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

Pereira, Robert (1999): Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules.

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.

• Item Type: MPRA Paper
• Language: English
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; G - Financial Economics > G0 - General

References

1. Achelis S. B. (1995) Technical Analysis from A to Z. Probus Publishing, Chicago.

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, 338–372

3. Allen F., Karjalainen R. (1999) Using Genetic Algorithms to Find Technical Trading Rules. Journal Finance Economics 51, 245–271

4. Ball R. (1978) Filter Rules: Interpretation of Market Efficiency, Experimental Problems and Australian Evidence. Accounting Education 18, 1–17

5. Bauer R. J. Jr. (1994) Genetic Algorithms and Investment Strategies. Wiley Finance Editions, John Wiley and Sons, New York

6. Bessembinder H., Chan K. (1995) The Profitability of Technical Trading Rules in the Asian Stock Markets. Pacific Basin Finance Journal 3, 257–284

7. Bessembinder H., Chan K. (1998) Market Efficiency and the Returns to Technical Analysis. Financial Management 27, 5–17

8. Brock W., Lakonishok J., LeBaron B. (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47, 1731–1764

9. Brown S., Goetzmann W., Ross S. (1995) Survival. Journal of Finance 50, 853–873

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 University

11. Carter R. B., Van Auken H. E. (1990) Security Analysis and Portfolio Management:A Survey and Analysis. Journal of Portfolio Management, Spring, 81–85

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, 369–387

13. Cumby R. E., Modest D. M. (1987) Testing for Market Timing Ability: A Framework for Forecast Evaluation. Journal of Financial Economics 19, 169–189

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, 53–66

15. Efron B. (1979) Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics 7, 1–26

16. Fama E., Blume M. (1966) Filter Rules and Stock Market Trading. Journal of Business 39, 226–241

17. Goldberg D. E. (1989) Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley, Reading

18. Holland J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor

19. Huang Y -S. (1995) The Trading Performance of Filter Rules On the Taiwan Stock Exchange. Applied Financial Economics 5, 391–395

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, 1121–1132

21. Koza J. R. (1992) Genetic Programming: On the Programming of Computers By the Means of Natural Selection. MIT Press, Cambridge

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, 451–474

23. Lo A. W., MacKinley A. G. (1990) Data Snooping Biases in Tests of Financial Asset Pricing Models. The Review of Financial Studies 3, 431–467

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, 463–488

25. Malkiel B. (1995) A Random Walk Down Wall Street, 6th edn. W. W. Norton, New York

26. Neely C. J., Weller P., Dittmar R. (1997) Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Journal of Financial Quantitative Analysis 32, 405–426

27. Raj M., Thurston D. (1996) Effectiveness of Simple Technical Trading Rules in the Hong Kong Futures Market. Applied Economic Letters 3, 33–36

28. Sweeney R. J. (1988) Some New Filter Rule Tests: Methods and Results. Journal of Financial and Quantitative Analysis 23, 285–301

29. Taylor M. P., Allen H. (1992) The Use of Technical Analysis in the Foreign Exchange Market. Journal of Money and Finance 11, 304–314

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, 116–121