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

Garnett Carl Wilson, Derek Leblanc, Wolfgang Banzhaf

Abstract: 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.

External IDs:dblp:conf/cec/WilsonLB12