Stock price forecasting using artificial neural network: (Case Study: PT. Telkom Indonesia)

Abstract

Investment in stocks is one of the best alternatives for investing the assets, because with a stake of exposure to risk of inflation is smaller when compared to the savings. But the problem is the difficulty prospective shareholders in stock options because they do not know the stock price predictions for the future. This resulted in the growing level of loss if there are errors in determining the decisions taken in regard to these shares. To solve these problems, required future stock price prediction by using the method of forecasting. Forecasting is done by using Artificial Neural Network (ANN) and for the training, Backpropagation algorithm is used. In this study, stock price prediction using neural network with backpropagation algorithm. ANN is used due to have the ability to perform activities based on past data, where the data of the past will be studied so as to have the ability to give a decision on the data that has never been studied. By using Backpropagation algorithms, network architectures are trained to get the best architecture. After the training, the best architecture that is obtained is 8: 9: 1. Then, the test carried out on test data using the best network architecture and found that the mean squared error (MSE) is equal to 0.1830.