Improving the Accuracy of Stock Price Prediction using Ensemble Neural Network.

Phang Wai San, and Tan Li Im, and Patricia Anthony, and Chin Kim On, (2018) Improving the Accuracy of Stock Price Prediction using Ensemble Neural Network. Advanced Science Letters, 24 (2). pp. 1524-1527. ISSN 1936-6612

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Abstract

This paper describes performance of different classifiers (established/combinations/new prediction methods) that are used in predicting stock price. Artificial Neural Network (ANN) was chosen as the target candidates for the forecasting model in this work because of its ability to solve complex problems such as the stock price prediction. We experimented three types of neural network namely Feed Forward Neural Network (FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network (JRNN) and compared their predictions’ accuracy. We then designed an ensemble neural network that combined FFNN, JRNN and ERNN using bagging method to build a more accurate predictive model. Based on the results obtained, our proposed ENN outperformed the other ANNs by achieving the highest prediction’s accuracy.

Item Type: Article
Uncontrolled Keywords: Elman Recurrent Neural Network; Ensemble Neural Network; Jordan Recurrent Neural Network; Stock Price Prediction
Subjects: H Social Sciences > HG Finance
Divisions: FACULTY > Faculty of Engineering
Depositing User: MR OTHMAN HJ RAWI
Date Deposited: 24 Jun 2019 00:28
Last Modified: 24 Jun 2019 00:28
URI: http://eprints.ums.edu.my/id/eprint/22294

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