Predicting Saudi Stock Market Index by Incorporating GDELT Using Multivariate Time Series Modelling

Abstract

Prediction of financial and economic markets is very challenging but valuable for economists, business owners, and traders. Forecasting stock market prices depends on many factors, such as other markets' performance, economic state of a country, and others. In behavioral finance, people's emotions and opinions influence their transactional decisions and therefore the financial markets. The focus of this research is to predict the Saudi Stock Market Index by utilizing its previous values and the impact of people's sentiments on their financial decisions. Human emotions and opinions are directly influenced by media and news, which we incorporated by utilizing the Global Data on Events, Location, and Tone (GDELT) dataset by Google. GDELT is a collection of news from all over the world from different types of media such as TV, broad-casts, radio, newspapers, and websites. We extracted two time series from GDELT, filtered for Saudi Arabian news. The two time series rep-resent daily values of tone and social media attention. We studied the characteristics of the generated multivariate time series, then deployed and compared multiple multivariate models to predict the daily index of the Saudi stock market.

Analyzing the Relationship between the Dow Jones Index and Oil Prices Using the ARIMAX Model

“…: White noise, which is a time series that includes other effects on the series , It is a series independent of the series . There are some difficulties with the ARIMAX model defined in formula (2) is that the conversion function may contain an unlimited number or a large number of parameters. The number of parameters of the input and output series parameters is specified while another formula for the ARIMAX model is being developed.…”

“…However, the test results indicated that the time series reach the stationary stage after Taking the first differences of both series, as the value of the significance levels is less than (0.05). The output and input series are drawn after taking the first differences to confirm stationary, as shown in Figure (2). Furthermore, the ACF coefficients for the two series are plotted in Figure (3)…”

“…This study forecasts the sales of the Central Bank of Iraq from foreign currency using the ARIMAX (2,0,0) (0,0,2) that depends on the demand gap of the currency. Some previous studies addressed the ARIMAX model in a study regarding the forecasting of temperatures for the city of Baghdad in the presence of wind speed as a series of inputs [1], [2]. Jaraskunlanat and Kijboonchoo [3] compared the ARIMAX (1,0,0) (0,0,2) and the method of neural networks.…”

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

“…In fact, few scholars have discussed how to predict the Tadawul index (TASI) using some specific factors [12,[45][46][47]. Alsufyani and Sarmidi [48] found there is no relation between commodity energy price and TASI volatility.…”

“…The reasons are that the interactions of multi-factors walk in line and across each other, which makes the goal of reaching a proper estimate truly challenging. Market economic factors, interaction between stock markets, and international variables are some instances of these concerned factors [12]. Unsurprisingly, specific movements of market indices may influence investors to buy, sell, or hold, especially if these occurrences take place at the right time.…”