Dynamic-radius species-conserving genetic algorithm for the financial forecasting of dow jones index stocks

Authors:
Michael Scott Brown;Michael J. Pelosi;Henry Dirska
Affiliations:
University of Maryland University College, Adelphi, Maryland;University of Maryland University College, Adelphi, Maryland;University of Maryland University College, Adelphi, Maryland
Venue:
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2013

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Abstract

This research uses a Niche Genetic Algorithm (NGA) called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to select stocks to purchase from the Dow Jones Index. DSGA uses a set of training data to produce a set of rules. These rules are then used to predict stock prices. DSGA is an NGA that uses a clustering algorithm enhanced by a tabu list and radial variations. DSGA also uses a shared fitness algorithm to investigate different areas of the domain. This research applies the DSGA algorithm to training data which produces a set of rules. The rules are applied to a set of testing data to obtain results. The DSGA algorithm did very well in predicting stock movement.