A New Approach to Predict Selective Critical Stock Indices Through Artificial Neural Networks and Chaos Theory
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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2016 by IJCOT Journal | ||
| Volume - 6 Issue - 4 |
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| Year of Publication : 2016 | ||
| Authors : Vinod K, Nasira G M, Haresh M Pandya | ||
| DOI : 10.14445/22492593/IJCOT-V35P303 |
Citation
Vinod K, Nasira G M, Haresh M Pandya"A New Approach to Predict Selective Critical Stock Indices Through Artificial Neural Networks and Chaos Theory", International Journal of Computer & organization Trends (IJCOT), V6(4):50-54 Jul - Aug 2016, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract Financial markets are generally considered as dynamic entities behaving in a random and chaotic manner posing a challenging problem to equity, commodity and currency forecasters. Adoption ofartificial neural network techniques to forecast such financial marketshas been resorted to by many, howeverwith many shortcomings. The present paper proposes a new model to address the above via a synthesis of integration of a live trading system, marketcrash factors and liquidity parameters with the help of chaos theory of physics and financial, technical analysis.
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Keywords
ANN, Chaos theory, Stock Prediction, Integrated model.