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Neural network applications in financial asset management

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Abstract

Modelling of financial systems has traditionally been done in partial equilibrium. Such models have been very useful in expanding our understanding of the capital markets; nevertheless, many empirical financial anomalies have remained unexplainable. It is possible that this may be due to the partial equilibrium nature of these models. Attempting to model financial markets in a general equilibrium framework still remains analytically intractable. Because of their inductive nature, dynamical systems such as neural networks can bypass the step of theory formulation, and they can infer complex non-linear relationships between input and output variables. Neural networks have now been applied to a number of live systems, and have demonstrated far better performance than conventional approaches. This paper reviews the state-of-the-art in financial modelling using neural networks, and describes typical applications in key areas of forecasting, classification and pattern recognition. The applications cover areas such as asset allocation, foreign exchange, stock ranking and bond trading.

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Author notes

  1. M. Azema-Barac

    Present address: Econostat Ltd, Hennerton House, RG10 8PD, Wargrave, Berkshire, UK

Authors and Affiliations

  1. Department of Decision Science, London Business School, Sussex Place, NW1 4SA, Regents Park, London, UK

    A. N. Refenes & M. Azema-Barac

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  1. A. N. Refenes
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  2. M. Azema-Barac
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Formerly, with Department of Computer Science, University College London, Gower Street, London WC1 6BT, UK.

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Refenes, A.N., Azema-Barac, M. Neural network applications in financial asset management. Neural Comput & Applic 2, 13–39 (1994). https://doi.org/10.1007/BF01423096

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  • DOI: https://doi.org/10.1007/BF01423096

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