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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/68269

    Title: 基於EEMD與類神經網路預測方法進行台股投資組合交易策略
    Portfolio of stocks trading by using EEMD-based neural network learning paradigms
    Authors: 賴昱君
    Lai, Yu Chun
    Contributors: 蕭又新
    Shiau, Yuo Hsien
    Lai, Yu Chun
    Keywords: 類神經網路
    Ensemble Empirical Mode Decomposition
    forecasting model
    Artificial Neural Network
    trading strategy
    Date: 2013
    Issue Date: 2014-08-06 11:47:34 (UTC+8)
    Abstract: 對投資者而言,投資股市的目的就是賺錢,但影響股價因素眾多,我們要如何判斷明天是漲是跌?因此如何建立一個準確的預測模型,一直是財務市場研究的課題之一,然而財務市場一直被認為是一個複雜.充滿不確定性及非線性的動態系統,這也是在建構模型上一個很大的阻礙,本篇研究中使用的EEMD方法則適合解決如金融市場或氣候等此類的非線性問題及有趨勢性的資料上。
    結果顯示,EEMD-ANN Model 1是一個穩健的模型,能夠創造出將近20%的年報酬率,其次為EEMD-ANN Model 2,在搭配移動平均線的策略後,表現與Model 1差不多,但在沒有配合移動平均線策略時,雖報酬率仍為正,但較不穩定,因此從研究結果也可以看到,EEMD-ANN的模型皆表現比ARMA的預測模型好。
    The main purpose of investing is to earn profits for an investor, but there are many factors that can influence stock price. Investments want to know the price will rise or fall tomorrow. Therefore, how to establish an accurate forecasting model is one of the important issue that researched by researchers of financial market. However, the financial market is considered of a complex, uncertainty, and non-linear dynamic systems. These characteristics are obstacles on constructing model. The measure, EEMD, used in this study is suitable to solve questions that are non-linear but have trends such as financial market, climate and so on.
    In this thesis, we used three models including ARMA model and two types of EEMD-ANN composite models to forecast the stock price. In addition, we tried to improve ARMA model, so a new model was proposed. Through EEMD, the fluctuation of stock price can be decomposed into several IMFs with different economical meanings. Moreover, we adopted portfolio approach to spread risks. We integrate the static weight and the dynamic weight to decide the optimal weights. Also, we added the moving average indicator to our trading strategy. The subject matters in this study are 10 attention stocks.
    Our results showed that EEMD-ANN Model 1 is a robust model. It is not only the best model but also can produce near 20% of 1-year return ratio. We also find that our EEMD-ANN model have better outcome than those of the traditional ARMA model. Owing to that, the increases of trading performance would be expected via the selected EEMD-ANN model.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1017550031
    Data Type: thesis
    Appears in Collections:[應用物理研究所 ] 學位論文

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