資料探勘於評估股權投資項目應用之研究

股權投資具有高收益、高風險的特點，如何規避主觀判斷的失誤、客觀合理地評估投資項目，是降低投資風險的關鍵。然而現實業界中企業數量繁多，投資機構在面對如此眾多之企業項目時，往往在花費大量人力物力之後，仍難以有效的篩選出初步候選項目，致使後期的深入調研缺乏可靠的前期基礎。因此，本研究將資料探勘引入投資項目評估模型之中，旨在於通過資料探勘的方式，篩選出「有投資價值」的企業，以利投資機構快速得到初步的候選名單，做為資料蒐集與盡職調研的基礎。為使評估篩選方式更為客觀，本文整理出過去股權投資項目之評估指標，並以實際可得之資料取值代替專家評分構建指標體系，從大量企業中篩選出有投資價值的備選企業。同時，本文採用資料探勘中的分類演算法並針對不平衡資料的問題加以處理，再利用屬性選擇的方法來降低資料維度，建立出嚴謹而客觀的投資項目評估模型。

High yield and high risk is the nature characteristic for most equity investments. While decisions are mainly made by human minds, it is crucial to apply decision models in investment evaluation period. However, due to the highly increased number of enterprises and low quality of their information, investors always found them jumping from one set of information silos into another. Thus, in this paper, we introduce data mining techniques into investment evaluation task in order to help those investors by quantitatively analyzing normal enterprises and choosing those which are most likely to be invested. Previous studies mainly focus on the categories of evaluation indexes, which are given by expert analysis. Our study extends it by specifying evaluation index with concrete measurements from real enterprises, and weighting them with feature selection techniques rather than subjective judgments. To handle the real data set, imbalanced data distribution is also considered in this study. Finally, a precise and objective evaluation model is built.