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Financial market prediction using feed forward neural network

Publication Type : Journal Article

Publisher : Communications in Computer and Information Science

Source : Communications in Computer and Information Science, Volume 145 CCIS, Mumbai, p.77-84 (2011)

Url :

ISBN : 9783642202087

Keywords : Commerce, decision making, Decision making process, financial forecasting, Financial market, Forecasting, Investments, Market data, minimum risk portfolio of two assets, Network architecture, Neural networks, Optimization, Radial basis function networks, risk free assets, Risky assets, Time step

Campus : Coimbatore

School : School of Business, School of Engineering

Department : Computer Science

Year : 2011

Abstract : pThis paper outlines a methodology for aiding the decision making process for investment between two financial market assets (eg a risky asset versus a risk-free asset or between two risky assets itself), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network has been evaluated. The model is employed for arriving at a decision as to where to invest in the next time step, given data from the current time step. The time step could be chosen on daily/weekly/monthly basis, based on the investment requirement. In this study, the FFNN has yielded good results over RBF. Consequently two such FFNN have been developed to enable us make a decision on investment in the next time step to decide between two risky assets. The prediction made by the two FFNN models has been validated from the actual market data. © 2011 Springer-Verlag./p

Cite this Research Publication : P. Na Kumar, Seshadri, G. Ra, Hariharan, Aa, Mohandas, V. Pb, and Dr. P. Balasubramanian, “Financial market prediction using feed forward neural network”, Communications in Computer and Information Science, vol. 145 CCIS, pp. 77-84, 2011.

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