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衡量非上市农业企业信用风险的模型
Kaihao Liang1, Yuqiu Chen2, Tinghong Guo2
1Department of Mathematics, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China.
PloS one
|September 25, 2025
概括
本研究引入了一种新的方法,用于测量非上市农业企业的信贷风险,使用KMV模型和CNN-BiLSTM神经网络. 该模型有效评估信用评级,揭示了传统方法之外的隐藏风险.
科学领域:
- 农业金融 农业金融
- 金融风险管理 金融风险管理
- 金融中的机器学习
背景情况:
- 对非上市农业企业进行信用风险评估存在重大挑战,原因是数据稀缺和缺乏标准化评级.
- 传统的信用风险模型往往难以捕捉金融数据中固有的复杂,非线性关系.
研究的目的:
- 为非上市农业企业开发和验证一种新的信用风险评估模型.
- 将KMV (Kealhofer,McNeil和Vasicek) 模型与CNN-BiLSTM神经网络集成,以增强信用风险测量.
主要方法:
- 使用KMV框架与Black-Scholes期权定价公式计算上市农业企业的预期违约频率 (EDF).
- 在上市农业企业数据上训练CNN-BiLSTM神经网络模型的开发和优化.
- 将优化的CNN-BiLSTM模型应用于非上市农业企业的标准化财务数据,以推导EDF并分配评级间隔.
主要成果:
- 综合CNN-BiLSTM-KMV模型证明了对非上市农业企业的有效信用评级评估.
- 模型的结果通常与机构评级结果保持一致,表明业绩强.
- 该模型成功地发现了传统评级方法无法检测到的隐藏信用风险.
结论:
- CNN-BiLSTM-KMV模型为评估非上市农业企业的信誉提供了一个强大的工具.
- 该研究强调了企业信用风险与关键金融指标之间的非线性相互作用.
- 这种方法为金融机构和政策制定者在管理农业部门信用风险方面提供了宝贵的见解.
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