Discovering effective technical trading rules with genetic programming: Towards robustly outperforming buy-and-hold

Dome Lohpetch, David Corne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

Genetic programming is now a common research tool in financial applications. One classic line of exploration is their use to find effective trading rules for individual stocks or for groups of stocks (such as an index). The classic work in this area (Allen & Karjaleinen, 99) found profitable rules, but which did not outperform a straightforward "buy and hold" strategy. Several later works report similar outcomes, while a small number of works achieve out-performance of buy and hold, but prove difficult to replicate. We focus here on indicating clearly how the performance in one such study (Becker & Seshadri, 03) was replicated, and we carry out additional investigations which point towards guidelines for generating results that robustly outperform buy-and-hold. These guidelines relate to strategies for organizing the training dataset, and aspects of the fitness function. ©2009 IEEE.

Original languageEnglish
Title of host publication2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings
Pages439-444
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 World Congress on Nature and Biologically Inspired Computing - Coimbatore, India
Duration: 9 Dec 200911 Dec 2009

Conference

Conference2009 World Congress on Nature and Biologically Inspired Computing
Abbreviated titleNABIC 2009
CountryIndia
CityCoimbatore
Period9/12/0911/12/09

Keywords

  • Genetic programming
  • Stock trading
  • Technical trading rules

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    Lohpetch, D., & Corne, D. (2009). Discovering effective technical trading rules with genetic programming: Towards robustly outperforming buy-and-hold. In 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings (pp. 439-444) https://doi.org/10.1109/NABIC.2009.5393324