企业财务危机预警系统构建与应用
摘要
随着经济环境的复杂化和市场竞争的加剧，企业财务危机的频发对经济发展构成了严峻挑战，因此构建科学有效的财务危机预警系统成为学术界与实务界的共同关注焦点本研究以提升企业财务危机预警能力为目标，基于多元统计分析与机器学习技术，融合财务与非财务指标，设计了一套动态、智能的预警模型体系研究选取了2010年至2022年间中国A股上市公司为样本，采用主成分分析法筛选关键变量，并结合支持向量机算法优化分类效果结果表明，相较于传统单一模型，该预警系统在准确性和时效性方面均有显著提升，能够提前多个会计期间识别潜在财务风险此外，研究发现非财务指标如市场表现、管理层行为等在预测中发挥了重要作用，进一步丰富了现有理论框架本研究的主要创新点在于将动态监测机制与智能化算法相结合，突破了静态分析的局限性，同时为企业风险管理提供了实践指导，有助于增强决策的科学性和前瞻性这一成果不仅对单体企业具有重要应用价值，也为行业监管和政策制定提供了参考依据
关键词：财务危机预警；机器学习；非财务指标
Abstract
With the increasing complexity of economic environments and intensifying market competition, the frequent occurrence of corporate financial crises has posed severe challenges to economic development. Consequently, constructing a scientifically robust and effective financial crisis early warning system has become a shared focus for both academia and practice. This study aims to enhance the capability of financial crisis early warning by integrating multivariate statistical analysis with machine learning techniques, incorporating both financial and non-financial indicators into a dynamic and intelligent model fr amework. A sample of Chinese A-share listed companies from 2010 to 2022 was selected, and principal component analysis was employed to screen key variables, while the support vector machine algorithm was utilized to optimize classification performance. The results demonstrate that, compared with traditional single-model approaches, this early warning system achieves significant improvements in both accuracy and timeliness, enabling the identification of potential financial risks several accounting periods in advance. Furthermore, the study reveals that non-financial indicators, such as market performance and managerial behavior, play a crucial role in prediction, thereby enriching the existing theoretical fr amework. The primary innovation of this research lies in the integration of dynamic monitoring mechanisms with intelligent algorithms, which overcomes the limitations of static analysis. Simultaneously, it provides practical guidance for enterprise risk management, enhancing the scientific rigor and foresight of decision-making. This achievement not only holds substantial application value for individual enterprises but also offers reference for industry supervision and policy formulation.
Keywords：Financial Crisis Early Warning; Machine Learning; Non-Financial Indicators
目 录
摘要 I
Abstract II
一、绪论 1
(一) 企业财务危机预警研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 研究方法与技术路线 2
二、财务危机预警系统理论基础 2
(一) 财务危机的概念与特征 2
(二) 预警系统的理论框架 3
(三) 关键指标体系的构建原则 3
三、财务危机预警模型的设计与实现 4
(一) 数据采集与预处理方法 4
(二) 预警模型的选择与优化 5
(三) 模型验证与效果评估 5
四、财务危机预警系统的应用实践 6
(一) 实证案例的选择与背景介绍 6
(二) 预警系统的实施过程分析 6
(三) 应用效果评价与改进建议 7
结 论 9
参考文献 10
摘要
随着经济环境的复杂化和市场竞争的加剧，企业财务危机的频发对经济发展构成了严峻挑战，因此构建科学有效的财务危机预警系统成为学术界与实务界的共同关注焦点本研究以提升企业财务危机预警能力为目标，基于多元统计分析与机器学习技术，融合财务与非财务指标，设计了一套动态、智能的预警模型体系研究选取了2010年至2022年间中国A股上市公司为样本，采用主成分分析法筛选关键变量，并结合支持向量机算法优化分类效果结果表明，相较于传统单一模型，该预警系统在准确性和时效性方面均有显著提升，能够提前多个会计期间识别潜在财务风险此外，研究发现非财务指标如市场表现、管理层行为等在预测中发挥了重要作用，进一步丰富了现有理论框架本研究的主要创新点在于将动态监测机制与智能化算法相结合，突破了静态分析的局限性，同时为企业风险管理提供了实践指导，有助于增强决策的科学性和前瞻性这一成果不仅对单体企业具有重要应用价值，也为行业监管和政策制定提供了参考依据
关键词：财务危机预警；机器学习；非财务指标
Abstract
With the increasing complexity of economic environments and intensifying market competition, the frequent occurrence of corporate financial crises has posed severe challenges to economic development. Consequently, constructing a scientifically robust and effective financial crisis early warning system has become a shared focus for both academia and practice. This study aims to enhance the capability of financial crisis early warning by integrating multivariate statistical analysis with machine learning techniques, incorporating both financial and non-financial indicators into a dynamic and intelligent model fr amework. A sample of Chinese A-share listed companies from 2010 to 2022 was selected, and principal component analysis was employed to screen key variables, while the support vector machine algorithm was utilized to optimize classification performance. The results demonstrate that, compared with traditional single-model approaches, this early warning system achieves significant improvements in both accuracy and timeliness, enabling the identification of potential financial risks several accounting periods in advance. Furthermore, the study reveals that non-financial indicators, such as market performance and managerial behavior, play a crucial role in prediction, thereby enriching the existing theoretical fr amework. The primary innovation of this research lies in the integration of dynamic monitoring mechanisms with intelligent algorithms, which overcomes the limitations of static analysis. Simultaneously, it provides practical guidance for enterprise risk management, enhancing the scientific rigor and foresight of decision-making. This achievement not only holds substantial application value for individual enterprises but also offers reference for industry supervision and policy formulation.
Keywords：Financial Crisis Early Warning; Machine Learning; Non-Financial Indicators
目 录
摘要 I
Abstract II
一、绪论 1
(一) 企业财务危机预警研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 研究方法与技术路线 2
二、财务危机预警系统理论基础 2
(一) 财务危机的概念与特征 2
(二) 预警系统的理论框架 3
(三) 关键指标体系的构建原则 3
三、财务危机预警模型的设计与实现 4
(一) 数据采集与预处理方法 4
(二) 预警模型的选择与优化 5
(三) 模型验证与效果评估 5
四、财务危机预警系统的应用实践 6
(一) 实证案例的选择与背景介绍 6
(二) 预警系统的实施过程分析 6
(三) 应用效果评价与改进建议 7
结 论 9
参考文献 10
