Forecasting stock prices using Genetic Programming and Chance Discovery
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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