Genetic Algorithms and Investment Strategy Development

The aim of this paper is to investigate the use of genetic algorithms in investment strategy development. This work follows and supports Franklin Allen and Risto Karljalainen’s previous work1 in the field, as well adding new insight into further applications of the methodology. The paper first examines the capabilities of the algorithm designed in Allen and Karjalainen’s work by using human‐developed (rather than market‐historical) datasets to determine whether the algorithm can detect simple signals; the results show that the algorithm is quite capable of such basic tasks. Next, the S&P 500 test performed in Allen and Karjalainen’s original work was confirmed. Then, experiments were conducted in emerging equity markets, as well as commodities markets with a range of fundamental as well as technical indicators. The results generally show no significant positive excess returns above a buy‐and‐hold strategy; speculations for possible reasons are discussed. In addition, suggestions for future research endeavors are presented