A Maximal Predictability Portfolio Using Dynamic Factor Selection Strategy

In this paper, we will propose a practical method for improving the performance of a maximal predictability portfolio (MPP) model proposed by Lo and MacKinlay and later extended by the authors. We will employ an alternative version of MPP using absolute deviation instead of variance as a measure of fitting and apply a dynamic strategy for choosing the set of factors which fits best to the market data. It will be shown that this approach leads to a significantly better performance than the standard MPP and the index.