Time series prediction system of stock price using multi-branch neural networks

Takashi Yamashita; Kotaro Hirasawa; Jinglu Hu

Time series prediction system of stock price using multi-branch neural networks

Takashi Yamashita, Kotaro Hirasawa, Jinglu Hu

Graduate School of Information, Production and Systems

Research output: Contribution to conference › Paper › peer-review

Abstract

Recently, artificial neural networks have been utilized for financial market applications. We have so far shown that multi-branch neural networks (MBNNs) could have higher representation and generalization ability. In this paper, a prediction system of a stock price using MBNNs is proposed. The result of our simulations shows that the proposed system has better accuracy than a system using conventional NNs.

Original language	English
Pages	2057-2062
Number of pages	6
Publication status	Published - 2005 Dec 1
Event	SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10

Conference

Conference	SICE Annual Conference 2005
Country	Japan
City	Okayama
Period	05/8/8 → 05/8/10

Keywords

Multi-branch
Neural networks
Stock price prediction

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

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title = "Time series prediction system of stock price using multi-branch neural networks",

abstract = "Recently, artificial neural networks have been utilized for financial market applications. We have so far shown that multi-branch neural networks (MBNNs) could have higher representation and generalization ability. In this paper, a prediction system of a stock price using MBNNs is proposed. The result of our simulations shows that the proposed system has better accuracy than a system using conventional NNs.",

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