Forecasting stock prices using Genetic Programming and Chance Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{oai:RePEc:sce:scecfa:489,

• author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
• title = "Forecasting stock prices using Genetic Programming and Chance Discovery",
• booktitle = "12th International Conference On Computing In Economics And Finance",
• year = "2006",
• pages = "number 489",
• month = jul,
• organisation = "Society for Computational Economics",
• bibsource = "OAI-PMH server at oai.repec.openlib.org",
• description = "Forecasting, Chance discovery, Genetic programming, machine learning",
• identifier = "RePEc:sce:scecfa:489",
• oai = "oai:RePEc:sce:scecfa:489",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://repec.org/sce2006/up.13879.1141401469.pdf",
• URL = "http://privatewww.essex.ac.uk/~algarc/Publications/CEF2006.pdf",
• URL = "http://ideas.repec.org/p/sce/scecfa/489.html",
• abstract = "In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been used to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.",
• notes = "CEF 2006",

}

Genetic Programming entries for Alma Lilia Garcia Almanza Edward P K Tsang

Citations