Forecasting of future stock prices using neural networks and genetic algorithms

Stelios A. Mitilineos and Panagiotis Artikis ()

International Journal of Decision Sciences, Risk and Management, 2017, vol. 7, issue 1/2, 2-25

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.

Keywords: stock market; future prices; numerical methods; neural networks; genetic algorithms. (search for similar items in EconPapers)
Date: 2017
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