Optimized Radial Basis Functional neural network for stock index prediction

Prediction of stock market indices is an interesting and challenging research problem in financial data mining area because movement of stock indices are nonlinear and they are dependent upon different constitutional and extraneous aspects. In this paper we come up with the practice of different techniques of Artificial Neural Network (ANN) in stock market prediction. Here we have selected Multilayer Perceptron model (MLP), Radial Basis Functional Network (RBFN) and an optimized Radial Basis Functional neural network. The proposed model provides an optimized set of center set to Radial Basis Functional Network for experiment and Particle swarm algorithm is used for this boosting process. Lastly in order to verify the best tactics, a comparative result of the applied models is represented.