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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/35163


    Title: 分析共同基金績效-使用資料採掘技術
    Evaluating the Performance of Mutual Funds— Using the Technology of Data Mining
    Authors: 謝明倫
    Contributors: 郭維裕
    謝明倫
    Keywords: 共同基金績效
    資料採掘
    分類決策樹
    CART
    mutual fund
    performance
    Date: 2006
    Issue Date: 2009-09-18 14:19:06 (UTC+8)
    Abstract: 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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    韋端、鄭宇庭、鄧家駒、匡宏波、謝邦昌,2003,Data Mining 概述—以Clementine 7.0 為例,中華資料採礦協會。
    Description: 碩士
    國立政治大學
    國際經營與貿易研究所
    94351018
    95
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0943510181
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

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