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Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading

  • Dome Lohpetch
  • David Corne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold” trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri (2003), which showed outperformance of buy-and-hold. In turn, Becker & Seshadri’s work had made several modifications to Allen & Karjalainen’s work, including the adoption of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable outperformance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust outperformance of buy-and-hold. Here we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions.

Keywords

genetic programming technical trading rules data mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dome Lohpetch
    • 1
  • David Corne
    • 1
  1. 1.School of MACSHeriot-Watt UniversityEdinburghUK

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