Computational technologies have offered faster and efficient solutions to financial sector. In the financial market, the advancements in computational field have been achieved by the use of neural networks and machine learning that delivered a number of financial tools. Thus, in this thesis, we aim to predict the stock index marketing for the “Dow Jones” index by using deep learning algorithms. We propose a model based on an adaptive NARX neural network to predict the closing price of a moderately stable market. In our model, non-linear auto regressive exogenous input model inserts delays into the input as well as the output acting as memory slots thereby raising the accuracy of the prediction. Moreover, Levenberg-Marquardt algorithm has been used for training the network. The accuracy of the model is determined by the mean squared error. We also used LR model, with the same parameters as NARX, to improve the overall accuracy.