Using Genetic Algorithms to Find Technical Trading Rules: A Comment on Risk Adjustment
Federal Reserve Bank of St. Louis Working Paper No. 99-015A
17 Pages Posted: 9 Nov 1999
Date Written: October 27, 1999
Allen and Karjalainen (1999) used genetic programming to develop optimal ex ante trading rules for the S&P 500 index. They found no evidence that the returns to these rules were higher than buy-and-hold returns but some evidence that the rules had predictive ability. This comment investigates the risk-adjusted usefulness of such rules and more fully characterizes their predictive content. These results extend Allen and Karjalainen's (1999) conclusion by showing that although the rules' relative performance improves, there is no evidence that the rules significantly outperform the buy-and-hold strategy on a risk-adjusted basis. Therefore, the results are consistent with market efficiency. Nevertheless, risk-adjustment techniques should be seriously considered when evaluating trading strategies.
JEL Classification: G0, G14
Suggested Citation: Suggested Citation