Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Stock Market Prediction using Feed-forward Artificial Neural Network

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 99 - Number 9
Year of Publication: 2014
Authors:
Suraiya Jabin
10.5120/17399-7959

Suraiya Jabin. Article: Stock Market Prediction using Feed-forward Artificial Neural Network. International Journal of Computer Applications 99(9):4-8, August 2014. Full text available. BibTeX

@article{key:article,
	author = {Suraiya Jabin},
	title = {Article: Stock Market Prediction using Feed-forward Artificial Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {99},
	number = {9},
	pages = {4-8},
	month = {August},
	note = {Full text available}
}

Abstract

This paper presents computational approach for stock market prediction. Artificial Neural Network (ANN) forms a useful tool in predicting price movement of a particular stock. In the short term, the pricing relationship between the elements of a sector holds firmly. An ANN can learn this pricing relationship to high degree of accuracy and be deployed to generate profits with sufficiently large amounts of data, preferably in times of low volatility and over a short time period. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed model succeeded in prediction of the trends of stock market with 100% prediction accuracy.

References

  • Teo Jasic, Douglas Wood: Neural network protocols and model performance. Neurocomputing 55(3-4): 747-753 (2003)
  • Chung-Ming Kuan and Tung Liu. December (1995). Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks. Journal of Applied of Econometrics 10 (4): 347-364.
  • Mn Y. LeCun, L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard and W. Hubbard: Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning, IEEE Communication, 41-46, invited paper, November 1989.
  • Min, J. H. , & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert systems with applications, 28(4), 603-614.
  • Najeb Masoud, Predicting Direction of Stock Prices Index Movement Using Artificial Neural Networks:The Case of Libyan Financial Market, British Journal of Economics, Management & Trade, 4(4): 597-619, 2014.
  • Akbilgic, O. , Bozdogan, H. , Balaban, M. E. , (2013) A novel Hybrid RBF Neural Networks model as a forecaster, Statistics and Computing. DOI 10. 1007/s11222-013-9375-7
  • Mitchell, Tom M. "Machine learning. 1997. " Burr Ridge, IL: McGraw Hill 45 (1997).