Stock Price Prediction: Comparison of Arima and Artificial Neural Network Methods - An Indonesia Stock´s Case

Author

Wijaya, Yohanes Budiman ; Kom, S. ; Napitupulu, Togar Alam

Author_Institution

Manajemen Sistem Informasi, Universitas Bina Nusantara, Jakarta, Indonesia

fYear

2010

fDate

2-3 Dec. 2010

Firstpage

176

Lastpage

179

Abstract

Neural Network is a network that resembles a human brain tissue, which may infer a result based on the facts or experience that happened. Many applications have implemented neural network. In this thesis, we compared the stock forecasting result of ANTM (PT Aneka Tam bang) using Artificial Neural Network and ARIMA. ARIMA is a technique of time-series forecasting, which means forecast based on the existing pattern. The results of the study showed that forecasting using Artificial Neural Network method has higher accuracy value than the results with ARIMA method.

Keywords

autoregressive moving average processes; neural nets; stock markets; time series; ARIMA method; Indonesia stock; artificial neural network; autoregressive integrated moving average; human brain tissue; stock forecasting; stock price prediction; time-series forecasting; Artificial neural networks; Biological system modeling; Data models; Forecasting; Instruments; Neurons; Predictive models; ARIMA; Artificial Neural Network; stock forecasting; time-series;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Control and Telecommunication Technologies (ACT), 2010 Second International Conference on

Conference_Location

Jakarta

Print_ISBN

978-1-4244-8746-2

Electronic_ISBN

978-0-7695-4269-0

Type

conf

DOI

10.1109/ACT.2010.45

Filename

5675813