TITLE

A Methodology for Aiding Investment Decision between Assets in Stock Markets Using Artificial Neural Network

AUTHOR(S)
Kumar, P. N.; Seshadri, G. Rahul; Hariharan, A.; Mohandas, V. P.; Balasubramanian, P.
PUB. DATE
November 2010
SOURCE
International Journal of Computer Science Issues (IJCSI);Nov2010, Vol. 7 Issue 6, p310
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This 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), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network have 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 the FFNN developed enable us make a decision on investment in the next time step between a risky asset (eg the BSE Sensex itself or a single share) versus a riskfree asset (eg Securities like Govt Bonds, Public Provident Funds etc).The FFNN is trained with a set of data which helps in under standing the market behaviour. The input parameters or the information set consisting of six items is arrived at by considering important empirical features acting on real markets. These are designed to allow both passive and active, fundamental and technical trading strategies, and combinations of these. Using just six items simplifies the decision making process by extracting potentially useful information from the large quantity of historic data. The prediction made by the FFNN model has been validated from the actual market data. This model can be further extended to choose between any two categories of assets whose historical data is available.
ACCESSION #
60124664

 

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