Abstract
In this study we combine support vector machine (SVM) and windowing operator in order to predict share market trend as well as the share price. The instability of the time series data is one of the main reasons to lead to decrease of prediction accuracy in this analysis. On the other hand, some special SVM parameters such as c, ε, g should be carefully determined to gain high accuracy. In order to solve this problem mentioned above we use windowing operator as preprocess in order to feed the highly reliable input to SVM model. And train the model in iterative process such that we can find out the best combination of SVM parameters. This study is done on some listed company of Dhaka stock exchange (DSE), Bangladesh. And the training and testing data sets are real time values are collected from DSE. Four years historical data (2009-2012) are used in this analysis. And finally, we compare the output with the real time trend from DSE.
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
Kim, K.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Ince, H., Trafalis, T.B.: Kernel Principal Component Analysis and Support Vector Machines for Stock Price Prediction, pp. 2053–2058 (2004) 0-7803-8359-1/04/2004 IEEE
Lucas, K., Lai, C., James, N., Liu, K.: Stock Forecasting Using Support Vector Machine. In: Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, pp. 1607–1614 (2010)
Lu, C., Chang, C., Chen, C., Chiu, C., Lee, T.: Stock Index Prediction: A Comparison of MARS, BPN and SVR in an Emerging Market. In: Proceedings of the IEEE IEEM, pp. 2343–2347 (2009)
Kannan, K.S., Sekar, P.S., Sathik, M.M., Arumugam, P.: Financial Stock Market Forecast using Data Mining Techniques. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, pp. 555–559 (2010)
Hu, Y., Pang, J.: Financial crisis early warning based on support vector machine. In: International Joint Conference on Neural Networks, pp. 2435–2440 (2008)
Chen, K., Ho, C.: An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting. In: The IEEE International Conference on Neural Networks and Brain, pp. 1633–1638 (2005)
Xue-Shen, S., Zhong-Ying, Q., Da-Ren, Y., Qing-Hua, H., Hui, Z.: A Novel Feature Selection Approach Using Classification Complexity for SVM of Stock Market Trend Prediction. In: 14th International Conference on Management Science & Engineering, pp. 1654–1659 (2007)
Debasish, B., Srimanta, P., Dipak, C.P.: Support Vector Regression. Neural Information Processing – Letters and Reviews 11(10), 203–224 (2007)
Hsu, C., Chang, C., Lin, C.: A Practical Guide to Support Vector Classification. Initial version (2003) Last updated version (2010)
Thissena, U., Brakela, R., Weijerb, A.P., Melssena, W.J., Buydensa, L.M.C.: Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69, 35–49 (2003)
Cao, L.: Support vector machines experts for time series forecasting. Neurocomputing 51, 321–339 (2003)
Alex, J., Bernhard, S.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meesad, P., Rasel, R.I. (2013). Dhaka Stock Exchange Trend Analysis Using Support Vector Regression. In: Meesad, P., Unger, H., Boonkrong, S. (eds) The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Advances in Intelligent Systems and Computing, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37371-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-37371-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37370-1
Online ISBN: 978-3-642-37371-8
eBook Packages: EngineeringEngineering (R0)