Paper Details: Downloads: 1245
Serial Number: P1121517374
Title: Evolving Stock Market Prediction Models Using Multigene Symbolic Regression Genetic Programming
Authors: Sara Elsir M. Ahmed and Alaa F. Sheta and Hossam Faris
Abstract: Stock market prediction is one of the hottest fields of research lately due to its business applications owing to high stakes and kinds of attractive benefits that it has to offer. The stock market is a dynamic, non-linear, complex, and chaotic in nature, prediction stock market is an important financial problem that is receiving increasing attention. The main objective of this paper is to describe an experiment of building suitable prediction model for the Standard & Poor 500 return index (S&P500) with potential influence feature using genetic programming (GP) with Multigene Symbolic Regression. The experiments and analysis developed in this research show some advantages of using GP and Multigene symbolic regression that evolves linear combinations of non-linear transformations of the input variable. The using of Multigene Symbolic Regression GP for predicting S&P500 return index is showed higher capability and accuracy.
Keywords: Stock market prediction, S&P500, Genetic Programming, Multigene Symbolic Regression
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 15
Issue: 1
Submission Date: 4/24/2015 12:00:00 AM
Review Date: 5/27/2015 10:43:45 PM
Publishing Date: 6/25/2015 6:39:52 AM
Article Downloads: 1245
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