Abstract
Stock trade is a popular investing activity and during this activity, investors expect to gain higher profit with lower risk. Therefore, the problem of predicting stock returns has been an important issue for many years. This study is aimed on the discover relationship between financial data of public companies and return on investment by using data mining technology. The study propose a stock selective system by using hybrid models of classification. Use the hybrid models of association rules, cluster, and decision tree, it can provide meaningful decision rules for stock selection for intermediate- or long-term investors. Further, these rules are use to select some profitable stocks of the following years. The outcome evidences the higher return on investment in proposed model than general market average.
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
Abraham, A., Nath, B., Mahanti, P.K.: Hybrid Intelligent Systems for Stock Market Analysis. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS 2001. LNCS, vol. 2074, pp. 337–345. Springer, Heidelberg (2001)
Huang, C.L., Tsai, C.Y.: A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert System with Applications 36(2), 1529–1539 (2009)
Chang, P.C., Liu, C.H.: A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Application 34(1), 135–144 (2008)
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)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, SH. (2013). A Stock Selective System by Using Hybrid Models of Classification. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-36546-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36545-4
Online ISBN: 978-3-642-36546-1
eBook Packages: Computer ScienceComputer Science (R0)