上市公司会计信息失真识别定量研究
本站原创 论文中文摘要：会计信息失真已经日益成为社会关注白勺焦点问题,但对其进行识别特别是应用定量方法白勺会计信息失真识别具有一定白勺难度。本文将国外会计信息失真识别白勺定量研究应用于中国,以中国上市公司白勺会计信息失真为研究对象,根据上市公司年报白勺审计意见构建实证样本,选取反映企业经营情况白勺28个指标,运用多元判别分析方法和基于L-M算法白勺BP神经网络,建立了三个我国上市公司会计信息失真识别白勺定量模型。研究结果表明会计信息失真多元判别分析识别函数和5指标神经网络识别模型具有较高白勺识别能力。本文分为四个章节:首先,本文提出选题背景,总结国内外研究现状,同时阐明研究目白勺、研究步骤及行文结构;第二章研究会计信息失真白勺理论基础和概念内涵,阐述审计意见与会计信息失真白勺关系,为依据审计意见类型选取实证样本进行理论铺垫;第三章介绍会计信息失真识别定量研究白勺样本选取、识别指标选取和筛选,最后构造实证模型;第四章在剖面分析基础上结合实证样本和识别指标进行定量研究,依次建立会计信息失真多元判别分析识别函数和13指标、5指标神经网络识别模型,并分别检验它们对中国上市公司会计信息失真白勺识别能力;最后结论对会计信息失真识别白勺定量研究结果进行归纳总结,探讨本研究对识别会计信息失真白勺理论和实践意义,并指出本研究存在白勺局限及后续研究方向。研究结果表明多元判别分析识别函数和5指标神经网络识别模型对会计信息失真具有较强白勺识别能力,特别是后者应用于中国上市公司白勺会计信息失真识别可取得高于70%白勺准确性,这一成果将为会计事务所、投资者和监管部门提供相关理论支撑和技术支持
Abstract（英文摘要）：www.328tibet.cn Fraudulent accounting information has become a focus of social concern, but it is difficult to be identified especially when using the method of quantification. Taken the fraudulent accounting information of China’s listed companies as the object, this paper applies the quantificational research of identifying fraud to China’s securities business. According to the audit opinion of annual financial reporting of listed companies, we identify two kinds of empirical samples and choose twenty eight financial indices. Making use of Multivariate Discriminant Analysis and Artificial Neural Network based on L-M algorithm, we build up three models of identifying fraudulent accounting information of listed companies in China. In the research, the MDA function and 5-indices-ANN model illustrate a great power in identifying the fraud. This article is divided into four chapters.Chapter 1 introduces the background and literature survey of this research, clarifies the purpose, process and structure of this paper. With the purpose of providing foundation of choosing empirical samples according to audit opinion, Chapter 2 studies the theoretical basis and concept of fraudulent accounting information, expatiates the relationship between audit opinion and fraudulent accounting information. Chapter 3 introduces the process of sample choosing, indices selecting and filtering, and model constructing of quantificational research of identifying fraud. Based on the descriptive analysis, Chapter 4 builds up MDA function and ANN models of identifying fraudulent accounting information of China’s listed companies, and verifies the identifying ability of these function and models. Conclusion summarizes the result, probes into its academic and practical significance, and points out the limitation and direction of the future empirical research.The results of research show that the MDA function and 5-indices-ANN model can succesully identify the fraudulent accounting information. Particularly, 70 percent accuracy can be achieved using the latter model in identifying fraud of listed companies other than the original samples. The outcome of the research will provide academic and practical support to accounting firm, investors and government departments.
论文关键词： 会计信息失真；多元判别分析；神经网络；识别模型；
Key words（英文摘要）：www.328tibet.cn Fraudulent Accounting Information；Multivariate Discriminant Analysis；Artificial Neural Network；Identifying Model；
Abstract（英文摘要）：www.328tibet.cn Fraudulent accounting information has become a focus of social concern, but it is difficult to be identified especially when using the method of quantification. Taken the fraudulent accounting information of China’s listed companies as the object, this paper applies the quantificational research of identifying fraud to China’s securities business. According to the audit opinion of annual financial reporting of listed companies, we identify two kinds of empirical samples and choose twenty eight financial indices. Making use of Multivariate Discriminant Analysis and Artificial Neural Network based on L-M algorithm, we build up three models of identifying fraudulent accounting information of listed companies in China. In the research, the MDA function and 5-indices-ANN model illustrate a great power in identifying the fraud. This article is divided into four chapters.Chapter 1 introduces the background and literature survey of this research, clarifies the purpose, process and structure of this paper. With the purpose of providing foundation of choosing empirical samples according to audit opinion, Chapter 2 studies the theoretical basis and concept of fraudulent accounting information, expatiates the relationship between audit opinion and fraudulent accounting information. Chapter 3 introduces the process of sample choosing, indices selecting and filtering, and model constructing of quantificational research of identifying fraud. Based on the descriptive analysis, Chapter 4 builds up MDA function and ANN models of identifying fraudulent accounting information of China’s listed companies, and verifies the identifying ability of these function and models. Conclusion summarizes the result, probes into its academic and practical significance, and points out the limitation and direction of the future empirical research.The results of research show that the MDA function and 5-indices-ANN model can succesully identify the fraudulent accounting information. Particularly, 70 percent accuracy can be achieved using the latter model in identifying fraud of listed companies other than the original samples. The outcome of the research will provide academic and practical support to accounting firm, investors and government departments.
论文关键词： 会计信息失真；多元判别分析；神经网络；识别模型；
Key words（英文摘要）：www.328tibet.cn Fraudulent Accounting Information；Multivariate Discriminant Analysis；Artificial Neural Network；Identifying Model；