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 mean-variance model which includes cardinality and bounding constraint...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & operations research Jg. 34; H. 4; S. 1177 - 1191
Hauptverfasser: FERNANDEZ, Alberto, GOMEZ, Sergio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.04.2007
Elsevier Science
Pergamon Press Inc
Schlagworte:
Algorithms
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Efficient frontier
Exact sciences and technology
Heuristic
Hopfield network
Integer programming
Mathematical programming
Neural networks
Operational research and scientific management
Operational research. Management science
Portfolio management
Portfolio selection
Quadratic programming
Studies
Efficient frontier
Portfolio selection
Hopfield network
Neural networks
Hopfield neural nets
Evolutionary algorithm
Mixed integer programming
Capital
Neural network
Quadratic programming
Search algorithm
Selection problem
Tabu search
Genetic algorithm
Heuristic method
Simulated annealing
Mixed problem
Portfolio management
Hopfield model
Investment
ISSN:0305-0548, 1873-765X, 0305-0548
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 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 NN heuristic and we compare them to those obtained with three previous heuristic methods. The portfolio selection problem is an instance from the family of quadratic programming problems 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 portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network (NN) model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2005.06.017