Application of multi-branch neural networks to stock market prediction

Takashi Yamashita, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages2544-2548
Number of pages5
DOIs
Publication statusPublished - 2005 Dec 1
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 2005 Jul 312005 Aug 4

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume4

Conference

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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Application of multi-branch neural networks to stock market prediction'. Together they form a unique fingerprint.

  • Cite this

    Yamashita, T., Hirasawa, K., & Hu, J. (2005). Application of multi-branch neural networks to stock market prediction. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 2544-2548). [1556303] (Proceedings of the International Joint Conference on Neural Networks; Vol. 4). https://doi.org/10.1109/IJCNN.2005.1556303