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This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
We propose a hybrid approach of support vector regression, genetic algorithm, and seasonal moving window to explore seasonality effect for the stock indexes in three developed and one emerging markets using daily prices from 1996 to 2005. First, we utilize genetic algorithm to locate the approximate optimal combination of technical indicators. Then the property of nonlinearity and high dimensionality...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
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