Future Stock Prediction Using Pattern Multilayer Recurrent ANN Backtrack Solver

Abstract

Stock price prediction is always a most challenging task. Stock price prediction helps in identifying the decision before investing on different companies. In nature, stock market is multidimensional. Stock market price prediction is necessary for getting profit and investment of companies. The various attributes related in change of market price values are economic, political, and human. Many intelligent networks are available to predict the price. Still there is a need a for new prediction to optimize the stock index price. Artificial Neural Network (ANN) has been applied in many different domains with success. ANN generalized and applied in learned base of example. ANN helps the better prediction to forecast the closing stock price. Neural network offers the capacity to determine the outlines in market prediction. ANN prediction clears the stock price forecasting challenge by forming the training set. ANN techniques are used to form the prediction of different variables. ANN is one of the best techniques used for analysing the historical dataset. Historical information in the network input is used to get the expected output of the network. This approach advances in predicting the best future stock price by forming training and testing set. This study uses the ANN model to train and forecast the stock market price to help the investors to choose better time for buying or selling the stocks. The imminent ways for relating the neural network to the stock market helps to predict the stock. Knowledge set is formed with the historical data using multilayer perceptron to get the optimal prediction as the expected result. In our research work, dataset is collected from BSE Sensex of life insurance company (HDFC) is taken for prediction. The stock price of 7 years is considered in this research. These techniques have been used to examine the stock to predict the closing price. Knowledge set is applied for selecting the result and training set. Knowledge set using multilayer neural network will predict the accurate clos