Abstract
This paper presents the development of a hybrid system based on Genetic Algorithms, Neural Networks and the GARCH model for the selection of stocks and the management of investment portfolios. The hybrid system comprises four modules: a genetic algorithm for selecting the assets that will form the investment portfolio, the GARCH model for forecasting stock volatility, a neural networks for predicting asset returns for the portfolio, and another genetic algorithm for determining the optimal weights for each asset. Portfolio management has consisted of weekly updates over a period of 49 weeks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bollerslev, T. (1986). “Generalized Autoregressive Conditional Hetero-scedasticity,” Journal of Econometrics, 31, 307–327.
Campos L., E. (1998). “Modelo de Escala Local: Uma Alternativa de Especificação Multiplicativa para Estimação e Previsão de Volatilidade de Séries Financeras,” Dissertação de Mestrado Dee Puc-Rio, Rio de Janeiro, Brazil.
Down, K. (1998). Beyond Value at Risk — The New Science of Risk Management John Wiley & Sons.
Drost, F. C. and T. N. E. (1992). “Temporal Aggregation of GARCH Processes,” Center Discussion Papers 9066 e 9240. Tilburg University.
Duarte Jr., Antonio M., M. A. Pinheiro, and T. B. B. Heil (1997). Previsão da Volatilidade de Ativos e Índices Brasileiros. Resenha BM&F, Number 112.
Elton, E. J. and M. J. Gruber (1995). Modern Portfolio Theory and Investment Analysis, 5th ed. John Wiley & Sons.
Engle, R. F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of the United Kingdom Inflation,” Econometrica, 50(4), 987–1007.
Engle, R. F. (1995). ARCH Selected Readings — Advanced Texts in Econometrics. Oxford University Press.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley.
Haykin, S. (1998). Neural Networks — A Comprehensive Foundation. McMillan College Publishing Co.
Hull, J. C. (1999). Options, Futures & other Derivatives, 4th ed. Prentice Hall.
Jorion, P. (1997). Value at Risk: The New Benchmark for Controlling Market Risk. McGraw-Hill.
Lazo, J. G. L., Marley M.B.R. V., and M. A. C. Pacheco (2000). “A Hybrid Genetic-Neural System for Portfolio Selection and Management,” in Dimitris Tsaptsinos (ed.), Proceedings of the Sixth International Conference on Engineering Applications of Neural Networks (EANN 2000), 147–154.
Levy, H. and M. Sarnat (1984). Portfolio and Investment Selection: Theory and Practice. Prentice-Hall.
Markowitz, H. M. (1959). Portfolio Selection. Cambridge, MA: Black-Well.
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag.
Nelso, D. B. (1990). “Stationarity and Persistence in the GARCH(1,1) Model,” Econometric Theory, 6, 318–334.
Zurada, J. M. (1995). Introduction to Artificial Neural System. Boston: Mass.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Lazo Lazo, J.G., Pacheco, M.A.C., Vellasco, M.M.R. (2002). Portfolio Selection and Management Using a Hybrid Intelligent and Statistical System. In: Chen, SH. (eds) Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0835-9_10
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0835-9_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5262-4
Online ISBN: 978-1-4615-0835-9
eBook Packages: Springer Book Archive