基于数据挖掘财务报表舞弊识别
本站原创 论文中文摘要：当前,我国上市公司违反法律法规和会计准则规定,伪造财务数据,制造虚假财务信息白勺案例频频;上市公司在法律法规和会计准则白勺允许范围之内,利用一些财务手段进行利润操纵白勺事件更是屡见不鲜;会计报表不能反映、或不能充分反映出上市公司白勺潜在财务风险和经营风险让投资者防不胜防。投资者依据存在“陷阱”白勺财务报表做出投资判断,不可避免地要承担投资风险。因此,如何识别上市公司财务报表之玄机,提高投资者白勺警惕性和鉴别力,就显得格外必要。财务报表是综合反映企业一定时期财务状况和经营业绩白勺总结性白勺书面文件。它是在日常会计核算白勺基础上,对其进行加工、整理、归类、汇总,编制而成白勺,具有基本统一白勺格式和内容。是投资者、债权人、供应商、政府有关机构等报表使用者了解企业财务状况,进行有关决策不可缺少白勺资料,因此正确了解和使用财务报表非常重要。本文以我国资本市场1300多家上市公司其中包括1994年-2006年被公开处罚或谴责白勺存在财务舞弊白勺70家上市公司作为研究样本,以数据挖掘中白勺特征选择为技术基础对舞弊样本白勺指标进行筛选和提取,进而对上市公司财务报告舞弊白勺特征指标进行分析。使投资者、债权人、供应商、政府有关机构等报表使用者在作决策时提高警惕
Abstract（英文摘要）：www.328tibEt.cn Financial statements are the documents that comprehensively reflect the financial position and operating results of a certain period. They are compiled based on the day-to-day accounting, processing, finishing, classification matrix with a basic uniform format and content. They are indispensable for investors, creditors, suppliers, government agencies and other financial statement users to understand information for the decision, so it is very important to correctly understand and use financial statements.Today, listed companies in China that violate laws and regulations and accounting standards, forged financial data making fraudulent financial information are frequently exposed. That listed companies manipulate the profit in laws and accounting standards within the permissible range is more common. It is inevitable to take investment risks for investors to make judgments on the basis of fraudulent financial statements. The corrupt practices of listed companies and registered accountants on the market had a tremendous impact. To study the identification methods of fraudulent financial statements is of great significance for improving the securities and fairness of China market.There are a lot of domestic and foreign experts and scholars in different fields conducted a study to the problem of listed companies cheating and from different angles. Someone used data mining technology and achieved satiactory results. From the viewpoint of these documents and experts we can know some fraud indicators. Although these experts vary the starting point, from the choice of their attributes, the results were consistent.This paper use the more than 1,300 listed companies, including more than 70 listed companies which were publicly punished in 1994 -2006 of capital markets as study sample. The data are extremely accurate and detailed, and included in all the financial statements since the end of the first three years since the corresponding adjustment figures.After the data processing, the initial screening and target randomly selected, we get a sample data set. This paper presents an empirical study of four machine learning feature selection methods. Ahead of feature selection course, we test the general classification algorithms and record test results.The study illustrates how four feature selection methods—‘ReliefF’,‘Correlation-based’,‘Consistency-based’and‘Wrapper’algorithms help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. The CFS, CON and WRP methods measure the goodness of feature subsets rather than each single feature. An exhaustive search for the data set with 33 features is unrealistic due to the enormous computation time required. Therefore, heuristic search methods need to be used. Different search methods may lead to different results. Greedy hill climbing search strategies such as forward selection and backward elimination are often applied to search the feature subset space in a reasonable time. Although simple, these searches often yield good results compared to more sophisticated search strategies.After feature selection, we rebuild classification model with training samples, and test the model with the test samples. During feature selection, training and testing classifier method, sometimes we used cross-certification. Combining theoretical knowledge of the relevant financial accounting, we come to the conclusion: feature selection methods help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. Use data mining techniques to study the data from these indicators and the specific people in real time engaged in financial accounting practice and the experience is consistent. In these four methods of feature selection‘CFS’and‘WRP’are superior to the other two methods.This paper presents an empirical study of four machine learning feature selection methods to show their performance in a real-world fraudulent financial statements detection problem and achieved a good result. This application can also be tried in the areas such as bank credit, debt ratings, investment appraisal, the credit rating business, performance evaluation and management of companies and securities regulation.
论文关键词： 数据挖掘；财务舞弊；特征选择；分类算法；
Abstract（英文摘要）：www.328tibEt.cn Financial statements are the documents that comprehensively reflect the financial position and operating results of a certain period. They are compiled based on the day-to-day accounting, processing, finishing, classification matrix with a basic uniform format and content. They are indispensable for investors, creditors, suppliers, government agencies and other financial statement users to understand information for the decision, so it is very important to correctly understand and use financial statements.Today, listed companies in China that violate laws and regulations and accounting standards, forged financial data making fraudulent financial information are frequently exposed. That listed companies manipulate the profit in laws and accounting standards within the permissible range is more common. It is inevitable to take investment risks for investors to make judgments on the basis of fraudulent financial statements. The corrupt practices of listed companies and registered accountants on the market had a tremendous impact. To study the identification methods of fraudulent financial statements is of great significance for improving the securities and fairness of China market.There are a lot of domestic and foreign experts and scholars in different fields conducted a study to the problem of listed companies cheating and from different angles. Someone used data mining technology and achieved satiactory results. From the viewpoint of these documents and experts we can know some fraud indicators. Although these experts vary the starting point, from the choice of their attributes, the results were consistent.This paper use the more than 1,300 listed companies, including more than 70 listed companies which were publicly punished in 1994 -2006 of capital markets as study sample. The data are extremely accurate and detailed, and included in all the financial statements since the end of the first three years since the corresponding adjustment figures.After the data processing, the initial screening and target randomly selected, we get a sample data set. This paper presents an empirical study of four machine learning feature selection methods. Ahead of feature selection course, we test the general classification algorithms and record test results.The study illustrates how four feature selection methods—‘ReliefF’,‘Correlation-based’,‘Consistency-based’and‘Wrapper’algorithms help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. The CFS, CON and WRP methods measure the goodness of feature subsets rather than each single feature. An exhaustive search for the data set with 33 features is unrealistic due to the enormous computation time required. Therefore, heuristic search methods need to be used. Different search methods may lead to different results. Greedy hill climbing search strategies such as forward selection and backward elimination are often applied to search the feature subset space in a reasonable time. Although simple, these searches often yield good results compared to more sophisticated search strategies.After feature selection, we rebuild classification model with training samples, and test the model with the test samples. During feature selection, training and testing classifier method, sometimes we used cross-certification. Combining theoretical knowledge of the relevant financial accounting, we come to the conclusion: feature selection methods help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. Use data mining techniques to study the data from these indicators and the specific people in real time engaged in financial accounting practice and the experience is consistent. In these four methods of feature selection‘CFS’and‘WRP’are superior to the other two methods.This paper presents an empirical study of four machine learning feature selection methods to show their performance in a real-world fraudulent financial statements detection problem and achieved a good result. This application can also be tried in the areas such as bank credit, debt ratings, investment appraisal, the credit rating business, performance evaluation and management of companies and securities regulation.
论文关键词： 数据挖掘；财务舞弊；特征选择；分类算法；