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Developing a Computational Model for Pricing Index Future

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 5
Year of Publication: 2012
Shom Prasad Das
Prabhat Kumar Tripathy
Upendra Kumar Panda

Shom Prasad Das, Prabhat Kumar Tripathy and Upendra Kumar Panda. Article: Developing a Computational Model for Pricing Index Future. International Journal of Computer Applications 55(5):1-6, October 2012. Full text available. BibTeX

	author = {Shom Prasad Das and Prabhat Kumar Tripathy and Upendra Kumar Panda},
	title = {Article: Developing a Computational Model for Pricing Index Future},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {5},
	pages = {1-6},
	month = {October},
	note = {Full text available}


Forecasting in financial market has been difficult task due to its non linearity and high volatility. Effective modeling of forecasting is a major concern for financial market participants. Artificial Neural Network (ANN) is a statistical technique under the non linear regression model. ANNs do consider the non parametric aspects like semantics, emotions for the calculation of volatilities. A Support Vector Machines (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis SVMs are a very specific type of learning algorithms that uses the kernel functions. Training SVMs is equivalent to solving a linearly constrained quadratic programming problem so that the solution of SVMs is always unique and globally optimal. In this paper, we have focused on some techniques of soft computing such as Neural Network and Support Vector Machines to predict the future pricing. At last we have compared the output of these two models and found which model gives the optimal solution. This computational model can be widely used to find out the future volatility of share in share market.


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