2014
DOI: 10.1080/18756891.2013.864472
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Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Abstract: Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based non… Show more

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Cited by 35 publications
(23 citation statements)
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“…The result showed the performance of the proposed method was better than LS and MMSE methods in all the mutation and crossover values and in all the iterations computed. Xiao et al [115] constructed three different types of neural network based models, that is, Elman network, generalized regression neural network (GRNN), and wavelet neural network (WNN) constructed by three nonoverlapping training sets. Their empirical results suggested the ensemble ANNs-PSO-GA approach significantly improved the prediction performance over other individual models and linear combination models.…”
Section: Fpsomentioning
confidence: 99%
“…The result showed the performance of the proposed method was better than LS and MMSE methods in all the mutation and crossover values and in all the iterations computed. Xiao et al [115] constructed three different types of neural network based models, that is, Elman network, generalized regression neural network (GRNN), and wavelet neural network (WNN) constructed by three nonoverlapping training sets. Their empirical results suggested the ensemble ANNs-PSO-GA approach significantly improved the prediction performance over other individual models and linear combination models.…”
Section: Fpsomentioning
confidence: 99%
“…Aiming at the above problems, this paper proposed that Elman be optimized by the particle swarm optimization (PSO) algorithm, which taps PSO into training weights and self feedback gain factors in Elman. PSO was proposed by Kennedy and Eberhart in 1995 [32]. The optimization procedure of specific flow chart is shown in Fig.…”
Section: Discriminant Analysismentioning
confidence: 99%
“…More recently, many studies have demonstrated that artificial intelligence such as decision trees, neural networks, and support vector machine can be alternative methods for financial distress prediction [12][13][14]. Moreover, there are various studies on the comparison between statistical and machine learning methods in terms of their ability to predict financial data [15][16][17][18].…”
Section: Introductionmentioning
confidence: 99%