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Title: Statistical Analysis of Genetic Algorithms in Discovering Technical Trading Strategies
Authors: 陳樹衡
Date: 2004
Issue Date: 2010-11-24 22:04:12 (UTC+8)
Abstract: 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
Data Type: article
Appears in Collections:[經濟學系] 期刊論文

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