Portfolio selection using neural networks

By Alberto Fernández, Sergio Gómez. Computers & Operations Research, 2005

330 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/db12140f6f3fc186334201263e8bb16b71d3a9db

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Citations in the corpus

Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies (2017, methodology): “Fernandez and Gomez (2007) utilise a generalised form of the Markowitz model with additional constraints pertaining to both the number of securities to be included and the capital to be allocated to each security.”
Recent Advances in Mathematical Programming with Semi-continuous Variables and Cardinality Constraint (2013, background): “The model (MV) has been also investigated in finance literature in the context of limiteddiversification, small portfolios and portfolio selection models with real features (see, e.g., [8, 33, 34, 42, 46]).”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “In the same domain, [65] used four different heuristic models (NN, tabu search, genetic algorithm and simulated annealing) in portfolio selection.”