Advertisement

Comparative Analysis of Impact of Various Global Stock Markets and Determinants on Indian Stock Market Performance - A Case Study Using Multiple Linear Regression and Neural Networks

  • Avinash Pokhriyal
  • Lavneet Singh
  • Savleen Singh
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)

Abstract

Globalization and technological advancement has created a highly competitive market in the stock and share market industry. Performance of the industry depends heavily on the accuracy of the decisions made at performance level. The stock market is one of the most popular investing places because of its expected high profit. For prediction, technical analysis approach, that predicts stock prices based on historical prices and volume, basic concepts of trends, price patterns and oscillators, is commonly used by stock investors to aid investment decisions. In recent years, most of the researchers have been concentrating their research work on the future prediction of share market prices by using Statistical & Quantitative tools. But, in this paper we newly propose a methodology in which the Multiple Linear Regression and neural networks is applied to the investor’s financial decision making to invest all type of shares irrespective of the high / low index value of the scripts, in a continuous time frame work. The proposed network has been tested with stock data obtained from the Asian Stock Market Database. Finally, the design, implementation and performance of the proposed multiple linear regression and model of simulated neural network are described.

Keywords

Stock Market Performance Multiple Linear Regression NIFTY 50 Artificial Neural Networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aiken, M.: Using a Neural Network to Forecast Inflation. Industrial Management & Data Systems 99(7), 296–301 (1999)CrossRefGoogle Scholar
  2. 2.
    Athanasoglou, P.P., Brissimis, S.N., Delis, M.D.: Bank-specific, Industry-specific and Macroeconomic Determinants of Bank Profitability. International Financial Markets, Institutions & Money 18, 121–136 (2008)CrossRefGoogle Scholar
  3. 3.
    Murphy, J.J.: Technical Analysis of Financial Markets – A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance (1999)Google Scholar
  4. 4.
    Boritz, J.E., Kennedy, D.B.: Effectiveness of Neural Network Types for Prediction of Business Failure. Expert Systems with Applications 9(4), 503–512 (1995)CrossRefGoogle Scholar
  5. 5.
    Brunell, P.R., Folarin, B.O.: Impact of Neural Networks in Finance. Neural Computation & Application 6, 193–200 (1997)CrossRefGoogle Scholar
  6. 6.
    Han, J., Lu, H., Feng, L.: Stock Movement Prediction and N dimensional Inter-Transaction Association Rules. In: Proc. of 1998 SIGMOD 1996 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 1998), Seattle, Washington, June 1998, pp. 12:1-12:7 (1998)Google Scholar
  7. 7.
    Refenes, A.N., Zapranis, A., Francis, G.: Stock Performance Modeling Using Neural Networks: A Comparative Study with Regression Models. Neural Networks 7(2), 375–388 (1994)CrossRefGoogle Scholar
  8. 8.
    Chokmani, K.T., Quarda, J.V., Hamilton, S., Hosni, G.M., Hugo, G.: Comparison of Ice-Affected Streamflow Estimates Computed Using Artificial Neural Networks and Multiple Regression Techniques. Journal of Hydrology 349, 383–396 (2008)CrossRefGoogle Scholar
  9. 9.
    Mender, B.: Introduction to Probability and Statistics, 9th edn. International Thomson Publishing (1994)Google Scholar
  10. 10.
    Tang, Z., Almeida, C., Fishwick, P.A.: Simulation: Time series forecasting using neural networks vs. Box-Jenkins methodology, pp. 303–310 (1991)Google Scholar
  11. 11.
    Morgan, S.: Neural Networks and Speech Processing. Kluwer Academic Publishers, Dordrecht (1991)CrossRefzbMATHGoogle Scholar
  12. 12.
    Roman, J., Jameel, A.: Backpropagation and Recurrent Neural networks in Financial Analysis of MultipleStock Market Returns. In: Proceedings of the 29th Annual Hawaii International Conference on System Sciences (1996)Google Scholar
  13. 13.
    Rumelhart, D.E., McClelland, J.L.: PDP Research @OUP: Parallel Distributed Processing Volume: Foundations, The Massachusetts Institute of Technology (1988) Google Scholar
  14. 14.
    Wicirow, Rumelhart: L&R: Journal of Communications of the ACM. Neural Networks: Applications in Industry, Business and Science 37(3), 93–105 (1994)Google Scholar
  15. 15.
    Gately, E.: Neural Networks for Financial Forecasting. Wiley, New York (1996)Google Scholar
  16. 16.
    Haron, S.: Determinants of Islamic Bank Profitability. Global Journal of Finance & Economics 1(1), 11–33 (2005)MathSciNetGoogle Scholar
  17. 17.
    Leshno, M., Spector, Y.: Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing 10, 125–147 (1996)CrossRefGoogle Scholar
  18. 18.
    Nguyen, N., Cripps, A.: Predicting Housing Value: A Comparison of Multiple Linear Regression Analysis and Artificial Neural Networks. Journal of Real Estate Research 22(3), 313–336 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Avinash Pokhriyal
    • 1
  • Lavneet Singh
    • 1
  • Savleen Singh
    • 1
  1. 1.Management & Computer ApplicationsR.B.S CollegeAgra

Personalised recommendations