author = "Tina Yu and Shu-Heng Chen",
title = "Using Genetic Programming with Lambda Abstraction to
Find Technical Trading Rules",
booktitle = "Computing in Economics and Finance",
year = "2004",
address = "University of Amsterdam",
month = "8-10 " # jul,
organisation = "Society for Computational Economics",
keywords = "genetic algorithms, genetic programming",
abstract = "Using GP with lambda abstraction module mechanism to
generate technical trading rules based on S&P 500
index, we find strong evidence of excess returns over
buy-and-hold after transaction cost on the testing
period from 1989 to 2002. The rules can be interpreted
easily; each uses a combination of one to four widely
used technical indicators to make trading decisions.
The consensus among GP rules is high, with most of the
time 80% of the evolved rules give the same decision.
The GP rules give high transaction frequency.
Regardless of market climate, they are able to identify
opportunities to make profitable trades and out-perform
notes = "22 aug 2004
http://ideas.repec.org/p/sce/scecf4/200.html CEF 2004