企业财务风险预警模型构建与应用
摘 要
随着经济全球化和市场竞争的加剧，企业面临的财务风险日益复杂多变，构建有效的财务风险预警模型对于保障企业稳健运营具有重要意义。本研究旨在建立一个科学合理的企业财务风险预警模型并探讨其应用价值。基于此目的，选取了涵盖盈利能力、偿债能力、营运能力和发展能力等多维度的财务指标体系，并引入非财务指标以增强模型的全面性与前瞻性。采用主成分分析法对原始数据进行降维处理，提取主要信息，再利用BP神经网络算法构建预警模型，该方法能够较好地拟合非线性关系且具备自学习能力。通过实证检验发现，所构建模型对企业财务风险识别准确率较高，在不同行业样本中均表现出良好的适应性和稳定性，可提前多个会计期间预警潜在财务危机。
关键词：财务风险预警模型 主成分分析法 BP神经网络
Abstract
With the intensification of economic globalization and market competition, the financial risks faced by enterprises are increasingly complex and changeable. It is of important significance to build an effective financial risk early warning model to guarantee the sound operation of enterprises. This study aims to establish a scientific and reasonable enterprise financial risk early warning model and explore its application value. Based on this purpose, a multi-dimensional financial index system covering profitability, solvency, operating capacity and development capacity is selected, and non-financial indicators are introduced to enhance the comprehensiveness and foresight of the model. Principal component analysis method is used to reduce the dimension of the original data, extract the main information, and then the BP neural network algorithm is used to build the early warning model. This method can better fit the nonlinear relationship and have the ability of self-learning. Through empirical test, it is found that the constructed model has high accuracy of enterprise financial risk identification, and shows good adaptability and stability in different industry samples, which can warn potential financial crisis in several accounting periods.
Keyword:Financial Risk Warning Model Principal Component Analysis Bp Neural Network
目 录
1绪论 1
1.1企业财务风险预警研究背景与意义 1
1.2国内外研究现状综述 1
1.3研究方法与技术路线 1
2企业财务风险识别与评估 2
2.1财务风险的类型与特征 2
2.2关键财务指标分析框架 3
2.3风险识别的数据来源与处理 3
3财务风险预警模型构建 4
3.1模型选择与理论基础 4
3.2指标体系设计原则 4
3.3模型参数优化与验证 5
4预警模型的应用与效果评价 6
4.1实证案例分析 6
4.2预警效果评估方法 6
4.3模型应用中的问题与改进建议 7
结论 7
参考文献 9
致谢 10
摘 要
随着经济全球化和市场竞争的加剧，企业面临的财务风险日益复杂多变，构建有效的财务风险预警模型对于保障企业稳健运营具有重要意义。本研究旨在建立一个科学合理的企业财务风险预警模型并探讨其应用价值。基于此目的，选取了涵盖盈利能力、偿债能力、营运能力和发展能力等多维度的财务指标体系，并引入非财务指标以增强模型的全面性与前瞻性。采用主成分分析法对原始数据进行降维处理，提取主要信息，再利用BP神经网络算法构建预警模型，该方法能够较好地拟合非线性关系且具备自学习能力。通过实证检验发现，所构建模型对企业财务风险识别准确率较高，在不同行业样本中均表现出良好的适应性和稳定性，可提前多个会计期间预警潜在财务危机。
关键词：财务风险预警模型 主成分分析法 BP神经网络
Abstract
With the intensification of economic globalization and market competition, the financial risks faced by enterprises are increasingly complex and changeable. It is of important significance to build an effective financial risk early warning model to guarantee the sound operation of enterprises. This study aims to establish a scientific and reasonable enterprise financial risk early warning model and explore its application value. Based on this purpose, a multi-dimensional financial index system covering profitability, solvency, operating capacity and development capacity is selected, and non-financial indicators are introduced to enhance the comprehensiveness and foresight of the model. Principal component analysis method is used to reduce the dimension of the original data, extract the main information, and then the BP neural network algorithm is used to build the early warning model. This method can better fit the nonlinear relationship and have the ability of self-learning. Through empirical test, it is found that the constructed model has high accuracy of enterprise financial risk identification, and shows good adaptability and stability in different industry samples, which can warn potential financial crisis in several accounting periods.
Keyword:Financial Risk Warning Model Principal Component Analysis Bp Neural Network
目 录
1绪论 1
1.1企业财务风险预警研究背景与意义 1
1.2国内外研究现状综述 1
1.3研究方法与技术路线 1
2企业财务风险识别与评估 2
2.1财务风险的类型与特征 2
2.2关键财务指标分析框架 3
2.3风险识别的数据来源与处理 3
3财务风险预警模型构建 4
3.1模型选择与理论基础 4
3.2指标体系设计原则 4
3.3模型参数优化与验证 5
4预警模型的应用与效果评价 6
4.1实证案例分析 6
4.2预警效果评估方法 6
4.3模型应用中的问题与改进建议 7
结论 7
参考文献 9
致谢 10
