2005/01/03 by Alberto Fernandez, Sergio Gomez · 1 citation
Computer Science · #cs.NE
paper · pdf · doi:10.1016/j.cor.2005.06.017
published as Computers & Operations Research 34 (2007) 1177-1191 · 12 pages; submitted to "Computers & Operations Research"
arxiv created 2005/01/03 · arxiv updated 2009/12/01
In this paper we apply a heuristic method based on artificial neural networks in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance 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 neural network heuristic and we compare them to those obtained with three previous heuristic methods.