Forecasting of future stock prices using neural networks and genetic algorithms
by Stelios A. Mitilineos; Panayiotis G. Artikis
International Journal of Decision Sciences, Risk and Management (IJDSRM), Vol. 7, No. 1/2, 2017

Abstract: Neural networks are a well established and widely used class of machine learning tools for classification and clustering that have been successfully applied to time-series analysis and prediction. On the other hand, genetic algorithms have been used in the literature for a vast range of optimisation problems ranging from electromagnetic optimisation to mechanical design, industrial control and genetic engineering. In this work, we propose to use the former in predicting future values of a time-series of particular interest, i.e., the future values of stock market indices. Based on a large body of work that is present in the literature, we develop, test and present a set of neural networks for predicting future stock market index values. Furthermore, we evaluate the use of modified GAs as a stand-alone tool for prediction, but also the use of GAs as neural network training and optimising tools. We also test two benchmark time-series extrapolation techniques based on linear regression. The proposed stock market prediction tools are fine-tuned and applied to a number of stock market index time-series and numerical results are presented demonstrating their superiority compared to standard benchmark techniques.

Online publication date:: Tue, 02-May-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Decision Sciences, Risk and Management (IJDSRM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email