摘 要：企业财务危机预警是防范金融风险、保障经济稳定的重要手段，本研究基于当前复杂多变的经济环境与企业财务风险频发的背景，旨在构建一种高效且精准的财务危机预警模型以提升风险识别能力。研究采用多元统计分析与机器学习相结合的方法，通过收集并处理大量企业财务数据，筛选出关键财务指标，并运用主成分分析法降低维度，同时引入支持向量机算法优化分类效果。实证分析以某地区上市公司为样本，验证了模型在预测精度和稳定性方面的优越性，结果显示该模型的准确率显著高于传统Logistic回归模型。本研究的创新点在于将机器学习技术与经典财务分析方法深度融合，有效解决了高维数据处理与非线性关系建模难题，为企业管理层及投资者提供了科学决策依据，同时也为完善金融市场风险防控体系贡献了理论支持与实践指导。
关键词：财务危机预警；机器学习；支持向量机
Abstract：Enterprise financial crisis early warning is an essential tool for preventing financial risks and ensuring economic stability. Against the backdrop of a complex and ever-changing economic environment, as well as the frequent occurrence of corporate financial risks, this study aims to construct an efficient and precise financial crisis early warning model to enhance the ability to identify risks. By integrating multivariate statistical analysis with machine learning techniques, the study collects and processes extensive financial data from enterprises, screens out key financial indicators, and applies principal component analysis to reduce dimensionality. Additionally, the support vector machine algorithm is introduced to optimize classification performance. Empirical analysis using listed companies in a specific region as samples demonstrates the superiority of the proposed model in terms of predictive accuracy and stability, with results indicating that its accuracy significantly surpasses that of the traditional Logistic regression model. The innovation of this research lies in the deep integration of machine learning technologies with classical financial analysis methods, effectively addressing challenges related to high-dimensional data processing and nonlinear relationship modeling. This provides a scientific basis for decision-making by enterprise management and investors, while also contributing theoretical support and practical guidance for improving the risk prevention and control system in financial markets.
Keywords: Financial Crisis Early Warning；Machine Learning；Support Vector Machine
目 录
引言 1
一、企业财务危机预警的理论基础 1
(一)财务危机的概念与特征 1
(二)预警模型的研究意义 2
(三)国内外研究现状分析 2
二、财务危机预警模型的构建方法 2
(一)数据选择与变量设定 3
(二)模型构建的技术路径 3
(三)关键指标体系的设计 3
三、实证分析的设计与实施 4
(一)样本企业的选取标准 4
(二)数据处理与模型验证 4
(三)实证结果的初步分析 5
四、预警模型的效果评估与优化 5
(一)模型预测能力的评价 6
(二)实证结果的深入解读 6
(三)模型优化的方向与策略 6
结论 7
参考文献 8
致谢 8
关键词：财务危机预警；机器学习；支持向量机
Abstract：Enterprise financial crisis early warning is an essential tool for preventing financial risks and ensuring economic stability. Against the backdrop of a complex and ever-changing economic environment, as well as the frequent occurrence of corporate financial risks, this study aims to construct an efficient and precise financial crisis early warning model to enhance the ability to identify risks. By integrating multivariate statistical analysis with machine learning techniques, the study collects and processes extensive financial data from enterprises, screens out key financial indicators, and applies principal component analysis to reduce dimensionality. Additionally, the support vector machine algorithm is introduced to optimize classification performance. Empirical analysis using listed companies in a specific region as samples demonstrates the superiority of the proposed model in terms of predictive accuracy and stability, with results indicating that its accuracy significantly surpasses that of the traditional Logistic regression model. The innovation of this research lies in the deep integration of machine learning technologies with classical financial analysis methods, effectively addressing challenges related to high-dimensional data processing and nonlinear relationship modeling. This provides a scientific basis for decision-making by enterprise management and investors, while also contributing theoretical support and practical guidance for improving the risk prevention and control system in financial markets.
Keywords: Financial Crisis Early Warning；Machine Learning；Support Vector Machine
目 录
引言 1
一、企业财务危机预警的理论基础 1
(一)财务危机的概念与特征 1
(二)预警模型的研究意义 2
(三)国内外研究现状分析 2
二、财务危机预警模型的构建方法 2
(一)数据选择与变量设定 3
(二)模型构建的技术路径 3
(三)关键指标体系的设计 3
三、实证分析的设计与实施 4
(一)样本企业的选取标准 4
(二)数据处理与模型验证 4
(三)实证结果的初步分析 5
四、预警模型的效果评估与优化 5
(一)模型预测能力的评价 6
(二)实证结果的深入解读 6
(三)模型优化的方向与策略 6
结论 7
参考文献 8
致谢 8
