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Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules

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  • Robert Pereira

    (Department of Economics and Finance, La Trobe University)

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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Bibliographic Info

Paper provided by School of Economics, La Trobe University in its series Working Papers with number 1999.06.

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Length: 27 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:ltr:wpaper:1999.06

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Keywords: Forecasts; Trade; Shareholders EDIRC Provider-Institution: RePEc:edi:smlatau;

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References

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  1. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(04), pages 405-426, December.
  2. 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.
  3. 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.
  4. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-67.
  5. 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.
  6. 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.
  7. Mahendra Raj & David Thurston, 1996. "Effectiveness of simple technical trading rules in the Hong Kong futures markets," Applied Economics Letters, Taylor & Francis Journals, vol. 3(1), pages 33-36.
  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. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
  10. 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.
  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. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
  13. 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.
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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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