Using Data Mining Techniques to Build Enterprise Financial Crisis Prediction Model－by Combining Financial、Non-Financial and Intellectual Capital Indicators

• Main Authors: Ching-Huang Wang, 王景煌
• Other Authors: Wei-Ping Lee
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/21336584215654641706

碩士 === 中原大學 === 資訊管理研究所 === 94 === Abstract Financial statements reflect enterprise operating statuses. Investors can obtain complete information once formal financial statements are disclosed. However, if executives of firms intentionally embellish financial statements, investors cannot get the actual picture of the enterprise operation from the disclosed financial statements. With the advent of the era of “knowledge-based economy”, intangible assets, mainly relying on intelligence capital, have created competitive edges for enterprises. In previous literature, financial and non-financial information has been proved beneficial in predicting financial crises. Nevertheless, research applying intellectual capital indicators to foreshadow financial crises is still lacking. This research adopts neural networks, decision trees, support victor machine data mining techniques, as well as stepwise regression and factor analysis to establish a forecasting model. Additionally, it uses financial, non-financial, and intellectual capital indicators to predict corporate financial crises. 26 electronic corporations in financial crises and 26 corporations with stable financial status in the same industry have been chosen as samples. Furthermore, this research utilizes 32 financial indicators,12 non-financial indicators, and 16 intellectual capital indicators to construct a forecasting model of enterprise financial crisis. This research finds that the combination of financial, non-financial, and intellectual capital indicators has efficaciously enhanced the accuracy of the forecasting model, which is higher than that of the financial forecasting models established in previous studies. In addition, this research shows that the accuracy of the forecasting model based on the support vector machine surpasses the accuracy of other 4 prediction methods.