Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relations- hip among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial neural network for forecasting financial data series with use of different algorithms such as backpropagation, radial basis function etc. With their ability of adapting non-linear and chaotic patterns, ANN is the current technique being used which offers the ability of predicting financial data more accurately. "A x-y-1 network topology is adopted because of x input variables in which variable y was determined by the number of hidden neurons during network selection with single output." Both x and y were changed.

ANN Financial forecasting BPA LRN RBF GRNN