Integrating Ensemble of Intelligent Systems for Modeling Stock Indices

Abraham, Ajith; Auyeung, Andy

doi:10.1007/3-540-44869-1_98

Cited by 12 publications

(10 citation statements)

References 4 publications

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“…Therefore, some different learning strategies such as combined/ensemble learning and meta-learning have been presented. For example, Abraham and AuYeung [20] present two ensemble approaches: based on a direct error measure and based on an evolutionary algorithm to search the optimal linear combination. Experimental results reveal that the ensemble techniques perform better than the individual methods and the direct ensemble approach seems to work well for the problem considered.…”

Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2013

IJCIS

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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 nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2013

IJCIS

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“…The generalized comparison made by Abraham and Yeung [4], Härdle [5], Sapankevych and Sangar [6], Khandani [7], Öğüt [8] and Papadimitriou [9] confirmed that the SVR is a state-of-art technique in exchange rate forecasting. Lam [10] pointed out that machine learning methods regarded numeric data directly as input, which made it contain more information than classical methodologies.…”

Section: Introductionmentioning

confidence: 77%

Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction

Feng¹,

Wu²

2016

Proceedings of the 2016 International Conference on Engineering Science and Management

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“…The SVR is considered a state-of-the-art forecasting methodology; see Sapankevych and Sangar (2009) for a generalized comparison and Abraham and Yeung (2003) for a comparison specifically in exchange rate forecasting. The SVR is a direct extension of the classic support vector machines algorithm (Cortes and Vapnik, 1995).…”

Section: Forecasting: Support Vector Regression (Svr)mentioning

confidence: 99%

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

Plakandaras

Papadimitriou

Gogas

2015

Journal of Forecasting

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In this paper we propose and test a forecasting model on monthly and daily spot prices of five selected exchange rates. In doing so, we combine a novel smoothing technique (initially applied in signal processing) with a variable selection methodology and two regression estimation methodologies from the field of machine learning (ML). After the decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method into a smoothed and a fluctuation component, multivariate adaptive regression splines (MARS) are used to select the most appropriate variable set from a large set of explanatory variables that we collected. The selected variables are then fed into two distinctive support vector machines (SVR) models that produce one‐period‐ahead forecasts for the two components. Neural networks (NN) are also considered as an alternative to SVR. The sum of the two forecast components is the final forecast of the proposed scheme. We show that the above implementation exhibits a superior in‐sample and out‐of‐sample forecasting ability when compared to alternative forecasting models. The empirical results provide evidence against the efficient market hypothesis for the selected foreign exchange markets. Copyright © 2015 John Wiley & Sons, Ltd.

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Integrating Ensemble of Intelligent Systems for Modeling Stock Indices

Cited by 12 publications

References 4 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

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