99
Views
5
CrossRef citations to date
0
Altmetric
Articles

Hybrid support vector machine rule extraction method for discovering the preferences of stock market investors: Evidence from Montenegro

, &

References

  • Andrews, R., Diederich, J., & Tickle, AB (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based Systems, 8, 373–389.
  • Apartsin, Y., Maymon, Y., Cohen, Y., & Singer, G. (2013). Nationality and risk attitude: Testing differences and similarities of investors' behavior in selected financial markets. Global Finance Journal, 24, 114–118.
  • Arun Kumar, M., & Gopal, M. (2010). A hybrid SVM based decision tree. Pattern Recognition, 43, 3977–3987.
  • Barakat, N., & Bradley, AP (2010). Rule extraction from support vector machines: A review. Neurocomputing, 74, 178–190.
  • Barakat, N., & Diederich, J. (2004). Learning-based rule-extraction from support vector machines. In The 14th International Conference on Computer Theory and Applications ICCTA'2004. not found.
  • Barber, BM, & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116, 261–292.
  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. London: CRC press.
  • Cai, C., Ma, Q., & Lv, S. (2012). Research on support vector regression in the stock market forecasting. In Advances in electronic commerce, web application and communication (pp. 607–612). Berlin: Springer.
  • Campbell, J. Y., Ramadorai, T., & Ranish, B. (2013). Getting better: Learning to invest in an emerging stock market. Available at SSRN, 2176222.
  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines ACM transactions on intelligent systems and technology, 2: 27: 1–27: 27, 2011. Retrieved from http://www.csie.ntu.edu.tw/cjlin/libsvm.
  • Chen, G. M., Kim, K. A., Nofsinger, J. R., & Rui, O. M. (2004). Behavior and performance of emerging market investors: Evidence from China. Unpublished Washington State University Working paper (January).
  • Cieslak, D. A., & Chawla, N. V. (2008). Learning decision trees for unbalanced data. In Machine learning and knowledge discovery in databases (pp. 241–256). Berlin: Springer.
  • Covrig, V., Lau, ST, & Ng, L. (2006). Do domestic and foreign fund managers have similar preferences for stock characteristics? A cross-country analysis. Journal of International Business Studies, 37, 407–429.
  • Cronqvist, H., Siegel, S., & Yu, F. (2013). Value versus growth investing: Why do different investors have different styles? Available at SSRN 2351123.
  • Dahlquist, M., & Robertsson, G. (2001). Direct foreign ownership, institutional investors, and firm characteristics. Journal of Financial Economics, 59, 413–440.
  • Delibasic, B., Jovanovic, M., Vukicevic, M., Suknovic, M., & Obradovic, Z. (2011). Component-based decision trees for classification. Intelligent Data Analysis, 15, 671–693.
  • Diederich, J. (2008). Rule extraction from support vector machines: An introduction. In Rule extraction from support vector machines (pp. 3–31). Berlin: Springer.
  • Diederich, J., Tickle, A. B., & Geva, S. (2010). Quo vadis? Reliable and practical rule extraction from neural networks. In Advances in machine learning I (pp. 479–490). Berlin: Springer.
  • Dong, G. M., & Chen, J. (2008, October). Study on support vector machine based decision tree and application. In Fuzzy Systems and knowledge discovery, FSKD'08. Fifth International Conference on (Vol. 5, pp. 318–322). IEEE: Jinan Shandong
  • Fan, R. E., Chen, P. H., & Lin, C. J. (2005). Working set selection using second order information for training support vector machines. The Journal of Machine Learning Research, 6, 1889–1918.
  • Farquad, M. A. H., & Bose, I. (2012). Preprocessing unbalanced data using support vector machine. Decision Support Systems, 53, 226–233.
  • Grinblatt, M., & Keloharju, M. (2001). What makes investors trade? The Journal of Finance, 56, 589–616.
  • Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. The Journal of Finance, 64, 549–578.
  • Grinblatt, M., Keloharju, M., & Linnainmaa, J. (2011). IQ and stock market participation. The Journal of Finance, 66, 2121–2164.
  • Gupta, P., Mehlawat, MK, & Mittal, G. (2012). Asset portfolio optimization using support vector machines and real-coded genetic algorithm. Journal of Global Optimization, 53, 297–315.
  • Hartigan, J. A. (1975). Clustering algorithms. New York, NY: John Wiley & Sons.
  • Jianakoplos, NA, & Bernasek, A. (1998). Are women more risk averse? Economic Inquiry, 36, 620–630.
  • Kang, J. K., & Stulz, R. (1997). Why is there a home bias? An analysis of foreign portfolio equity ownership in Japan. Journal of Financial Economics, 46, 3–28.
  • Kašćelan, Lj, Kašćelan, V., & Jovanović, M. (2014). Analysis of investors' preferences in the Montenegro stock market using data mining techniques. Economic Research-Ekonomska Istraživanja, 27, 463–482. doi:10.1080/1331677X.2014.970451.
  • Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183, 1466–1476.
  • Martens, D., Huysmans, J., Setiono, R., Vanthienen, J., & Baesens, B. (2008). Rule extraction from support vector machines: An overview of issues and application in credit scoring. In Rule extraction from support vector machines (pp. 33–63). Berlin: Springer.
  • Ng, L., & Wu, F. (2006). Revealed stock preferences of individual investors: Evidence from Chinese equity markets. Pacific-Basin Finance Journal, 14, 175–192.
  • Núñez, H., Angulo, C., & Català, A. (2002, April). Rule extraction from support vector machines. In ESANN (pp. 107–112). Bruges (Belgium): d-side publi.
  • Pai, P. F., & Hsu, M. F. (2011). An enhanced support vector machines model for classification and rule generation. In Computational optimization, methods and algorithms (pp. 241–258). Berlin: Springer.
  • Peress, J. (2004). Wealth, information acquisition, and portfolio choice. Review of Financial Studies, 17, 879–914.
  • Quinlan, J. R. (1993). C4. 5: Programs for machine learning (Vol. 1).` San Francisco (CA, USA): Morgan Kaufmann.
  • Rapid Miner User Manual  Retrieved from www.rapidminer.com (accessed 30.01.2014).
  • Setiono, R., Baesens, B., & Martens, D. (2012). Rule extraction from neural networks and support vector machines for credit scoring. In Data mining: Foundations and intelligent paradigms (pp. 299–320). Berlin: Springer.
  • Su, C. T., & Chen, Y. C. (2012). Rule extraction algorithm from support vector machines and its application to credit screening. Soft Computing, 16, 645–658.
  • Tian, Y., Shi, Y., & Liu, X. (2012). Recent advances on support vector machines research. Technological and Economic Development of Economy, 18, 5–33.
  • Tsai, C. F., Lin, Y. C., & Wang, Y. T. (2009). Discovering stock trading preferences by self-organizing maps and decision trees. International Journal on Artificial Intelligence Tools, 18, 603–611.
  • Vapnik, V. (2000). The nature of statistical learning theory. New York: Springer-Verlag.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.