Portfolio selection using neural networks

In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz meanvariance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the NN heuristic and we compare them to those obtained with three previous heuristicmethods.Theportfolioselectionproblemisaninstancefromthefamilyofquadraticprogrammingproblems when the standard Markowitz mean-variance model is considered. But if this model is generalized to include cardinality and bounding constraints, then the portfolio selection problem becomes a mixed quadratic and integer programming problem. When considering the latter model, there is not any exact algorithm able to solve the p...

• Type: Journal
• Year: 2007
• Where: COR
• Authors: Alberto Fernández, Sergio Gómez
• Keywords: COR 2007, Heuristic, Heuristic Methods, Portfolio Selection Problem