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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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References

  1. Burns T. The interpretation and use of economic predictions. Proc Roy Soc Series A. 1986; 103–125

  2. Wallis KF. Macroeconomic forecasting: a survey. Economic 1989; 99: 28–61

    Google Scholar 

  3. Holden K. Current issues in macroeconomic forecasting. In Greenaway D. (ed). Croom Helm, London (1990)

    Google Scholar 

  4. Hendry DF. Encompassing implications of feedback versus feedforward mechanisms in econometrics. Oxford Economic Papers 1988; 40: 132–149

    Google Scholar 

  5. Brock WA. Causality, chaos, explanation and prediction in economics and finance. In: Casti J, Karlqvist A. (eds). Beyond Belief: Randomness, Prediction, and Explanation in Science. CRC Press, Boca Raton, FL, 1991, pp 230–279

    Google Scholar 

  6. Peters EE. Chaos and Order in the Capital Markets. John Wiley, USA, 1991

    Google Scholar 

  7. Ormerod P, Taylor JC, Walker T. Neural networks in economics. Henley Centre, UK, 1991

    Google Scholar 

  8. Deboeck D. Pre-processing and evaluation of neural nets for trading stocks. Advanced Technology for Developers 1992; 1(2)

  9. Hoptroff AR. The principles and practice of time series forecasting and business modelling using neural nets. Neural Comput Applic 1993; 1(1): 59–66

    Google Scholar 

  10. Kimoto Tet al. Stock market prediction with modular neural networks. Proc IJCNN, San Diego, CA, 1990

  11. Schoenenburg E. Stock price prediction using neural networks: a project report. Neurocomputing 1990; 2: 17–27

    Google Scholar 

  12. Refenes AN. Constructive learning and its application to currency exchange rate prediction. In: Turban E, Trippi R (eds). Neural Network Applications in Investment and Finance Services. Probus Publishing, USA, 1992

    Google Scholar 

  13. Tsibouris G, Zeidenberg M. Back propagation as a test of the efficient markets hypothesis. Proc Hawaii Int Conf on Syst Sci, Kauai, HI, 1992, Vol 4, pp 523–532

    Google Scholar 

  14. White H. Economic prediction using neural networks: the case of IBM daily stock returns. Department of Economics, University of California, 1988

  15. Hinton G. Connectionist Learning Procedures. Computer Science Department, Carnegie-Melon University, December 1987

  16. le Cun Y. Generalisation and Network Design Strategies. Technical Report CRG-TR-89-4, University of Toronto, Department of Computer Science, 1989

  17. Refenes AN, Alippi C. Histological image understanding by error backpropagation. Microprocess Microprogram 1991; 32: 437–446

    Google Scholar 

  18. Fahlman SE, Lebiere C. The Cascade-Correlation Learning Architecture. Carnegie Mellon University, Technical Report CMU-CS-90-100, 1990

  19. Denker J.et al. Large automatic learning. Rule extraction and generalisation. Complex Syst 1987; I: 877–922

    Google Scholar 

  20. Rumelhart DEet al. Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL, and PDF Research Group (eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1 Foundation. MIT Press, Cambridge, MA, 1986

    Google Scholar 

  21. Refenes AN, Chen L. Review & Analysis of Methods for Dynamic Network Configuration. Tech-Report DCN:C30080, Department of Computer Science, University College London, 1991

  22. Mezard M, Nadal J. Learning in feedforward layered network: The tiling algorithm. J Physics A 1989; 22(12): 2191–2203

    Google Scholar 

  23. Gallant SI. Three consecutive algorithms for neural learning. Proc 8th Ann Conf of Cognitive Sci Soc 1986

  24. Frean MRA. The upstart algorithm: a method for constructing and training feedforward neural networks. Neural Computation 1989

  25. Refenes AN, Vithlani S. Constructive learning by specialisation. Proc ICANN, Helsinki, Finland, 1991

  26. Sietsma J, Dar RJ. Creating artificial neural networks that generalize well. Neural Networks 1991; 4: 67–79

    Google Scholar 

  27. Mozer MC, Smolensky P. Using relevance to reduce network size automatically. Connection Sci 1989; 1: 3–16

    Google Scholar 

  28. Refenes AN (ed). Neural Networks in the Capital Markets. John Wiley, Chichester, 1994 (to appear)

    Google Scholar 

  29. Refenes AN, Chan EB. Sound recognition and optimal neural network design. Proc EUROMICRO, Paris, France, September 1992

  30. Econostat, Tactical Asset Allocation in the Global Bond Markets. TR-92/07, Hennerton House, Wargrave, Berkshire RG10 8PD, UK, 1992

  31. Ross RL, Ross F. An empirical investigation of the arbitrage pricing theory. Finance, 1990

  32. Sharp W. Capital asset prices: A theory of market equilibrium under conditions of risk. J Finance 1964; 19

  33. Dutta S, Shashi S. Bond rating: a non-conservative application. Proc ICNN-88, San Diego, CA, July 24–27 1988

  34. Zapranis AD. Stock Ranking Using Neural Networks. Project Report, Department of Computer Science, University College London, September 1992

  35. Refenes ANet al. Stock ranking using neural networks. Proc ICNN, San Francisco, CA, Spring 1992

  36. Brown RG. Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice-Hall International, Englewood Cliffs, NJ, 1963

    Google Scholar 

  37. Merdenhall Wet al. Statistics for Management And Economics. PWS-KENT Publishing Co., Boston, MA, 1989

    Google Scholar 

  38. Weigend Aet al. Predicting the future: a connectionist approach. Int J Neural Syst 1990; 1: 193–209

    Google Scholar 

  39. Gorman RP, Sejnowski TP. Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1988; 1: 75–89

    Google Scholar 

  40. Klimisaukas CC, Guiver J, Pelton G. Neural Computing. Neural Ware Inc. Pittsburgh, PA 1989

    Google Scholar 

  41. Sen T, Oliver R, Sen N. Predicting Corporate Mergers Using Backpropagation Neural Networks: a comparative study with Logistic Models. Department of Accounting, R.B. Pamblin College of Business, Virginia Tech., 1992

  42. Wan EA. Neural network classification: A Bayesian interpretation. IEEE Trans Neural Networks 1990; 303–305

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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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