- 學位論文
結合DEA與資料縮減方法建立基金選購決策樹模型
Applying DEA and data reduction techniques to building decision trees for mutual fund selection
摘要
由於台灣金融市場越來越成熟，共同基金也受到大眾的青睞，越來越多投資人把它當成一種理財工具。雖然共同基金管理方便又容易變現，但相對的受到風險的影響，要如何挑選績效良好的共同基金，勢必為一個重要的議題。由於金融市場波動性很高，為了使投資人在選購基金上能夠更便利，透過投資組合提高收益的穩定性減少投資風險，本研究利用三階段的資料縮減(data reduction)技術結合資料包絡分析法(DEA)建立一套決策模型，來協助投資人獲利。本研究的範疇來自為國內外四大種類基金股票、生技醫療、能源與債券等四個領域，樣本期間從2006年6月到2009年2月。第一階段以等量劃分法等概念進行資料前處理，第二階段透過DEA結合決策樹模型去評估基金績效，第三階段加入三種資料縮減方法建構決策樹模型並與原始資料進行比較。在進一步分析後，發現三階段資料縮減的三種方法無法改善二階段資料縮減方法，而在三階段資料縮減中，使用最多屬性的主成分分析法搭配較多區間劃分數，可得到最高的準確率，但因素組合選取方法之平均準確率變異數最小且有中上的準確率水準。在空頭期間下，三階段資料縮減的封裝型特徵選取(wrapper)方法在五等量區間、六等量區間即七等量區間選取的屬性較少，但其準確率卻高於其他二個方法，甚至超越了第二階段資料縮減的TAL方法，表示屬性少的資料集搭配較多區間劃分數，有可能找到一個適用的範圍來建立準確率高的決策樹模型。
並列摘要
Mutual funds are popular in the matured financial market of Taiwan. Many investors use it as a financial instrument because mutual funds management is not complicated and liquidity preference. However, with imposed risk, selecting a good mutual fund is always an important issue. As the financial market volatility is high, investors must avoid the risk through the investment portfolio to improve the stability. This study used a three-stage of data reduction combined with data envelopment analysis (DEA) to build decision tree models to help investors gain profits. Using data preprocessing for sample classification is the first stage, and then DEA is used to evaluate mutual funds performance with a single criterion, namely, the efficiency of a decision making unit.. The third stage of data reduction is proceeded using three data reduction methods, that is, PCA, wrap and common factors. All reduction data set are used to build decision tree models and compared with that of original raw data. The study finds that in the bull market size of data set will affect the accuracy of the decision tree model. The decision tree model built with original raw data set has the highest accuracy. For the bear market wrapper select less attributes in the five equal interval discretion case. The accuracy of tree model derived from wrapper is higher than those from the other two methods, even outperforms the TAL method. It means a decision tree with high accuracy is possible with a reduced data set through the proposed data reduction techniques.