Random Articles
Reseach Article
An Efficient Approach to Forecast Indian Stock Market Price and their Performance Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 34 - Number 5 |
| Year of Publication: 2011 |
| Authors: K.K.Sureshkumar, Dr.N.M.Elango |
10.5120/4103-5942
|
K.K.Sureshkumar, Dr.N.M.Elango . An Efficient Approach to Forecast Indian Stock Market Price and their Performance Analysis. International Journal of Computer Applications. 34, 5 ( November 2011), 44-49. DOI=10.5120/4103-5942
Abstract
Forecasting accuracy is the most important factor in selecting any forecasting methods. Research efforts in improving the accuracy of forecasting models are increasing since the last decade. The appropriate stock selections those are suitable for investment is a difficult task. The key factor for each investor is to earn maximum profits on their investments. Numerous techniques used to predict stocks in which fundamental and technical analysis are one among them. In this paper, prediction algorithms and functions are used to predict future share prices and their performance will be compared. The results from analysis shows that isotonic regression function offers the ability to predict the stock prices more accurately than the other existing techniques. The results will be used to analyze the stock prices and their prediction in depth in future research efforts.
References
- Abhyankar, A., Copeland, L. S., & Wong, W. (1997). “Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100”. Journal of Business & Economic Statistics, 15, 1–14.
- Achelis, S.B., Technical analysis from A to Z, IL: Probus Publishing, Chicago, 1995.
- Aiken, M. and M. Bsat. “Forecasting Market Trends with Neural Networks.” Information Systems Management 16 (4), 1999, 42-48.
- Atiya, A. F, E1-Shoura, S. M, Shaheen, S. I and El-Sherif, M. S. “A comparison between neural network forecasting techniques case study: river flow forecasting,” in IEEE Transaction Neural Networks pp. 402-409, 10-2, 1999.
- Brock,.W, Lakonishok.,J, and Lebaron,B, “Simple technical trading rules and the stochastic properties of stock returns”, Journal of Finance, vol. 47, 1992, pp. 1731-1764.
- Carlson, W. L., Thorne, B., “Applied statistical methods”, published by Prentice Hall, Inc., 1997.
- Chang, P.C., Wang, Y. W. and W. N. Yang. “An Investigation of the Hybrid Forecasting Models for Stock Price Variation in Taiwan.” Journal of the Chinese Institute of Industrial Engineering, 21(4), 2004, pp.358-368.
- Chi, S. C., H. P. Chen, and C. H. Cheng. A Forecasting Approach for Stock Index Future Using Grey Theory and Neural Networks. IEEE International Joint Conference on Neural Networks, (1999), 3850-3855.
- Colby, R.W., (2003). The Encyclopedia of Technical Market Indicators. New York: McGraw-Hill.
- National Stock Exchange of Indi details at http://www.nseindia.com/content/us/fact2011_sec1.pdf
- Kimoto, T., and K. Asakawa Stock market prediction system with modular neural network. IEEE International Joint Conference on Neural Network, (1990).1-6.
- Lee, J. W. Stock Price Prediction Using Reinforcement Learning. IEEE International Joint Conference on Neural Networks, (2001). 690-695.
- Mizuno, H., Kosaka, M., Yajima, H. and Komoda N., “Application of Neural Network to Technical Analysis of Stock Market Prediction”, Studies in Informatic and Control, 1998, Vol.7, No.3, pp.111-120.
- Murphy, J.J. Technical analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications, Prentice-Hall, New York, 1986.
- Murphy, J.J. Technical analysis of the financial markets, Prentice-Hall, New York, 1999.
- Pan, H.P. “A joint review of technical and quantitative analysis of the financial markets towards a unified science of intelligent finance”. Proc. 2003 Hawaii International Conference on Statistics and Related Fields, June 5-9, Hawaii, USA, 2003.
- Refenes, AN, Burgess, N and Bentz, Y. “Neural Networks in Financial Engineering: A study in methodology,” in IEEE Trans on Neural Networks, pp. 1222-1267, 8(6), 1997.
- Refenes, Zapranis, and Francis, (1994) Journal of Neural Networks, “Stock Performance Modeling Using Neural Networks: A Comparative Study with Regression Models,” Vol. 7, No. 2, 375-388.
- Steven B. Achelis “Technical Analysis from A-To-Z,” 1st Ed. Vision Books, NewDelhi, 2006.
- Swales, G.S. and Yoon, Y.: “Applying artificial neural networks to investment analysis”. Financial Analysts Journal, 1992, 48(5).
- Voelker.D, Orton.P, Adams.S, “Statistics”, Published by Wiley, 2001.
Index Terms
Keywords