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)


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.


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

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

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