By Topic

Using sector information with linear genetic programming for intraday equity price trend analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wilson, G. ; Afinin Labs. Inc., St. John''s, NL, Canada ; Leblanc, D. ; Banzhaf, W.

A number of researchers who apply genetic programming (GP) to the analysis of financial data have had success in using predictability pretests to determine whether the time series under analysis by a GP contains patterns that are actually inherently predictable. However, most studies to date apply no such pretests, or pretests of any kind. Most previous work in this area has attempted to use filters to ensure inherent predictability of the data within a window of a time series, whereas other works have used multiple time frame windows under analysis by the GP to provide one overall GP recommendation. This work, for the first time, analyzes the use of external information about the price trend of a stock's market sector. This information is used in a filter to bolster confidence of a GP-based alert regarding formation of a trend for the chosen stock. Our results indicate a significant improvement in trend identification for the majority of stocks analyzed using intraday data.

Published in:

Evolutionary Computation (CEC), 2012 IEEE Congress on

Date of Conference:

10-15 June 2012