DMCA
Heriot-Watt University
BibTeX
@MISC{Single-objective_heriot-wattuniversity,
author = {Multiobjective Out-trades Single-objective and Dome Lohpetch and David Corne},
title = {Heriot-Watt University},
year = {}
}
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Abstract
Abstract — Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold ” (B&H) strategy. Attempts since then tend to report similar findings, except for a handful of cases where GP methods have been found to outperform B&H. Recent work has clarified that robust outperformance of B&H depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that amount to sound engineering of the GP grammar and the validation strategy. Here we add a comprehensive study of multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming B&H, even in the context of more frequent (e.g. weekly) trading decisions. Keywords-financial trading; genetic programming; multiobjective algorithms I.
Keyphrases
heriot-watt university gp method early work technical trading multiobjective approach genetic programming comprehensive study infrequent trading strategy keywords-financial trading abstract genetic programming robust outperformance simple buy gp grammar multiobjective algorithm trading decision similar finding multiobjective strategy validation strategy effective rule recent work
