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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94217

    Title: 遺失資料插補法在最適資產配置投資組合上之應用與比較 : 以臺灣證券市場為例
    Other Titles: Missing data imputation methods comparisons in optimal assets allocation : the empirical analysis in Taiwan equity markets
    Authors: 程于庭;Cheng, Yu-Ting
    Contributors: 淡江大學統計學系碩士班
    林志娟;Lin, Jyh-Jiuan
    Keywords: 資產配置;證券報酬預測模型;遺失資料;遺失資料插補方法;Mean-variance portfolio model;factor model;Missing data;imputation method
    Date: 2013
    Issue Date: 2014-01-23 14:10:50 (UTC+8)
    Abstract: 本研究主要探討遺失值插補方法之優劣,研究中首先將完整資料以隨機設定比例移除10%、30%、50%資料,並以不同插補方法將遺失值插補後所得資料與真實值比較,做不同遺失比例、不同插補方法之插補結果與真實值之誤差評估。接著,再將插補後的資料代入預期報酬率估計模型中,以不同模型所計算得到的預期報酬率、變異數與共變異數矩陣,並探討插補方法應用在預期報酬率估計模型上之預測績效表現。最後,將上述所計算得到的預期報酬率、變異數與共變異數矩陣,作為投入要素,求解而得最適資產配置投資組合,並以研究期間最後一個交易日之累績報酬率為指標,用以衡量插補方法在最適資產配置投資組合投資績效之好壞。

    Data missing is a prevail problem for most of the data analysis. This thesis mainly focuses on how the remedy strategies, data imputation, could affect the optimal assets allocation problems. At first, 10%, 30% and 50% data are removed artificially and randomly from a complete data set. Then four different methods, mean, EM, Regression and MCMC, are employed to impute the data respectively. Then the four imputed data sets are adopted by the optimal assets allocation problems by incorporating the input from three mean estimation models and two variance estimation models.

    Empirical evidence shows that EM method outperforms the rest imputation methods in terms of the accuracy. Although not as good as EM method, Regression method also performs well especially compare with MCMC method. Mean estimation is quite sensitive to the extreme data and hence is unstable and not recommended. Besides, the investment performance through the aforementioned four imputation methods in optimal assets allocation problems follow the same pattern in terms of the cumulative rate of return. It identifies the importance of the imputation methods in the optimal assets allocation problems application.
    Appears in Collections:[統計學系暨研究所] 學位論文

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