Application of multi-branch neural networks to stock market prediction

Takashi Yamashita*, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

Recently, artificial neural networks (ANNs) have been utilized for financial market applications. On the other hand, we have so far shown that multi-branch neural networks (MBNNs) could have higher representation and generalization ability than conventional NNs. In this paper, we investigate the accuracy of prediction of TOPIX (Tokyo Stock Exchange Prices Indexes) using MBNNs. Using the TOPIX related values in time series and other information, MBNNs can learn the characteristics of time series and predict the TOPIX values of the next day. Several simulations were carried out in order to compare the proposed predictor using MBNNs with that using conventional NNs. The results show that the proposed method can have higher accuracy of the prediction.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
ページ2544-2548
ページ数5
DOI
出版ステータスPublished - 2005 12 1
イベントInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
継続期間: 2005 7 312005 8 4

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
4

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
国/地域Canada
CityMontreal, QC
Period05/7/3105/8/4

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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