Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJARAI.2015.040710
Article Published in International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 4 Issue 7, 2015.
Abstract: Obtaining accurate prediction of stock index sig-nificantly helps decision maker to take correct actions to develop a better economy. The inability to predict fluctuation of the stock market might cause serious profit loss. The challenge is that we always deal with dynamic market which is influenced by many factors. They include political, financial and reserve occasions. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria.
Alaa F. Sheta, Sara Elsir M. Ahmed and Hossam Faris, “A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(7), 2015. http://dx.doi.org/10.14569/IJARAI.2015.040710