The Predictability of the Amman Stock Exchange using the Univariate Autoregressive Integrated Moving Average (ARIMA) Model

Abstract

This study examines the univariate ARIMA forecasting model, using the Amman Stock Exchange (ASE) general daily index between 4/1/2004 and 10/8/2004; with out-of-sample testing undertaken on the following seven days. Different diagnostic tests were performed to find the best model describing the data. The selected model predicted that the ASE would continue to grow by 0.195% for seven days starting on 11/8/2004. This forecast, however, was not consistent with actual performance during the period of the prediction (11/8/2004 -19/8/2004) since ASE declined by -0.003% assuring the fact that ASE followed most closely the Efficient Market Hypothesis (EMH) in its weak form.

Temporal Modelling of Producer Price Inflation Rates of Ghana

“…Different diagnostic tests used to perform the best fitted model and showed that the selected model is suitable for forecasting on ASE [7]. Another research based on ARIMA model has done by Nochai et al They investigated to find a model to forecast three types of oil palm price in Thailand such as Farm price, Wholesale price and pure oil price.…”

Dynamic Forecasting method for Shariah-compliant Share Price of Healthcare sector in Malaysian Stock Exchange

“…Their finding is important to economists and researchers in order to understand the dynamic behavior of currency movement. However, study from Al-Shiab (2016) [6] …”

“…Past study have discussed a number of volatility forecasting model such as ARIMA (Al-Shiab, 2016) [6], GARCH (Luo et al, 2010) [7] and moving average (Abu Bakar and Rosbi, 2016) [8].…”

Modelling the Pattern of Monetary Policy Rates of Ghana

“…Different diagnostic tests used to perform the best fitted model and showed that the selected model is suitable for forecasting on ASE [13]. Another research based on ARIMA model has done by Nochai et al They investigated to find a model to forecast three types of oil palm price in Thailand such as Farm price, Wholesale price and Pure oil price.…”

“…Non-seasonal Box Jenkins methodology is used and three models are found based on the minimum of mean absolute percentage error (MAPE). Finally they developed model for three types of palm oil price and found that models ARIMA (2,1,0) for the farm price, ARIMA (1,0,1) for the wholesale price, and ARIMA (3,0,0) for the pure oil price [13].…”