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Improving moving average trading rules with boosting and statistical learning methods

Author

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  • Julián Andrada-Félix

    (Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain)

  • Fernando Fernández-Rodríguez

    (Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain)

Abstract

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out-of-sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy-and-hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Julián Andrada-Félix & Fernando Fernández-Rodríguez, 2008. "Improving moving average trading rules with boosting and statistical learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 433-449.
    • Handle: RePEc:jof:jforec:v:27:y:2008:i:5:p:433-449
    DOI: 10.1002/for.1068
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    References listed on IDEAS

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    Cited by:

    1. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    2. Jacinta Chan Phooi M’ng & Rozaimah Zainudin, 2016. "Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    3. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
    4. Jacinta Chan Phooi M'ng & Azmin Azliza Aziz, 2016. "Using Neural Networks to Enhance Technical Trading Rule Returns: A Case with KLCI," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 2(1), pages 63-70, January.
    5. Phooi M’ng, Jacinta Chan, 2018. "Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 336-345.

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