Predicting Trading Signals of Sri Lankan Stock Market Using Genetic Algorithms and Neural Networks

Conference paper

Abstract

This study predict the trading signals of Sri Lankan stock market using two sophisticated machine learning techniques called Genetic Algorithm (GA) and Neural Networks. These two techniques in combination predict the direction (going up or not) of the close price of tomorrow’s (day t+1) ‘All Share Price Index’ (ASPI) of the Colombo Stock Exchange (CSE). The study period considered was from 1st November 2002 to 31st December 2008. The influential factors considered in this study represent the intermarket influence, political and environmental factors, economic stability and microeconomic factors: such as interest rate and exchange rate. A software called ‘genetic model’ was developed to find the optimum input variable combination that will affect the direction of tomorrow’s ASPI value. Two identical neural network models called A and B were created for two different time periods, to predict the direction of ASPI of day (t+1).

Keywords

Genetic Algorithm Stock Market Genetic Model Neural Network Model Genetic Algorithm Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    M. L. Leung, H. Daouk, and A. S. Chen, “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models,” International Journal of Forecasting, 16, pp. 173-190, 2000.CrossRefGoogle Scholar
  2. [2]
    A. Wijayanayake, and T. Rupasinghe, Identifying the Factors That Affect the Sri Lankan Stock Market, Proceedings of the International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, 2004, pp. 381-388.Google Scholar
  3. [3]
    E. D. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning, New York: Addison-Wesley, 1989.Google Scholar
  4. [4]
    M. C. Bishop, Neural Networks for Pattern Recognition, New York: Oxford University Press, 1996.MATHGoogle Scholar
  5. [5]
    K. Gurney, An Introduction to Neural Networks, London: UCL Press, 1997.CrossRefGoogle Scholar
  6. [6]
    C. D. Tilakaratne, J. H. D. S. P. Tissera and M. A. Mammadov, Predicting Trading Signals of All Share Price Index Using a Modified Neural Network Algorithm, Proceedings of 9th International Information Technology Conference, Colombo, Sri Lanka, 2008.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of ColomboColomboSri Lanka

Personalised recommendations