Performance Forecasting of National Stock Exchange of India Using Symbolic Versus Numerical Methodology

  • Sachin KamleyEmail author
  • Shailesh Jaloree
  • Ramjeevan Singh Thakur
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


Though stock market holds instability in nature and due to instability, its knowledge representation and forecast procedure is exceptionally complicated. For the previous couple of decades, various tools and techniques are developed to forecast symbolic and numerical stock prices. However, these tools and techniques have been totally failed to make inferences from stock market knowledge base. During this direction, backward reasoning and back-propagation methods are adopted for symbolic and numerical forecasting, respectively. This study mainly highlights the performance comparison of both the approaches. The common LISP 3.0 and MATLAB R2011a tool are used to make inferences from stock market knowledge base. Finally, experimental results had shown that back-propagation method performs significantly well as compared to backward reasoning method.


Stock market Artificial intelligence Backward reasoning Back-propagation Common LISP 3.0 MATLABR2011a 


  1. 1.
    Brown, D.P., Jennings, R.H.: On technical analysis. Rev. Financ. Stud. 2(4), 527–551 (1989)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Velumoni, D., Rau, S.S.: Cognitive intelligence based expert system for predicting stock markets using prospect theory. Indian J. Sci. Technol. 9(10), 1–6 (2016)CrossRefGoogle Scholar
  3. 3.
    Rich, E., Knight, K.: Artificial Intelligence, 3rd edn. McGraw-Hill, New York (1991)Google Scholar
  4. 4.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Englewood Cliffs, NJ (2009)zbMATHGoogle Scholar
  5. 5.
    Dutta, G., Jha, P., Laha, A.K., Mohan, N.: Artificial neural network models for forecasting stock price index in the Bombay stock exchange. J. Emerg. Market Finan. 5(3), 283–295 (2006)CrossRefGoogle Scholar
  6. 6.
    El-Hammady, A.I., Abo-Rizka, M.: Neural network based stock market forecasting. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 11(8), 204–212 (2011)Google Scholar
  7. 7.
    Zarandi, F.M., Neda, M.H., Bastani, S.: A fuzzy rule based expert system for evaluating intellectual capital. Adv. Fuzzy Syst. 12, 1–11 (2012)Google Scholar
  8. 8.
    Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42(4), 2162–2172 (2015)CrossRefGoogle Scholar
  9. 9.
    Mohamed, T., Gayar, N.E., Atiya, A.F.: Forward and backward forecasting ensembles for the estimation of time series missing data. Artif. Neural Netw. Pattern Recogn. (ANNPR) 8774, 93–104 (2014)Google Scholar
  10. 10.
    Oliveira, F.A., Nobre, E.N., Zarate, L.E.: Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index—case study of PETR4, Petrobas, Brazil. Expert Syst. Appl. 40, 7596–7606 (2013)CrossRefGoogle Scholar
  11. 11.
    Tsai, C.F., Wang. S.P.: Stock price forecasting by hybrid machine learning techniques. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists (IMECS), vol. 1. Hong Kong, 18–20 March 2009Google Scholar
  12. 12.
    Bola, A.A., Adesola, A.G., Olusayo, O.E., Adebisi, A.A.: Forecasting movement of the nigerian stock exchange all share index using artificial neural and Bayesian networks. J. Finan. Invest. Anal. 2(1), 41–59 (2013)Google Scholar
  13. 13.
    Kamley, S., Jaloree, S., Thakur, R.S.: Performance comparison between forward and backward chaining rule based expert system approaches over global stock exchanges. Int. J. Comput. Sci. Infor. Secur. (IJCSIS) 14(3), 74–81 (2016)Google Scholar
  14. 14.
    Markic, B., Tomic, D., Pavlovic, I.: An expert system approach in stock selection attractive for investment. In: 13th International Research/Expert Conference, Trends in the Development of Machinery and Associated Technology, pp. 297–300. Hammamet, Tunisia 16–21 (2009)Google Scholar
  15. 15.
    Burney, S.M.A., Mahmood, N.: A brief history of mathematical logic and applications of logic in CS/IT. J. Sci. 34(1), 61–75 (2006)Google Scholar
  16. 16.
    Online Stock Market Dataset Available on Yahoo Finance Site.
  17. 17.
    Tiwari, V., Tiwari, V., Gupta, S., Tiwari, R.: Association rule mining: a graph based approach for mining frequent itemsets. In: International Conference on Networking and Information Technology (ICNIT), pp. 309–313. IEEE (2010) Google Scholar
  18. 18.
    Online Macroeconomic Variables Data Available on Site.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sachin Kamley
    • 1
    Email author
  • Shailesh Jaloree
    • 2
  • Ramjeevan Singh Thakur
    • 3
  1. 1.S.A.T.IVidishaIndia
  2. 2.Department of Appl. Math’s and C.ScS.A.T.IVidishaIndia
  3. 3.Department of Computer ApplicationsMaulana Azad National Institute of TechnologyBhopalIndia

Personalised recommendations