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Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach

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Hambuckers, J. [ULg] Heuchenne, Cédric [UCL]

(eng) 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.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2016
Language Anglais
Journal information "Journal of Forecasting" - Vol. 35, no. 4, p. 347-372 (2016)
Peer reviewed yes
Publisher JohnWiley & Sons Ltd. ((United Kingdom) Chichester)
issn 0277-6693
e-issn 1099-131X
Publication status Publié
Affiliation UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Links
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Bibliographic reference Hambuckers, J. ; Heuchenne, Cédric. Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach. In: Journal of Forecasting, Vol. 35, no. 4, p. 347-372 (2016)
Permanent URL http://hdl.handle.net/2078.1/171439