Bagged Pretested Portfolio Selection

Abstract

This paper exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.