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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2014 Vol.2(2): 130-132 ISSN: 2301-3559
DOI: 10.7763/LNSE.2014.V2.109

S&P 500 Forecasting by Fuzzy Neural Network

Shang-Jen Chuang and Chung-Yung Lee
Abstract—In order to establish a new method to predict the highest-price, lowest-price, and the close-price in the daily S&P 500 Index, this research use a combination of Back -propagation Neural Network (BPNN) and Fuzzy Controller as the tools to build up models. The data used in this research were collected between 03. Jan. 2000 to 02. Nov. 2012, 3230 days in total. The neural network input variables include daily open-price, a day before, two days before, and a week prior’s open-price, close-price, highest-price, lowest price, and the trading volume. The data collected from 2000~2007 were used for training; while those from 2008~2012 were used for testing.

Index Terms—Fuzzy neural network, back-propagation neural network, fuzzy controller, stock forecast.

The authors are with the Electronic Communication Engineering, National Kaohsiung Marine University, Kaohsiung, Taiwan (e-mail: david@mail.nkmu.edu.tw, 1011536101@stu.nkmu.edu.tw).


Cite: Shang-Jen Chuang and Chung-Yung Lee, "S&P 500 Forecasting by Fuzzy Neural Network," Lecture Notes on Software Engineering vol. 2, no. 2, pp. 130-132, 2014.

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