摘要
随着大数据技术的快速发展，企业财务风险管理逐渐从传统模式向数据驱动型转变，这为上市公司提升风险预警能力提供了新的机遇。本研究旨在探讨基于大数据技术的上市公司财务风险管理方法，以实现更精准的风险识别与控制。研究选取了2015年至2022年间中国A股市场中具有代表性的上市公司为样本，结合财务报表、市场交易数据及非结构化信息，构建了一个多层次的大数据财务风险评估模型。该模型采用机器学习算法对财务风险进行分类预测，并通过特征重要性分析确定关键风险驱动因素。研究结果表明，大数据技术能够显著提高财务风险预测的准确性和时效性，尤其是非财务数据（如舆情信息和管理层行为）在风险评估中的作用不可忽视。此外，研究发现不同行业间的财务风险特征存在显著差异，需针对性地调整风险管理策略。本研究的主要创新点在于将非结构化数据纳入财务风险评估体系，并提出了一种动态更新的预测框架，为上市公司优化资源配置和制定战略决策提供了理论支持和技术保障，同时为监管机构加强市场监督提供了参考依据。
关键词：大数据财务风险评估；非结构化数据；机器学习；风险预警；行业差异性
Abstract
With the rapid development of big data technology, enterprise financial risk management is gradually shifting from traditional models to data-driven approaches, offering new opportunities for listed companies to enhance their risk warning capabilities. This study aims to explore financial risk management methods for listed companies based on big data technology to achieve more precise risk identification and control. By selecting representative listed companies in China's A-share market from 2015 to 2022 as samples, this research constructs a multi-layered big data financial risk assessment model that integrates financial statements, market transaction data, and unstructured information. The model employs machine learning algorithms for classification and prediction of financial risks and determines key risk drivers through feature importance analysis. The results indicate that big data technology can significantly improve the accuracy and timeliness of financial risk prediction, particularly highlighting the indispensable role of non-financial data, such as public sentiment and managerial behavior, in risk assessment. Moreover, the study finds significant differences in financial risk characteristics across industries, suggesting the need for tailored risk management strategies. A major innovation of this research lies in incorporating unstructured data into the financial risk assessment system and proposing a dynamically updated predictive fr amework, which provides theoretical support and technical assurance for listed companies to optimize resource allocation and strategic decision-making, while also offering reference for regulatory authorities to strengthen market supervision.
Keywords：Big Data Financial Risk Assessment; Unstructured Data; Machine Learning; Risk Warning; Industry Differences
目 录
摘要 I
Abstract II
一、绪论 1
(一) 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、大数据在财务风险管理中的应用基础 2
(一) 大数据技术的核心特征 2
(二) 上市公司财务风险的识别框架 3
(三) 数据驱动的风险管理理论支撑 3
三、基于大数据的财务风险评估模型构建 4
(一) 财务风险评估的关键指标体系 4
(二) 大数据分析方法的选择与优化 4
(三) 风险评估模型的设计与验证 5
四、实证研究与案例分析 6
(一) 样本选择与数据来源 6
(二) 模型应用效果评估 6
(三) 典型上市公司案例剖析 7
结 论 8
参考文献 9
