%0 Conference Paper %1 RePEc:sce:scecf4:200 %A Yu, Tina %A Chen, Shu-Heng %B Computing in Economics and Finance %C University of Amsterdam %D 2004 %K algorithms, genetic programming %T Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules %X 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 buy-and-hold

@inproceedings{RePEc:sce:scecf4:200, 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 buy-and-hold}, added-at = {2008-06-19T17:35:00.000+0200}, address = {University of Amsterdam}, author = {Yu, Tina and Chen, Shu-Heng}, biburl = {https://www.bibsonomy.org/bibtex/2e19f9a7fdbd1e7df9370b533bfbbe9bb/brazovayeye}, booktitle = {Computing in Economics and Finance}, interhash = {24172658655cdc4b9b81f24ad8013199}, intrahash = {e19f9a7fdbd1e7df9370b533bfbbe9bb}, keywords = {algorithms, genetic programming}, month = {8-10 July}, notes = {22 aug 2004 http://ideas.repec.org/p/sce/scecf4/200.html CEF 2004 number 200}, organisation = {Society for Computational Economics}, timestamp = {2008-06-19T17:55:03.000+0200}, title = {Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules}, year = 2004 }