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Improving moving average trading rules with boosting and statistical learning methods
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DOI：10.1002/for.1068.png)
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Abstract
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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 (C) 2008 John Wiley & Sons, Ltd.
Keywords:
technical analysis
boosting
statistical learning
model selection
combining forecasts
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