Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices

Veselin L. Shahpazov, Lyubka A. Doukovska, Dimitar N. Karastoyanov

Abstract

The interest in using artificial neural networks (ANN’s) for forecasting has led to a tremendous surge in research activities over time. Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. Forecasting problems arise in so many different disciplines and the literature on forecasting using ANN’s is scattered in so many diverse fields that it is hard for a researcher to be aware of all the work done to date in the area. There is an extensive literature in financial applications of ANN’s. Naturally forecasting stock price or financial markets has attracted considerable interest and it has been one of the biggest challenges. This paper reviews the history of the application of artificial neural networks for forecasting future stock prices. From the introduction of the back-propagation algorithm in 1980’s for training an MLP neural network by Werbos, who used this technique to train a neural network and claimed that neural networks are better than regression methods and Box-Jenkins model in prediction problems through the application of such technics to financial markets forecasting by pioneers in the field like White, Kimoto and Kamijo to the more recent studies of stocks prices in not only the biggest capital markets but also in some emerging and illiquid markets, we will look at the progress made in the past more than twenty five years of research.

References

  1. Azoff E. M., 1994. Neural Network Time Series Forecasting of Financial Markets. John Wiley&Sons.
  2. Chen A. S., M. T. Leung, H. Daouk, 2003. Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers & Operations Research, Vol. 30, Issue 6, pp. 901-923.
  3. Chen Z., X. Du, 2013. Study of Stock Prediction Based on Social Network, In: Proceedings of 2013 International Conference on Social Computing, September 8-14 Alexandria, VA, USA, pp. 913-916.
  4. Freisleben B., 1992. Stock market prediction with backpropagation networks. In: Proceedings of 5th Intl. Conf. on the industrial and engineering application of artificial intelligence and expert systems, Germany, June 1992, pp. 451-460.
  5. Gosh A., 2012. Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning. Brain, Vol. 3 Issue 1, pp. 41-49.
  6. Kaastra I., M. Boyd, 1995. Forecasting futures trading volume using neural networks Journal of Futures Markets, Vol. 15, Issue 8, pp. 953-970.
  7. Kalyvas E., 2001. Using Neural Networks And Genetic Algorithms to Predict Stock Market Returns. Department of Computer Science, The University of Manchester, UK.
  8. Kamijo K., T. Tanigawa, 1990. Stock market pattern recognition - A recurrent neural network approach Proc. IJCNN, San Diego, Vol. 1, pp. 215-221.
  9. Kim K., 2006. Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications, Vol. 30, Issue 3, pp. 519- 526.
  10. Kim W., B. Lee, 2004. Stock Market Prediction using artificial neural networks with optimal feature transformation. Neural Comput & Applic, Vol. 13, pp. 255-260.
  11. Kimoto T., K. Asakawa, M. Yoda, M. Takeoka, 1990. Stock market prediction system with modular neural network. Proceedings of the International Joint Conference on Neural Networks, pp. 1-6.
  12. Leung M. T., H. Daouk, A.-S. Chen, 2000. Forecasting stock indices: A comparison of classification and level estimation models. Int. J of Forecasting, Vol. 16, pp. 173-190.
  13. Madden M. G., N. O'Connor, 2006. Neural Network Approach to Predicting Stock Exchange Movements using External Factors. In: Applications and Innovations in Intelligent Systems XIII, pp. 64-77.
  14. Matsuba I., 1991. Neural sequential associator and its application to stock price prediction Proc. IECON 91, Japan, Vol. 2, pp. 1476-1479.
  15. Pan H., I. Tilakaratne, J. Yearwood, 2005. Predicting Australian Stock Market Index Using Neural Networks Exploiting Dynamical Swings and Intermarket Influences, Journal of Research and Practice in Information Technology, Vol. 37, No. 1, pp. 43-55.
  16. Werbos P. J., 1988. Generalization of back-propagation with application to a recurrent gas market model, Neural Networks, Vol. 1, pp. 339-356.
  17. White H., 1988. Economic prediction using Neural Networks: The case of IBM daily stock returns. In: Proc. of the IEEE International Conference on Neural Networks, pp. 451-458.
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Paper Citation


in Harvard Style

L. Shahpazov V., Doukovska L. and Karastoyanov D. (2014). Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices . In Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD, ISBN 978-989-758-032-1, pages 282-288. DOI: 10.5220/0005427202820288


in Bibtex Style

@conference{bmsd14,
author={Veselin L. Shahpazov and Lyubka A. Doukovska and Dimitar N. Karastoyanov},
title={Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices},
booktitle={Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,},
year={2014},
pages={282-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005427202820288},
isbn={978-989-758-032-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,
TI - Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices
SN - 978-989-758-032-1
AU - L. Shahpazov V.
AU - Doukovska L.
AU - Karastoyanov D.
PY - 2014
SP - 282
EP - 288
DO - 10.5220/0005427202820288