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对上市公司的信贷风险预测模型基于改进的强化学习和贝叶斯优化超频带优化
Cai Yuanqing1, Zhenming Gao2, Zhang Jian3
1College of Business and Public Management, Wenzhou-Kean University, Wenzhou, China.
PloS one
|October 28, 2025
概括
本研究引入了一种使用强化学习 (近接政策优化) 进行特征选择和不平衡分类的新型信贷风险预测方法. 该方法显著提高了上市公司的预测准确性.
科学领域:
- 金融风险管理 金融风险管理
- 金融中的机器学习
- 计算经济学 计算经济学
背景情况:
- 由于金融部门的快速增长,对金融机构,监管机构和投资者来说,信用风险预测至关重要.
- 传统的信用风险模型 (z-score,logit,KVM,神经网络) 在特征选择,不平衡分类和超参数优化方面面临挑战.
- 现有的方法往往产生低于最佳的结果,需要先进的方法.
研究的目的:
- 为上市公司开发先进的信贷风险预测模型.
- 通过整合强化学习和先进的优化技术来解决传统模型的局限性.
- 提高信用风险预测的准确性和效率.
主要方法:
- 利用了政策之外的近接政策优化 (PPO) 算法,一种强化学习 (RL) 技术,用于有效的特征选择和不平衡的分类.
- 采用贝叶斯优化超带 (BOHB) 进行高效的超参数优化,将贝叶斯优化与超带合并.
- 在不同的数据集上验证了模型:CSMAR,MorningStar,KMV默认,GMSC和UCICCD.
主要成果:
- 拟议的模型在所有测试的数据集中,与最先进的方法相比,实现了更高的性能.
- 获得的特定F测量值为90.763% (CSMAR),86.358% (MorningStar),87.047% (KMV默认),90.576% (GMSC) 和89.485% (UCICCD).这些测量值的比率均为90.763% (CSMAR),86.358% (MorningStar),87.047% (KMV默认),90.576% (GMSC) 和89.485% (UCICCD).这些测量值的比率均为90.763% (CSMAR),86.358% (MorningStar),87.047% (KMV默认),90.576% (GMSC),以及89.485% (UCICCD) 的测量值.
- 通过基于RL的PPO证明了提升样本效率和优化数据利用.
结论:
- 新型信贷风险预测方法在金融设置中显示出显著的前景和效率.
- PPO和BOHB的整合代表了信用风险评估系统的重大进步.
- 这些发现支持该模型在增强信用风险调查方法方面的有效性.
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