popular, an appropriate supervision system has to be established. In this paper, a multiple factor regression models are constructed in order to investigate the feasibility for credit default prediction based on R program. Since risks are unavoidable, some measures should be taken to predict them in order to help the banks that sell credit default swaps to minimize their risks. According to the analysis, a model is successfully created. These results shed light on guiding further exploration focusing on credit default prediction.

Suggested Citation

  • Yingzi Sun & Lirui Yang & Ruonan Zhao, 2023. "Credit Default Prediction Based on Multivariate Regression," Advances in Economics, Business and Management Research, in: Yushi Jiang & Guangming Li & Wilson Xinbao Li (ed.), Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), pages 16-23, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-142-5_3
    DOI: 10.2991/978-94-6463-142-5_3
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