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

See all articles by Christopher J. Neely

Christopher J. Neely

Federal Reserve Bank of St. Louis - Research Division

Date Written: October 27, 1999

Abstract

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

Neely, Christopher J., Using Genetic Algorithms to Find Technical Trading Rules: A Comment on Risk Adjustment (October 27, 1999). Federal Reserve Bank of St. Louis Working Paper No. 99-015A, Available at SSRN: https://ssrn.com/abstract=189488 or http://dx.doi.org/10.2139/ssrn.189488

Christopher J. Neely (Contact Author)

Federal Reserve Bank of St. Louis - Research Division ( email )

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HOME PAGE: http://www.stls.frb.org/research/econ/cneely/

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