摘要
财务报表质量评估是会计信息质量评价的重要组成部分，对投资者决策、市场监管及企业内部管理具有重要意义。本研究旨在构建一个全面的财务报表质量评估框架，以提高财务信息透明度和可靠性。通过对国内外相关文献的系统梳理，结合我国资本市场特点，选取了包括盈余质量、资产质量、现金流质量等多维度指标体系，并引入机器学习算法进行综合评估。研究采用2015 - 2020年沪深两市A股上市公司为样本，运用因子分析法提取关键特征变量，通过Logistic回归模型检验各因素对企业财务风险预警能力的影响。实证结果表明，所构建的评估体系能够有效识别高风险企业，且在预测精度方面优于传统方法。特别是将文本挖掘技术应用于年报非结构化数据处理，显著提升了模型解释力。研究表明，财务报表质量不仅受会计政策选择影响，还与公司治理水平密切相关，良好的内部控制机制有助于提升财务信息质量。该研究创新性地融合了定量与定性分析手段，为监管机构提供了科学依据，也为投资者判断企业价值提供了新视角。
关键词：财务报表质量评估；机器学习算法；因子分析
Abstract
The evaluation of financial statement quality is an essential component of accounting information quality assessment, playing a significant role in investor decision-making, market regulation, and corporate internal management. This study aims to construct a comprehensive fr amework for assessing financial statement quality to enhance the transparency and reliability of financial information. By systematically reviewing relevant literature both domestically and internationally, and considering the characteristics of China's capital market, this research selects a multi-dimensional indicator system including earnings quality, asset quality, and cash flow quality, while incorporating machine learning algorithms for integrated evaluation. The study employs A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2015 to 2020 as samples, using factor analysis to extract key feature variables and logistic regression models to examine the impact of various factors on corporate financial risk warning capabilities. Empirical results indicate that the constructed evaluation system can effectively identify high-risk enterprises and outperforms traditional methods in terms of predictive accuracy. Notably, applying text mining techniques to the processing of unstructured data in annual reports significantly enhances model interpretability. The research reveals that financial statement quality is influenced not only by accounting policy choices but also closely related to corporate governance levels, with effective internal control mechanisms contributing positively to financial information quality. Innovatively integrating quantitative and qualitative analytical approaches, this study provides scientific evidence for regulatory authorities and offers new perspectives for investors in evaluating corporate value.
Keywords：Financial Statement Quality Evaluation; Machine Learning Algorithm; Factor Analysis
目 录
摘要 I
Abstract II
一、绪论 1
(一) 财务报表质量评估的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与创新点 2
二、财务报表质量评估的理论基础 2
(一) 财务报表质量的概念界定 2
(二) 财务报表质量的影响因素分析 3
(三) 财务报表质量评估的主要理论 4
三、财务报表质量评估的方法体系 4
(一) 定量评估方法的应用 4
(二) 定性评估方法的构建 5
(三) 混合评估方法的实践 6
四、财务报表质量评估的实证研究 7
(一) 实证研究的设计与数据来源 7
(二) 实证结果分析与讨论 7
(三) 研究结论与政策建议 8
结 论 9
参考文献 10
财务报表质量评估是会计信息质量评价的重要组成部分，对投资者决策、市场监管及企业内部管理具有重要意义。本研究旨在构建一个全面的财务报表质量评估框架，以提高财务信息透明度和可靠性。通过对国内外相关文献的系统梳理，结合我国资本市场特点，选取了包括盈余质量、资产质量、现金流质量等多维度指标体系，并引入机器学习算法进行综合评估。研究采用2015 - 2020年沪深两市A股上市公司为样本，运用因子分析法提取关键特征变量，通过Logistic回归模型检验各因素对企业财务风险预警能力的影响。实证结果表明，所构建的评估体系能够有效识别高风险企业，且在预测精度方面优于传统方法。特别是将文本挖掘技术应用于年报非结构化数据处理，显著提升了模型解释力。研究表明，财务报表质量不仅受会计政策选择影响，还与公司治理水平密切相关，良好的内部控制机制有助于提升财务信息质量。该研究创新性地融合了定量与定性分析手段，为监管机构提供了科学依据，也为投资者判断企业价值提供了新视角。
关键词：财务报表质量评估；机器学习算法；因子分析
Abstract
The evaluation of financial statement quality is an essential component of accounting information quality assessment, playing a significant role in investor decision-making, market regulation, and corporate internal management. This study aims to construct a comprehensive fr amework for assessing financial statement quality to enhance the transparency and reliability of financial information. By systematically reviewing relevant literature both domestically and internationally, and considering the characteristics of China's capital market, this research selects a multi-dimensional indicator system including earnings quality, asset quality, and cash flow quality, while incorporating machine learning algorithms for integrated evaluation. The study employs A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2015 to 2020 as samples, using factor analysis to extract key feature variables and logistic regression models to examine the impact of various factors on corporate financial risk warning capabilities. Empirical results indicate that the constructed evaluation system can effectively identify high-risk enterprises and outperforms traditional methods in terms of predictive accuracy. Notably, applying text mining techniques to the processing of unstructured data in annual reports significantly enhances model interpretability. The research reveals that financial statement quality is influenced not only by accounting policy choices but also closely related to corporate governance levels, with effective internal control mechanisms contributing positively to financial information quality. Innovatively integrating quantitative and qualitative analytical approaches, this study provides scientific evidence for regulatory authorities and offers new perspectives for investors in evaluating corporate value.
Keywords：Financial Statement Quality Evaluation; Machine Learning Algorithm; Factor Analysis
目 录
摘要 I
Abstract II
一、绪论 1
(一) 财务报表质量评估的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与创新点 2
二、财务报表质量评估的理论基础 2
(一) 财务报表质量的概念界定 2
(二) 财务报表质量的影响因素分析 3
(三) 财务报表质量评估的主要理论 4
三、财务报表质量评估的方法体系 4
(一) 定量评估方法的应用 4
(二) 定性评估方法的构建 5
(三) 混合评估方法的实践 6
四、财务报表质量评估的实证研究 7
(一) 实证研究的设计与数据来源 7
(二) 实证结果分析与讨论 7
(三) 研究结论与政策建议 8
结 论 9
参考文献 10
