Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48007
Title: Improving moving average trading rules with boosting and statistical learning methods
Authors: Andrada Félix, Julián 
Fernández Rodríguez, Fernando 
UNESCO Clasification: 5302 Econometría
Keywords: Bolsa de valores
Modelos económetricos
Issue Date: 2008
Publisher: 0277-6693
Journal: Journal of Forecasting 
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
URI: http://hdl.handle.net/10553/48007
ISSN: 0277-6693
DOI: 10.1002/for.1068
Source: Journal of Forecasting[ISSN 0277-6693],v. 27, p. 433-449
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