INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.10, No.2, Mar. 2018

Analysis and Prediction of Individual Stock Prices of Financial Sector Companies in NIFTY50

Full Text (PDF, 1062KB), PP.33-41


Views:71   Downloads:7

Author(s)

Vikalp Ravi Jain, Manisha Gupta, Raj Mohan Singh

Index Terms

Neural network;Stock Forecasting;Backpropagation;NIFTY50

Abstract

Prediction of the stock market is currently a big business opportunity for the data analytic solution providers. As the vast range of factors influencing the stock market index are available, it is essential to find the relation between those macroeconomic variables with company share prices and predict the accurate results. Our research is analyzing different relation between the prediction and individual stock prices of financial sector companies in National Stock Exchange 50(NIFTY 50). To make a strong portfolio the selection of different companies is one of the vital decisions we should attempt for a good investment. Trending researches regarding financial forecast are based on the accuracy of the models that how well National Stock Exchange (NSE) index values can be predicted. There is significant literature survey available on the prediction of the stock market as well as its pricing. NIFTY 50 is one of the well-known indexes in India for the investors seeking a good investment. In our research, we attempt, to forecast the stock values of different organizations of Banking and Financial sectors in NIFTY 50. Before including the factors to forecast share market index we are trying to find the relation between different factors and indices of those companies. The study empirically proves that the proposed model is precise to be used in real time stock prediction which can benefit the sellers, investors and stakeholders in their real time savings, investment, and speculation.

Cite This Paper

Vikalp Ravi Jain, Manisha Gupta, Raj Mohan Singh," Analysis and Prediction of Individual Stock Prices of Financial Sector Companies in NIFTY50", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.2, pp. 33-41, 2018. DOI: 10.5815/ijieeb.2018.02.05

Reference

[1]Huang, Wei, Yoshiteru Nakamori, and Shou-Yang Wang. "Forecasting stock market movement direction with support vector machine." Computers & Operations Research 32.10 (2005): 2513-2522.

[2]Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of computational science 2.1 (2011): 1-8.

[3]Baba, Norio, and Motokazu Kozaki. "An intelligent forecasting system of the stock price using neural networks." Neural Networks, 1992. IJCNN.,     International Joint Conference on. Vol. 1. IEEE, 1992.

[4]White, Halbert. "Economic prediction using neural networks: The case of IBM daily stock returns." (1988): 451-458.

[5]White, Halbert. "Economic prediction using neural networks: The case of IBM daily stock returns." (1988): 451-458.

[6]Wang, Jung-Hua, and Jia-Yann Leu. "Stock market trend prediction using ARIMA-based neural networks." Neural Networks, 1996., IEEE International Conference on. Vol. 4. IEEE, 1996.

[7]Kim, Kyoung-jae, and Ingoo Han. "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index." Expert systems with Applications 19.2 (2000): 125-132.

[8]Kuo, Ren Jie, C. H. Chen, and Y. C. Hwang. "An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network." Fuzzy sets and systems 118.1 (2001): 21-45.

[9]Yang, Haiqin, Laiwan Chan, and Irwin King. "Support vector machine regression for volatile stock market prediction." International Conference on Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, 2002.

[10]Schumaker, Robert P., and Hsinchun Chen.  "Textual analysis of stock market prediction using breaking financial news: The AZFin text system." ACM Transactions on Information Systems (TOIS) 27.2 (2009):12.

[11]Enke, David, and Suraphan Thawornwong. "The use of data mining and neural networks for forecasting stock market returns." Expert Systems with applications 29.4 (2005): 927-940.

[12]Bondt, Werner FM, and Richard Thaler. "Does the stock market overreact?." The Journal of finance 40.3 (1985): 793-805.

[13]Fama, Eugene F. "The behavior of stock-market prices.The journal of Business 38.1 (1965): 34-105.

[14]Chen, Nai-Fu, Richard Roll, and Stephen A. Ross. "Economic forces and the stock market." Journal of business (1986): 383-403.

[15]Schwert, G. William. "Why does stock market volatility change over time?." The journal of finance 44.5 (1989): 1115-1153.

[16]Lee, Wayne Y., Christine X. Jiang, and Daniel C. Indro. "Stock market volatility, excess returns, and the role of investor sentiment." Journal of banking & Finance 26.12 (2002): 2277-2299.

[17]Idowu, Peter Adebayo, et al. "Prediction of stock market in nigeria using artificial neural network." International Journal of Intelligent Systems and Applications 4.11 (2012): 68.

[18]Hedr, Ayman E., S. E. Salama, and Nagwa Yaseen. "Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis." International Journal of Intelligent Systems and Applications (IJISA) 9.7 (2017): 22-30.