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Improving moving average trading rules with boosting and statistical learning methods
Authors:Julián Andrada‐Félix  Fernando Fernández‐Rodríguez
Affiliation: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.
Keywords:technical analysis  boosting  statistical learning  model selection  combining forecasts
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