P. Perner (Ed.): MLDM 2013, LNAI 7988, pp. 27–41, 2013. © Springer-Verlag Berlin Heidelberg 2013 Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Index Stocks Michael Scott Brown, Michael J. Pelosi, and Henry Dirska University of Maryland University College, Adelphi Maryland, USA michaels.brown@faculty.umuc.edu, mpelosi@maui.net, dirska@nova.edu Abstract. This research uses a Niche Genetic Algorithm (NGA) called Dynam- ic-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. Keywords: Niche Genetic Algorithm, Genetic Algorithm, stock forecasting, financial forecasting, classification, black-box investing. 1 Introduction Forecasting the price movements of stocks is a difficult task. The possible financial reward of picking the correct direction that a stock will move has created much inter- est in developing systems to predict such behavior. Early work on formal financial forecasting began in the early 1900’s [1] and continues to this day [2]. In the last 20 years a variety of techniques have been used to predict stock movement. These include Genetic Algorithms (GAs), Neural Networks and other artificial intelligence techniques. A variety of methods use GAs and Genetic Programming (GP) to predict stock and security movements [3-5]. These methods take different approaches. Some research uses GAs and GPs to develop classification rules, while others use GAs in hybrid approaches [2]. Much research has been done using these evolutionary approaches to perform black-box investing. This paper presents a new system for financial forecasting using a Niche Genetic Algorithm (NGA). The presented research used an NGA and a set of financial data to derive a set of classification rules that the research later applied to another set of data. The training and test data each comes from a full quarter of stock prices from the