Using Genetic Algorithms to Find Technical Trading Rules

Abstract: In this paper, a genetic algorithm is used to find technical trading rules for standard and Poor's Composite Stock Index in 1963-89. Compared to a simple buy-and-hold strategy, these trading rules appear to reduce the variability of the returns. The results are compared to benchmark models of a random walk, an autoregressive model, and a GARCH-AR model. Conventional statistical tests and bootstrapping simulations are carried out to study the robustbess of the results. It is found that the excess returns are both stratistically and economically significant, even when transaction costs are taken into account.

Author(s): Franklin Allen and Risto Karjalainen

using genetic algorithms to find technical trading rules(1996).pdf