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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 24-Nov-2010 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 dc.creator (作者) 陳樹衡 zh_TW dc.creator (作者) Chen,Shu-Heng;Tsao,Chueh-Yung - dc.date (日期) 2004 - dc.date.accessioned 24-Nov-2010 22:04:12 (UTC+8) - dc.date.available 24-Nov-2010 22:04:12 (UTC+8) - dc.date.issued (上傳時間) 24-Nov-2010 22:04:12 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/48587 - dc.description.abstract (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. - dc.language zh_TW en dc.language.iso en_US - dc.relation (關聯) Advances in Econometrics,19,1-43 en dc.title (題名) Statistical Analysis of Genetic Algorithms in Discovering Technical Trading Strategies en dc.type (資料類型) article en
