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.

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Financial time series forecast has been eyed as key standard job because of its high non-linearity and high volatility in data. Various statistical methods, machine learning and optimization algorithms has been widely used for forecasting time series of various fields. To overcome the problem of solution trapping in local minima, here in this paper, we propose novel approach of financial time series forecasting using simulated annealing and threshold acceptance genetic back propagation network to obtain the global minima and better accuracy. Time series dataset is normalized and bifurcated into training and test datasets, which is used as supervised learning in BPA artificial neural network and optimized with genetic algorithm. Results thus obtained are used as seed for start point of simulated annealing and threshold acceptance. Empirical results obtained from proposed approach confirm the outperformance of forecast results than conventional BPA artificial neural networks.