Modeling stock prices using mixture autoregressive model

Telecommunication has been being a need for a wide community that cannot be avoided. The development of communication technology users in Indonesia causes the movement of the development of information technology from a secondary or tertiary need to be a primary need. The increasing of the needs of communication in the community makes these stocks being the largest capital stocks. So that it makes community interest to invest in the telecommunication factory. The closing price of this stocks somehow changing form the high prices switch to the low prices or vice versa. The closing price fluctuation could cause the behavior of stock prices to emerge to a multi-modal pattern. Frequently it would hard to perform a time series model because of its multi-modal characteristics in its serial data. This paper demonstrates the success of the work of the Mixture Autoregressive (MAR) modeling to overcome the multi-modality of some of the serial telecommunication stock price data and compare its performance with the Autoregressive Integrated Moving Average (ARIMA) modeling based on the smaller Mean Square Error (MSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC).