Multiobjective algorithms for financial trading: Multiobjective out-trades single-objective

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.