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    題名: Statistical Analysis of Genetic Algorithms in Discovering Technical Trading Strategies
    作者: 陳樹衡
    Chen,Shu-Heng;Tsao,Chueh-Yung
    日期: 2004
    上傳時間: 2010-11-24 22:04:12 (UTC+8)
    摘要: In this study, the performance of ordinal GA-based trading strategies is
    evaluated under six classes of time series model, namely, the linear ARMA
    model, the bilinear model, the ARCH model, the GARCH model, the
    threshold model and the chaotic model. The performance criteria employed
    are the winning probability, accumulated returns, Sharpe ratio and luck
    coefficient. Asymptotic test statistics for these criteria are derived. The
    hypothesis as to the superiority of GA over a benchmark, say, buy-and-hold,
    can then be tested using Monte Carlo simulation. From this rigorouslyestablished
    evaluation process, we find that simple genetic algorithms
    can work very well in linear stochastic environments, and that they also
    work very well in nonlinear deterministic (chaotic) environments. However,
    they may perform much worse in pure nonlinear stochastic cases. These
    results shed light on the superior performance of GA when it is applied
    to the two tick-by-tick time series of foreign exchange rates: EUR/USD
    and USD/JPY.
    關聯: Advances in Econometrics,19,1-43
    資料類型: article
    顯示於類別:[經濟學系] 期刊論文

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