A Hybrid Forecasting Model for Stock Market Prediction

Huseyin Ince and Theodore B. Trafali̇s

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2017, vol. 51, issue 3, 263-280

Abstract: Stock market predictions have been studied by academics and practitioners. In this paper, a hybrid model is proposed to predict the stock market movement. We have combined the independent component analysis (ICA) and kernel methods. ICA is used to select the important indicators. After determining the inputs, kernel methods are employed to predict the direction of the stock market. We have used the Dow-Jones, Nasdaq and S&P500 indices for experiments. Technical indicators of the indices are used as input variables for the proposed model. According to the analysis of the experimental results, kernel methods are capable of producing satisfactory forecasting accuracies and gain rates for Dow-Jones, Nasdaq and S&P 500 indices. The trading experiment shows that the kernel methods obtain higher rate of returns than the other investment strategies.

Keywords: Hybrid Model; Kernel Methods; Stock Market Forecasting; Support Vector Machines; Minimax Probability Machines (search for similar items in EconPapers)
JEL-codes: C45 E37 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb3_2017p263-280.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cys:ecocyb:v:50:y:2017:i:3:p:263-280

Access Statistics for this article

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH is currently edited by Gheorghe RUXANDA

More articles in ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH from Faculty of Economic Cybernetics, Statistics and Informatics Contact information at EDIRC.
Bibliographic data for series maintained by Corina Saman ().