Abstract
In this chapter, we shall review some early applications of genetic programming to financial data mining and knowledge discovery, including some analyses of its statistical behavior. These early applications are known as symbolic regression in GP. In this type of application, genetic programming is formally demonstrated as an engine searching for the hidden relationships among observations. Here, we find evidence of the closest step ever made toward the original motivation of John Holland’s invention of genetic algorithms: Instead of trying to write your programs to perform a task you don’t quite know how to do, evolve them.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allen, F., Karjalainen, R. (1993) Using Genetic Algorithms to Find Technical Trading Rules. Rodney L. White Center for Financial Research, The Wharton School, Technical Report, 20–93
Allen, F., Karjalainen, R. (1999) Using Genetic Algorithms to FindTechnical Trading Rules. Journal of Financial Economics, Vol. 51, no 2, 215–271
Bessembinder, H., Chan, 1K. (1995) The Profitability of Technical Trading Rules in the Asian Stock Markets. Pacific Basin Finance Journal, ‘Vol. 3, 2517–284
Brock, W.A., Lakonishok, I., LeBaron, B. (1992): Simple Technical ‘Trading Rules and the Stochastic Properties of Stock Returrns..Journal of Finance, Vol. 47, 1731–1764
Bauer, R. J. Jr., Liepins, G. E. (1992) Genetic Algorithms and Computerized Trading Strategies. In: O’Leary, D. E., Watkins, R. R. (I4’,ds.), Expert Systems in Finance. North Holland
Bauer, R. J. Jr. (1994) Genetic Algorithms and Investment Strategies. Wiley
Chen, S.-H., Yeh, C.-H. (1997) Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming. journal of Economic Dynamics and Control, Vol. 21, no. 6, 1043–1063
Chen, S.-H., Kuo, T.-W. (2002) Genetic Programming: A Tutorial with the Software Simple GP. In Chen, S.-H. (Ed.), Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer Academic Publishers, 55–77
Chidambaran, N., Lee, C.-W. J., Trigueros, J. (2002) Option Pricing via Genetic Programming. In: Chen, S.-H. (Ed.), Evolutionary Computation in Economics and Finance, Physica-Verlag, 383–397
Johnson, H. E., Gilbert, R. J., Winson, K., Goodacre, R., Smith, A. R., Rowland, J. J., Hall, M. A., Kell, D. B. (2000) Explanatory Analysis of the Metabolome using Genetic Programming of Simple, Interpretable Rules. Genetic Programming and Evolable Machines, Vol. 1, no 3, 243–258
Kaboudan, M. A. (1999) A Measure of Time Series’s Predictability Using Genetic Programming Applied to Stock Returns. Journal of Forecasting, Vol. 18, 345–357
Kaboudan, M. A. (2002) GP Forecasts of Stock Prices for Profitable Trading. In Chen, S.-H. (Ed.), Evolutionary Computation in Economics and Finance. Physica-Verlag, 359–381
Kaboudan, M. A. (2001) Genetically Evolved Models and Normality of Their Fitted Residuals. Journal of Economic Dynamics and Control, Vol. 25, no. 11, 1719–1749
Koza, J. (1992) A Genetic Approach to Econometric Modelling. In: 13ourgine, P., Walliser, B. (Eds.) Economics and Cognitive Science. Pergamon Press, 57–75
Koza, J. (1992a) Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press.
Koza, J. (1994) Genetic Programming I I: Automatic Discovery of Reusable Programs. The MIT Press.
Neely, C., Weller, P., Ditmar, R. (1997) Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Journal of Financial and Quantitative Analysis, Vol. 32, no. 4, 405–427
Neely, C. J., Weller P. A. (1999) Technical Trading Rules in the European Monetary System. Journal of International Money and Finance, Vol. 18, no. 3, 429–458
Szpiro, G. G. (1997a) Forecasting Chaotic Time Series with Genetic Algorithms. Physical Review E, 2557–2568
Pereira, R. (2002): Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimized Technical Trading Rules. In: Chen. S.-H. (Ed.), Evolutionary Computation in Economics and Finance. PhysicaVerlag, 287–310
Szpiro, G. G. (1997b) A Search for Hidden Relationships: Data Mining with Genetic Algorithms. Computational Economics, Vol. 10, no. 3, 267–277
Szpiro, G. (2002) Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics. In: Chen S.-H. ( Ed.) Evolutionary Computation in Economics and Finance. Physica Verlag.
Wang, J. (2000) Trading and Hedging in SandP 500 Spot and Futures Markets Using Genetic Programming. Journal of Fritures Markets, Vol. 20, no. 10, 911–942
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chen, SH., Kuo, TW. (2004). Discovering Hidden Patterns with Genetic Programming. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-662-06373-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07902-3
Online ISBN: 978-3-662-06373-6
eBook Packages: Springer Book Archive