• ISSN: 2301-3567 (Print)
    • Frequency: Quarterly (2013-2014); Monthly (2015-2017); Quarterly (Since 2018)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Mia Hu
    • Abstracting/ Indexing:  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, and Crossref.
    • E-mail: joebm@ejournal.net
JOEBM 2019 Vol.7(2): 60-64 ISSN: 2301-3567
DOI: 10.18178/joebm.2019.7.2.582

Predicting Direction of Individual Stock Price Movement Using a Hybrid Model

Cheng Li and Yu Song
Abstract—Predicting stock return or a stock index is an important financial subject which has attracted great popularity in major financial markets around the world. Scholars and investors tried to use many different kinds of algorithms to predict the stock market return. Various models have been used by researchers to forecast market value by using ANN (Artificial Neural Network). ANN model trained via back propagation algorithm is one of the models which are most commonly studied now. In this paper, a hybrid model is improved by testing the active function. To prove the applicability of the model, the improved model is applied to some individual stocks. Furthermore, an investment strategy is proposed based on the prediction results of individual stock.

Index Terms—Artificial neural network, forecast, individual stocks, investment strategy.

C. Li is with Graduate School of Engineering, Fukuoka Institute of Technology, Fukuoka, Japan (e-mail: 1chcheng1@163.com). Y. Song is with Department of Systems Engineering, Fukuoka Institute of Technology, Fukuoka, Japan (e-mail: song@fit.ac.jp).

[PDF]

Cite:Cheng Li and Yu Song, "Predicting Direction of Individual Stock Price Movement Using a Hybrid Model," Journal of Economics, Business and Management vol. 7, no. 2, pp. 60-64, 2019.

Copyright © 2008-2019. Journal of Economics, Business and Management. All rights reserved.
E-mail: joebm@ejournal.net