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

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.