Abstract
In this paper, we have presented a new approach to predict pattern of the financial time series in stock market for next 10 days and compared it with the existing method of exact value prediction [2, 3, and 4]. The proposed pattern prediction technique performs better than value prediction. It has been shown that the average for pattern prediction is 58.7% while that for value prediction is 51.3%. Similarly, maximum for pattern and value prediction are 100% and 88.9% respectively. It is of more practical significance if one can predict an approximate pattern that can be expected in the financial time series in the near future rather than the exact value. This way one can know the periods when the stock will be at a high or at a low and use the information to buy or sell accordingly. We have used Support Vector Machine based prediction system as a basis for predicting pattern. MATLAB has been used for implementation.
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
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Kim, K.: Financial Time Series Forecasting using Support Vector Machines. Neurocomputing 55, 307–319 (2003)
Kim, K., Lee, W.B.: Stock Market Prediction using Artificial Neural Networks with Optimal Feature Transformation. Neural Computing and Application 13, 255–260 (2004)
Ince, H., Trafalis, T.B.: Kernel Principal Component Analysis and Support Vector Machines for Stock Price Prediction. In: Proceedings of IEEE International Joint Conference on Neural Networks, vol. 3, pp. 2053–2058 (2004)
Yu, L., Wang, S., Lai, K.K.: Mining Stock Market Tendency Using GA-based Support Vector Machines. In: Deng, X., Ye, Y. (eds.) WINE 2005. LNCS, vol. 3828, pp. 336–345. Springer, Heidelberg (2005)
Li, W., Liu, J., Le, J.: Using GARCH-GRNN Model to Forecast Financial Times Series. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 565–574. Springer, Heidelberg (2005)
Chen, W.H., Sheh, J.Y., Wu, S.: Comparison of Support Vector Machines and Back Propagation Neural Networks in forecasting the six major Asian Stock Markets. Int. J. Electronic Finance 1(1), 49–67 (2006)
Singhal, N.: Stock Price Prediction for Indian Market, Master, Thesis. Department of Computer Science and Engineering. Indian Institute of Technology, Delhi, India (2008)
National Stock Exchange of India Ltd., Data Products NCFMOnline ReportCBT, http://www.nseindia.com
LS-SVM, package. Home Toolbox Book People Publications Faq Links, www.esat.kuleuven.ac.be/sista/lssvmlab
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaushik, S., Singhal, N. (2009). Pattern Prediction in Stock Market. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-10439-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
Online ISBN: 978-3-642-10439-8
eBook Packages: Computer ScienceComputer Science (R0)