A New Approach to Predict Selective Critical Stock Indices Through Artificial Neural Networks and Chaos Theory

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.