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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/36391
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/36391


    Title: 遺傳演算法投資策略在動態環境下的統計分析
    The Statistical Analysis of GAs-Based Trading Strategies under Dynamic Landscape
    Authors: 棗厥庸
    Tsao, Chueh-Yung
    Contributors: 吳柏林
    陳樹衡

    Wu, Berlin
    Chen, Shu-Heng

    棗厥庸
    Tsao, Chueh-Yung
    Keywords: 遺傳演算法
    投資策略
    時間數列模型
    Tick-by-tick資料
    蒙地卡羅模擬
    夏普級數
    幸運係數
    Genetic Algorithms
    Trading Strategies
    Time Series Models
    Tick-by-tick Data
    Monte Carlo Simulation
    Sharpe Ratio
    Luck Coefficient
    Date: 1998
    Issue Date: 2009-09-18 18:27:57 (UTC+8)
    Abstract: 本研究中,我們計算OGA演化投資策略在五類時間數列模型上之表現,這五類模型分別是線性模型、雙線性模型、自迴歸條件異質變異數模型、門檻模型以及混沌模型。我們選擇獲勝機率、累積報酬率、夏普比例以及幸運係數做為評斷表現之準則,並分別推導出其漸近統計檢定。有別於一般計算智慧在財務工程上之應用,利用蒙地卡羅模擬法,研究中將對各表現準則提出嚴格之統計檢定結果。同時在實証研究中,我們考慮歐元兌美元及美元兌日圓的tick-by-tick匯率資料。故本研究主要的重點之一,乃是對於OGA演化投資策略,於這些模擬及實証資料上的有效性應用,作了深入且廣泛的探討。
    In this study, the performance of ordinary GA-based trading strategies are evaluated under five classes of time series model, namely, linear ARMA model, bilinear model, ARCH model, threshold model and chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. We then provide the asymptotic statistical tests for these criteria. Unlike many existing applications of computational intelligence in financial engineering, for each performance criterion, we provide a rigorous statistical results based on Monte Carlo simulation. In the empirical study, two tick-by-tick foreign exchange rates are also considered, namely, EUR/USD and USD/JPY. As a result, this study provides us
    with a thorough understanding about the effectiveness of ordinary GA for evolving trading strategies under these artificial and natural time series data.
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    Description: 碩士
    國立政治大學
    應用數學研究所
    85751002
    87
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002001686
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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