A graphical model framework for stock portfolio construction with application to a Neural Network based trading strategy

Neural Network methods for stock prediction can be used to successfully signal when to buy individual stocks. The formation of weighted portfolios of such signaled stocks has however received little attention in the literature. Classical Mean-Variance based portfolio optimization techniques assume that stock returns fall into the Elliptical Family of Distributions and as such are not well suited to use with Neural Network based predictors. This paper introduces a new distribution independent framework for stock portfolio construction. Testing shows that the framework could be used to form profitable stocks portfolios when applied to a Neural Network stock predictor.