Stock Price Forecasting of E-Commerce Company Using Feedforward Backpropagation Neural Network

Ardana, Putu Doddy Heka and Sumarda, Gede and Astariani, Ni Kadek and Sudika, I Gusti Made (2019) Stock Price Forecasting of E-Commerce Company Using Feedforward Backpropagation Neural Network. International Journal of Advanced Trends in Computer Science and Engineering, 8 (1.5). pp. 221-226. ISSN 2278-3091

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Abstract

Stock price forecasting is widely known and has become a special issue in financial and scholarly research. This stock price movement trends to be non-linear and non-stationary which was influenced by many factors. The stock price forecasting involves a lot of processes because in it there is a lot of noise and the conditions are always changing. In recent years, the development of e-commerce has increased rapidly. E-commerce companies have been entered to the stock or trading market where one of them is Kioson Komersial Indonesia company with the KIOS stock code. In this study, stock price predictions investigated by using Matlab version R2018b software using the Feedforward Backpropagation Neural Network method. The results of the study proved that using artificial neural networks can predict the price of a stock whose price is very close to the actual price with a very small mean square error (MSE) value and high correlation coefficient parameter. Key words : Stock price, Forecasting, E-commerce, Feedforward backpropagation neural network, Prediction

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