Improving moving average trading rules with boosting and statistical learning methods
Julian Andrada-Felix () and
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Fernando Fernández-Rodríguez: Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain, Postal: Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain
Journal of Forecasting, 2008, vol. 27, issue 5, 433-449
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.
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:27:y:2008:i:5:p:433-449
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