Reference : Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap ...
Scientific journals : Article
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/2268/186808
Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach
English
Hambuckers, julien mailto [Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie >]
Heuchenne, Cédric mailto [Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie >]
Jul-2016
Journal of Forecasting
John Wiley & Sons, Inc. - Business
35
4
347-372
Yes (verified by ORBi)
International
0277-6693
1099-131X
[en] trading rule ; bootstrap .632 ; out-of-sample ; predictive ability
[en] In this paper, we provide a novel way to estimate the out-of-sample predictive ability
of a trading rule. Usually, this ability is estimated using a sample splitting scheme,
true out-of-sample data being rarely available. We argue that this method makes
a poor use of the available data and creates data mining possibilities. Instead, we
introduce an alternative .632 bootstrap approach. This method enables to build in-
sample and out-of-sample bootstrap datasets that do not overlap but exhibit the same
time dependencies. We show in a simulation study that this technique drastically
reduces the mean squared error of the estimated predictive ability. We illustrate our
methodology on IBM, MSFT and DJIA stock prices, where we compare 11 trading
rules speci cations. For the considered datasets, 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.
UER Operations
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/186808

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