Barapatre Omprakash et.al; International Journal of Advance Research, Ideas and Innovations in Technology © 2018, www.IJARIIT.com All Rights Reserved Page | 916 ISSN: 2454-132X Impact factor: 4.295 (Volume 4, Issue 2) Available online at: www.ijariit.com Stock Price Prediction using Artificial Neural Network Omprakash Barapatre omprakashbarapatre@bitraipur.ac.in Bhilai Institute of Technology Raipur, Chhattisgarh Eric Tete erictete1995@gmail.com Bhilai Institute of Technology Raipur, Chhattisgarh Chandan Lal Sahu chandansahu1303@gmail.com Bhilai Institute of Technology, Raipur, Chhattisgarh Domesh Kumar Domeshnirmalkar8827@gmail.com Bhilai Institute of Technology Raipur, Chhattisgarh Hira Kshatriya hira.cool007@gmail.com Bhilai Institute of Technology Raipur, Chhattisgarh ABSTRACT Stock market prediction is the art of determining the future value of a company stocks. This paper proposes a machine learning (ANN) artificial neural network model to predict stock market price. The proposed algorithm integrates the back – propagation algorithm. The backpropagation algorithm is employed here to training the ANN network model. And in this paper, we have done a research on the TESLA dataset. Keyword: Back-Propagation, Artificial Neural Network, Stock-Market Prediction. 1. INTRODUCTION In the past few decades, prediction of the stock price is gaining more attention as the profitability of the investors in the stock market is mainly depends on the predictability. If the direction of the market is successfully predicted then the investor can yield enough profit. For solving the kind of financial problem the relationship between the input and output is very complex so that’s why we have used ANN for solving or predicting the stock price. An artificial neural network model is computer model whose architecture essentially mimics the learning capability of the human brain. The processing element of artificial neural network resembles the biological structure of the neuron and the internal operation of the human brain.[5] In this paper, Multilayer feed forward back propagation neural network is used for the prediction purpose.[2] Feed forward neural network is a unidirectional connection between the neurons that means the information can flow only in forward direction. Here there is no connection between the neurons present in the same layer. Input has been fed into the first layer and with the help of hidden layers connected to the last layer that produces the output. And since all of the information is constantly feeding forward from one layer to the next hence it is called feed forward network. One of the learning methods in multilayer Perceptron Neural Networks is the error back propagation in which the network learns the pattern in the dataset and justifies the weight of the connections in the inverse direction respect to the gradient vector of error function which is usually regularized sum of square error.[9] The backpropagation method picks a training vector from training data set and moves it from the input layer toward the output layer. In the output layer, the error is calculated and propagated backward so the weight of the connection will be corrected. This will usually go on until the error reaches a pre-defined value. It’s proved that we can approximate any continuous function with a three-layer feedback network with any precision. It should be said that the learning speed will dramatically decrease according to the increase of the number of neurons and layer of the network.