Advertisement

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

  • Robert Pereira
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)

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.

Keywords

Genetic Algorithm Excess Return Trading Cost Binary Representation Sharpe Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Achelis S. B. (1995) Technical Analysis from A to Z. Probus Publishing, Chicago.Google Scholar
  2. 2.
    Alexander S. S. (1964) Price Movements in Speculative Markets: Trends or Random Walks, No. 2. In: Cootner P. (Ed.) The Random Character of Stock Prices. MIT Press, Cambridge, 338–372Google Scholar
  3. 3.
    Allen F., Karjalainen R. (1999) Using Genetic Algorithms to Find Technical Trading Rules. Journal Financial Economics 51, 245–271CrossRefGoogle Scholar
  4. 4.
    Ball R. (1978) Filter Rules: Interpretation of Market Efficiency, Experimental Problems and Australian Evidence. Accounting Education 18, 1–17Google Scholar
  5. 5.
    Bauer R. J. Jr. (1994) Genetic Algorithms and Investment Strategies. Wiley Finance Editions, John Wiley and Sons, New YorkGoogle Scholar
  6. 6.
    Bessembinder H., Chan K. (1995) The Profitability of Technical Trading Rules in the Asian Stock Markets. Pacific Basin Finance Journal 3, 257–284CrossRefGoogle Scholar
  7. 7.
    Bessembinder H., Chan K. (1998) Market Efficiency and the Returns to Technical Analysis. Financial Management 27, 5–17CrossRefGoogle Scholar
  8. 8.
    Brock W., Lakonishok J., LeBaron B. (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47, 1731–1764CrossRefGoogle Scholar
  9. 9.
    Brown S., Goetzmann W., Ross S. (1995) Survival. Journal of Finance 50, 853–873CrossRefGoogle Scholar
  10. 10.
    Brown S., Goetzmann W., Kumar A. (1998) The Dow Theory: William Peter Hamilton’s Track Record Reconsidered. Working Paper, Stern School of Business, New York UniversityGoogle Scholar
  11. 11.
    Carter R. B., Van Auken H. E. (1990) Security Analysis and Portfolio Management: A Survey and Analysis. Journal of Portfolio Management, Spring, 81–85CrossRefGoogle Scholar
  12. 12.
    Corrado C. J., Lee S. H. (1992) Filter Rule Tests of the Economic Significance of Serial Dependence in Daily Stock Returns. Journal of Financial Research 15, 369–387Google Scholar
  13. 13.
    Cumby R. E., Modest D. M. (1987) Testing for Market Timing Ability: A Framework for Forecast Evaluation. Journal of Financial Economics 19, 169–189CrossRefGoogle Scholar
  14. 14.
    Dorsey R. E., Mayer W. J. (1995) Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features. Journal of Business and Economic Statistics 13, 53–66Google Scholar
  15. 15.
    Efron B. (1979) Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics 7, 1–26MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Fama E., Blume M. (1966) Filter Rules and Stock Market Trading. Journal of Business 39, 226–241CrossRefGoogle Scholar
  17. 17.
    Goldberg D. E. (1989) Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley, ReadingGoogle Scholar
  18. 18.
    Holland J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann ArborGoogle Scholar
  19. 19.
    Huang Y -S. (1995) The Trading Performance of Filter Rules On the Taiwan Stock Exchange. Applied Financial Economics 5, 391–395CrossRefGoogle Scholar
  20. 20.
    Hudson R., Dempsey M., Keasey K. (1996) A Note on the Weak Form of Efficiency of Capital Markets: The Application of Simple Technical Trading Rules to the U.K. Stock Markets- 1935–1994. Journal of Banking and Finance 20, 1121–1132CrossRefGoogle Scholar
  21. 21.
    Koza J. R. (1992) Genetic Programming: On the Programming of Computers By the Means of Natural Selection. MIT Press, CambridgeMATHGoogle Scholar
  22. 22.
    Levich R. M., Thomas L. R. (1993) The Significance of Technical Trading-Rule Profits in the Foreign Exchange Market: A Bootstrap Approach. Journal of International Money and Finance 12, 451–474CrossRefGoogle Scholar
  23. 23.
    Lo A. W., MacKinley A. G. (1990) Data Snooping Biases in Tests of Financial Asset Pricing Models. The Review of Financial Studies 3, 431–467CrossRefGoogle Scholar
  24. 24.
    Maddala G. S., Li H. (1996) Bootstrap Based Tests in Financial Models. In: Maddala G. S., Rao C. R. (Eds.) Handbook of Statistics, V XIV. Elsevier Science, 463–488Google Scholar
  25. 25.
    Malkiel B. (1995) A Random Walk Down Wall Street, 6th edn. W. W. Norton, New YorkGoogle Scholar
  26. 26.
    Neely C. J., Weiler P., Dittmar R. (1997) Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Journal of Financial Quantitative Analysis 32, 405–426CrossRefGoogle Scholar
  27. 27.
    Raj M., Thurston D. (1996) Effectiveness of Simple Technical Trading Rules in the Hong Kong Futures Market. Applied Economic Letters 3, 33–36CrossRefGoogle Scholar
  28. 28.
    Sweeney R. J. (1988) Some New Filter Rule Tests: Methods and Results. Journal of Financial and Quantitative Analysis 23, 285–301CrossRefGoogle Scholar
  29. 29.
    Taylor M. P., Allen H. (1992) The Use of Technical Analysis in the Foreign Exchange Market. Journal of Money and Finance 11, 304–314CrossRefGoogle Scholar
  30. 30.
    Whitley D. (1989) The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In: Schaffer D.J. (Ed.) Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, 116–121Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Robert Pereira
    • 1
  1. 1.Investment Solutions & Quantitative AnalyticsMerrill Lynch Investment ManagersMelbourneAustralia

Personalised recommendations