Using Data Mining Approach to Explore Financial Statement Restatement Company's Characteristics

• Main Authors: Jia-Hui Lin, 林佳慧
• Other Authors: Shaio-Yen Huang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/6sbt4r

碩士 === 國立中正大學 === 會計與資訊科技研究所 === 102 === In this study, we focus on domestic companies haing restated financial restatements, using of data mining technology with multi-angle. We intend to find the company’s characteristics of the financial statement restatement. Researching sample includes companies listed in Taiwan market come from 2002 to 2012. In order to improve the accuracy in the cluster analysis, we use three kind of clustering algorithms (K-means, Two-step, SOM). To verify the accuracy of model, the sample data will be divided into the data from 2002 to 2006 as a training set to build model, and from 2007 to 2012 as a testing period to verify the stability of model. After finding the optimal clustering model, all samples will be put into the optimal clustering model. Further, the results of clustering were analyzed by decision tree analysis to find the characteristics of firms with financial statement restatement. We find that, the optimal clustering model is K-Means method divided into three groups. In the three group, decision tree analysis found that firms with financial restatement has the follow characteristics: (1) accountants issued that ability of company continuing operating is doubtful, (2) non-big four CPA firm, (3) lower return on equity. When the company has displayed one of these three characteristics, the higher the likelihood of restatement of financial statements Keywords: Data Mining, Clustering, Decision trees, Restated Financial Statements, Company Characteristics, Corporate Governance, Financial Indicators, Audit Quality, Enterprise Risk Management, Earnings Management