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Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules

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  • Pereira, Robert

Abstract

This paper evaluates the performance of several popular technical trading rules applied to the Australian share market. The optimal trading rule parameter values over the in-sample period of 4/1/82 to 31/12/89 are found using a genetic algorithm. These optimal rules are then evaluated in terms of their forecasting ability and economic profitability during the out-of-sample period from 2/1/90 to the 31/12/97. The results indicate that the optimal rules outperform the benchmark given by a risk-adjusted buy and hold strategy. The rules display some evidence of forecasting ability and profitability over the entire test period. But an examination of the results for the sub-periods indicates that the excess returns decline over time and are negative during the last couple of years. Also, once an adjustment for non–synchronous trading bias is made, the rules display very little, if any, evidence of profitability.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 9055.

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Date of creation: 1999
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Handle: RePEc:pra:mprapa:9055

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  1. Mahendra Raj & David Thurston, 1996. "Effectiveness of simple technical trading rules in the Hong Kong futures markets," Applied Economics Letters, Taylor and Francis Journals, vol. 3(1), pages 33-36.
  2. Levich, Richard M. & Thomas, Lee III, 1993. "The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach," Journal of International Money and Finance, Elsevier, vol. 12(5), pages 451-474, October.
  3. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
  4. Cumby, Robert E. & Modest, David M., 1987. "Testing for market timing ability : A framework for forecast evaluation," Journal of Financial Economics, Elsevier, vol. 19(1), pages 169-189, September.
  5. Christopher J. Neely & Paul A. Weller & Robert Dittmar, 1997. "Is technical analysis in the foreign exchange market profitable? a genetic programming approach," Working Papers 1996-006, Federal Reserve Bank of St. Louis.
  6. Stephen J. Brown & William N. Goetzmann & Alok Kumar, 2004. "The Dow Theory: William Peter Hamilton's Track Record Re-considered," Yale School of Management Working Papers ysm30, Yale School of Management.
  7. Andrew W. Lo & A. Craig MacKinlay, 1991. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," NBER Working Papers 3001, National Bureau of Economic Research, Inc.
  8. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(2-3), pages 257-284, July.
  9. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
  10. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
  11. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 285-300, September.
  12. Corrado, Charles J & Lee, Suk-Hun, 1992. "Filter Rule Tests of the Economic Significance of Serial Dependencies in Daily Stock Returns," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 15(4), pages 369-87, Winter.
  13. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
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Cited by:
  1. Serge Hayward, 2005. "The Role of Heterogeneous Agents’ Past and Forward Time Horizons in Formulating Computational Models," Computational Economics, Society for Computational Economics, vol. 25(1), pages 25-40, February.

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