机器学习与深度学习融入资产定价——理论框架与方法创新

晓全 刘(上海财经大学，中国)

摘要

论文系统性地探讨了机器学习与深度学习在实证资产定价中的应用，特别是在股票和债券市场中的创新方法与前沿应用。通过分析传统资产定价模型的理论基础，结合机器学习方法的优势，论文深入阐述了如何利用这些技术提升资产定价模型的预测精度和理论解释能力。研究表明，机器学习方法不仅在模型预测上表现优异，还为理解市场动态提供了新的视角。论文也探讨了这些方法在债券收益率预测、信用风险评估及组合优化中的应用潜力，并指出未来研究的挑战与方向。

关键词

实证资产定价；债券市场；因子模型；时间序列分析；图神经网络

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DOI: http://dx.doi.org/10.12345/cjygl.v8i10.21598