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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.

Suggested Citation

  • Robert Pereira, 1999. "Forecasting Ability but No Profitability: an Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules," Working Papers 1999.06, School of Economics, La Trobe University.
    • Handle: RePEc:ltr:wpaper:1999.06
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    References listed on IDEAS

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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 & Francis Journals, vol. 3(1), pages 33-36.
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    14. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
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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, Springer;Society for Computational Economics, vol. 25(1), pages 25-40, February.

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    More about this item

    Keywords

    Forecasts; Trade; Shareholders EDIRC Provider-Institution: RePEc:edi:smlatau;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G0 - Financial Economics - - General

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