Abstract
The effect of random news on the performance of adaptive agents as investors in stock market is modelled by genetic algorithm and measured by their portfolio values. The agents are defined by the rules evolved from a simple genetic algorithm, based on the rate of correct prediction on past data. The effects of random news are incorporated via a model of herd effect to characterize the human nature of the investors in changing their original plan of investment when the news contradicts their prediction. The random news is generated by white noise, with equal probability of being good and bad news. Several artificial time series with different memory factors in the time correlation function are used to measure the performance of the agents after the training and testing. A universal feature that greedy and confident investors outperform others emerges from this study.
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
Arthur W.B., “Complexity in Economic and Financial Markets”, Complexity, 1, 1995.
Cutler, D.M., J.M. Poterba, and L.H. Summer, “ What moves Stock Prices?” Journal of Portfolio Management, 4–12, 1989.
Palmer, R.G., W.B. Arthur, J.H. Holland, B. LeBaron, and P. Tayler, “ Artificial Economic Life: A Simple Model of a Stockmarket.” Physics D, 75, 264–274, 1994.
Friedman, D. and J. Rust. (Eds.) (1991). The Double Auction Market: Institutions, Theories, and Evidence. Proceeding Volume XIV, Santa Fe Institute Studies in the Science of Complexity. Menlo Park: Addison-Wesley Publishing.
K.Y. Szeto, K.O. Chan, K.H. Cheung; Application of genetic algorithms in stock market prediction, (Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets: Progress in Neural Processing Decision Technologies for Financial Engineering, Ed. A.S. Weigend, Y. Abu-Mostafa, and A.P.N. Refenes; World Scientific), NNCM-96, 1997, p95–103.
K.Y. Szeto and K.H. Cheung; Multiple Time Series Prediction using Genetic Algorithms Optimizer (Proceedings of the International Symposium on Intelligent Data Engineering and Learning) Hong Kong, IDEAL’98, Oct. 1998
Szeto K.Y. and Luo P.X., Self-organising behaviour in genetic algorithm for the forecasting of financial time series. Proceeding of the International Conference on Forecasting Financial Markets, FFM99, 1999. CD-Rom
L.Y. Fong and K.Y. Szeto, Rules Extraction in Short Memory Time Series using Genetic Algorithms, accepted by European Journal of Physics, B. 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szeto, K., Fong, L. (2000). How Adaptive Agents in Stock Market Perform in the Presence of Random News: A Genetic Algorithm Approach. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_74
Download citation
DOI: https://doi.org/10.1007/3-540-44491-2_74
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41450-6
Online ISBN: 978-3-540-44491-6
eBook Packages: Springer Book Archive