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Identifying the best technical trading rule: a .632 bootstrap approach.
Hambuckers, julien; Heuchenne, Cédric
20148th International Conference on Computational and Financial Econometrics and 7th International Conference of the ERCIM WG on Computational and Methodological Statistics
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Keywords :
bootstrap .632; predictive ability; technical trading; time series econometrics
Abstract :
[en] In this paper, we estimate the out-of-sample predictive ability of a set of trading rules. Usually, this ability is estimated using a rolling-window sample-splitting scheme, true out-of-sample data being rarely available. We argue that this method makes a poor use of the available information and creates data mining possibilities. Instead, we introduce an alternative bootstrap approach, based on the .632 bootstrap principle. This method enables to build in-sample and out-of-sample bootstrap data sets that do not overlap and exhibit the same time dependencies. We illustrate our methodology on IBM and Microsoft daily stock prices, where we compare 11 trading rules specifications. For the data sets considered, two different filter rule specifications have the highest out-of-sample mean excess returns. However, all tested rules cannot beat a simple buy-and-hold strategy when trading at a daily frequency.
Research center :
Centre for Quantitative Methods and Operation Management (QuantOM)
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Hambuckers, julien ;  Université de Liège - ULiège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Heuchenne, Cédric ;  Université de Liège - ULiège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Language :
English
Title :
Identifying the best technical trading rule: a .632 bootstrap approach.
Publication date :
07 December 2014
Event name :
8th International Conference on Computational and Financial Econometrics and 7th International Conference of the ERCIM WG on Computational and Methodological Statistics
Event organizer :
ERCIM Working Group on Computational and Methodological Statistics (CMStatistics)
University of Pisa
Event place :
Pisa, Italy
Event date :
du 6 décembre 2014 au 8 décembre 2014
Audience :
International
Peer reviewed :
Editorial reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
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since 20 November 2014

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