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Cluster Computing

, Volume 22, Supplement 6, pp 13159–13166 | Cite as

Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach

  • S. Kumar ChandarEmail author
Article
  • 234 Downloads

Abstract

Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1 week, simple moving average for 2 weeks, 14 days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks.

Keywords

Neuro fuzzy approach Technical indicators Stock market prediction ANFIS training method 

References

  1. 1.
    Kim, K.: Financial time series prediction using support vector machine. Neurocomputing 55, 307–319 (2003)CrossRefGoogle Scholar
  2. 2.
    Armano, G., Marchesi, M., Murru, A.: A hybrid genetic-neural architecture for stock indexes forecasting. Int. J. Inf. Sci. 170, 3–33 (2005)MathSciNetGoogle Scholar
  3. 3.
    Kim, H., Shin, K.S., Park, K.: Time delay neural networks and genetic algorithms for detecting temporal patterns in stock markets. Adv. Natl. Computat. 3610, 1247–1255 (2005)CrossRefGoogle Scholar
  4. 4.
    Cao, Q., Leggio, K.B., Schniederjans, M.J.: A comparison between Fema and French’s model and artificial neural networks in predicting the Chinese stock market. Comput. Oper. Res. 32, 2499–2512 (2005)CrossRefGoogle Scholar
  5. 5.
    Hassan, M.R., Nath, B., Kirley, M.: A fusion model of HMM, ANN and GA for stock market forecasting. Expert Syst. Appl. 33, 171–180 (2007)CrossRefGoogle Scholar
  6. 6.
    Yamashita, T., Hirasawa, K., Hu, J.: Multi-branch neural networks and its application to stock price prediction. In: International Conference on Knowledge based Intelligent Information and Engineering, System, vol 3681, pp. 1–7 Springer, Berlin (2005)Google Scholar
  7. 7.
    O’Connor, N., Madden, M.G.: A neural network approach to predicting stock exchange movements using external factors. Knowl. Based Syst. 19(5), 371–378 (2006)CrossRefGoogle Scholar
  8. 8.
    Yildiz, B., Yalama, A., Coskun, M.: Forecasting the Istanbul stock exchange national 100 index using an artificial neural network. World Acad. Sci. Eng. Technol. 2, 10–23 (2008)Google Scholar
  9. 9.
    Tilakaratne, C.D., Mammadov, M. A., Morris, S.A.: Modified neural network algorithms for predicting trading signals of stock market indices. J. Appl. Math. Decision Sci. (2009).  https://doi.org/10.1155/2009/125308 MathSciNetCrossRefGoogle Scholar
  10. 10.
    Akinwale Adio, T., Arogundade, O.T., Adekoya Adebayo, F.: Translated Nigeria stock market prices using artificial neural network for effective prediction. J. Theor. Appl. Inf. Technol. 1, 36–43 (2005)Google Scholar
  11. 11.
    Atsalakis, G.S., Valavanis, K.P.: Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst. Appl. 36(7), 10696–10707 (2009)CrossRefGoogle Scholar
  12. 12.
    Huang, K.Y., Jane, C.J.: A hybrid model stock market forecasting and portfolio selection based on ARX, grey system and RS theories. Expert Syst. Appl. 36, 5387–365392 (2009)CrossRefGoogle Scholar
  13. 13.
    Olaniyi, S.A., Adewole, K.S., Jimoh, R.G.: Stock trend prediction using regression analysis—a data mining approach. AJSS J. 1(4), 154–157 (2010)Google Scholar
  14. 14.
    Wong, Hsien-Lun, Yi-Hsien, Tu, Wang, Chi-Chen: Application of fuzzy time series models for forecasting the amount of Taiwan export. Experts Syst. Appl. 37, 1456–1470 (2010)CrossRefGoogle Scholar
  15. 15.
    Agrawal, S., Jindal, M., Pillai, G.N.: Momentum analysis based stock market prediction using adaptive neuro-fuzzy inference system (ANFIS). Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol 1, pp. 17–19, Hong Kong (2010)Google Scholar
  16. 16.
    Kara, Y., Boyacioglu, M.A., Baykan, Ö.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Syst. Appl. 38, 5311–5319 (2008)CrossRefGoogle Scholar
  17. 17.
    Atsalakis, G.S., Dimitrakakis, E.M., Zopounidis, C.D.: Elliot Wave Theory and neuro-fuzzy systems, in stock market predictions: The WASP system. Expert Syst. Appl. 38(8), 9196–9206 (2011)CrossRefGoogle Scholar
  18. 18.
    Sureshkumar, K.K., Elango, N.M.: An efficient approach to forecast Indian stock market price and their performance analysis. Int. J. Comput. Appl. 34(5), 44–49 (2011)Google Scholar
  19. 19.
    Adebiyi, A.A., Ayo, C.K., Adebiyi, M.O., Otokiti, S.O.: Stock price prediction using neural network with hybridized market indicators. J. Emerg. Trends Comput. Inf. Sci. 3(1), 1–9 (2012)Google Scholar
  20. 20.
    Budhani, N., Jha, C.K., Budhani, S.K.: Application of neural network in analysis of stock market prediction. IJCSET 3, 61–68 (2012)Google Scholar
  21. 21.
    Devadoss, V., Antony, T., Ligori, A.: Stock prediction using artificial neural networks. Int. J. Data Min. Tech. Appl. 2, 283–291 (2013)Google Scholar
  22. 22.
    Wang, Y., Choi, I.C.: Market index and stock price direction prediction using machine learning techniques: an empirical study on the KOSPI and HSI. Science Direct (2014). arXiv:1309.7119
  23. 23.
    Masoud, N.: Predicting direction of stock prices index movement using artificial neural networks: the case of Libyan financial market. Br. J. Econ. Manag. Trade 4(4), 597–619 (2014)CrossRefGoogle Scholar
  24. 24.
    Lai, L., Liu, J.: Support vector machine and least square support vector machine stock forecasting models. Comput. Sci. Inf. Technol. 2(1), 30–39 (2014)Google Scholar
  25. 25.
    Chong, E., Han, C., Park, F.C.: Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst. Appl. 83, 187–205 (2017)CrossRefGoogle Scholar
  26. 26.
    Gu, R.: Multiscale Shannon entropy and its application in the stock market. Phys. A. Stat. Mech. Appl. 484, 215–224 (2017)CrossRefGoogle Scholar
  27. 27.
    Rout, A.K., Dash, P.K., Bisoi, R.: Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J. King Saud Univ. Comput. Inf. Sci. 29(4), 536–552 (2017)CrossRefGoogle Scholar
  28. 28.
    Gu, R., Shao, Y.: How long the singular value decomposed entropy predicts the stock market?—Evidence from the Dow Jones Industrial Average Index. Phys. A. Stat. Mech. Appl. 453, 150–161 (2016)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Caraiani, P.: The predictive power of local properties of financial networks. Phys. A. Stat. Mech. Appl. 466, 79–90 (2017)CrossRefGoogle Scholar
  30. 30.
    Yoshihara, A., Fujikawa, K., Seki, K., Uehara, K.: Predicting stock market trends by recurrent deep neural networks. In: Pacific Rim International Conference on Artificial Intelligence, pp. 759–769, Springer, Cham (2014)Google Scholar
  31. 31.
    Zhong, X., Enke, D.: Forecasting daily stock market return using dimensionality reduction. Expert Syst. Appl. 67, 126–139 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Management StudiesChrist UniversityBengaluruIndia

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