[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 :
Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach
Publication date :
September 2014
Version :
Submitted version 20/11/2014
Number of pages :
43
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE] BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique