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| Title | Statistical Analysis of Genetic Algorithms in Discovering Technical Trading Strategies |
| Creator | 陳樹衡 Chen,Shu-Heng;Tsao,Chueh-Yung |
| Date | 2004 |
| Date Issued | 24-Nov-2010 22:04:12 (UTC+8) |
| Summary | 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. |
| Relation | Advances in Econometrics,19,1-43 |
| Type | 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 |