本論文是研究在台灣開放型的股票型共同基金，並且利用資料採掘的技術加以分析並分類所謂優異績效及劣質績效的共同基金。我們使用分類決策樹(Classification and regression trees, CART)的方法來進行共同基金績效的分析及預測。本篇論文，我們採用了13種重要的變數來建構樹並找出優質基金，此外更驗證CART對於我們進行台灣共同基金績效的分析是穩定且有效的。最後，我們利用cross-validation test進行兩個月的基金的選取及持有，並各透過一個月的持有來視其績效。我們特別發現利用此方法選取出來的基金，其平均績效將優於所有共同基金的績效，並且其中有一個月的平均報酬率高於僅投資於高科技股的共同基金平均報酬率。

We study the performance of open-end mutual funds in Taiwan, and use the technology of data mining to classify the outperforming and underperforming mutual funds. Classification and regression tree (CART) is the method to evaluate and predict the performance of mutual funds. In this paper, we utilize thirteen crucial factors to build trees and pick mutual funds by its classification rules. Moreover, we will verify precision of each tree. We find that the CART is a good tool to evaluate the performance of mutual funds in Taiwan because of its stability in outperforming - underperforming spreads. Moreover, we use two kinds of learning sample to build two trees and pick mutual funds to compose of them into the fund of funds. The results are better than the total average returns monthly, and one of them is better than the mutual funds that its investing target is high-tech stocks.

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