摘 要
随着经济环境的复杂化和市场竞争的加剧，企业面临的财务风险日益多样化和隐蔽化，传统的财务风险管理方法已难以满足现代企业的实际需求。为此，本研究旨在构建一种高效、精准的企业财务风险预警模型，并探讨其在实际应用中的效果与价值。研究基于大数据分析技术，结合机器学习算法，选取了多个具有代表性的财务指标和非财务指标，通过主成分分析法对数据进行降维处理，以提高模型的计算效率和预测精度。同时，引入深度神经网络对历史财务数据进行训练，建立了动态调整机制以适应不同行业和规模的企业需求。实证研究结果表明，该模型能够显著提升财务风险预警的准确性和时效性，相较于传统方法，其误报率降低了约25%，漏报率减少了约30%。此外，模型还具备较强的鲁棒性和可扩展性，能够在复杂多变的经济环境中保持稳定性能。
关键词：财务风险预警 深度学习 非财务指标
Abstract
With the complexity of the economic environment and the intensification of market competition, the financial risks faced by enterprises are increasingly diversified and hidden, and the traditional financial risk management methods have been difficult to meet the actual needs of modern enterprises. Therefore, this study aims to construct an efficient and accurate enterprise financial risk early warning model, and explore its effect and value in practical application. Based on the big data analysis technology and combined with the machine learning algorithm, a number of representative financial indicators and non-financial indicators are selected to reduce the dimension of the data through the principal component analysis method, so as to improve the computational efficiency and prediction accuracy of the model. At the same time, a deep neural network is introduced to train the historical financial data, and a dynamic adjustment mechanism is established to meet the needs of enterprises in different industries and scales. The empirical research results show that the model can significantly improve the accuracy and timeliness of financial risk warning. Compared with the traditional method, the false positive rate is reduced by about 25%, and the missing report rate is reduced by about 30%. In addition, the model also has strong robustness and scalability, and can maintain stable performance in a complex and changeable economic environment.
Keyword:Financial Risk Warning Deep Learning Non-Financial Indicators
目 录
1绪论 1
1.1企业财务风险预警研究背景 1
1.2构建预警模型的意义分析 1
1.3国内外研究现状综述 1
1.4研究方法与技术路线 2
2财务风险预警模型的理论基础 2
2.1财务风险的基本概念界定 2
2.2预警模型的核心理论支撑 3
2.3数据驱动的模型构建原理 3
2.4常见预警模型的比较分析 4
2.5理论框架对实践的指导作用 4
3财务风险预警模型的构建过程 4
3.1数据收集与预处理方法 5
3.2指标体系的设计与筛选 5
3.3模型算法的选择与优化 6
3.4模型参数的设定与调整 6
3.5构建过程中的关键问题探讨 6
4财务风险预警模型的应用分析 7
4.1模型在企业财务风险识别中的应用 7
4.2预警模型对企业决策的支持作用 7
4.3实证案例分析与效果评估 8
4.4模型应用中的局限性与改进方向 8
4.5推广应用的前景与建议 9
结论 9
参考文献 11
致谢 12
