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

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Evolutionary Computation in Economics and Finance

Part of the book series: Studies in Fuzziness and Soft Computing ((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.

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Author information

Authors and Affiliations

  1. Investment Solutions & Quantitative Analytics, Merrill Lynch Investment Managers, Melbourne, Australia, 3000

    Robert Pereira

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  1. Robert Pereira
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Editor information

Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan, R. O. China

    Shu-Heng Chen

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© 2002 Springer-Verlag Berlin Heidelberg

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Pereira, R. (2002). Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_16

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  • DOI: https://doi.org/10.1007/978-3-7908-1784-3_16

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2512-1

  • Online ISBN: 978-3-7908-1784-3

  • eBook Packages: Springer Book Archive

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