Predicting Saudi stock market Index by Incorporating GDELT using multivariate time series modelling
Alamro, Rawan, McCarren, AndrewORCID: 0000-0002-7297-0984 and Al-Rasheed, Amal
(2019)
Predicting Saudi stock market Index by Incorporating GDELT using multivariate time series modelling.
In: International Conference on Computing (ICC 2019), 10-12 Dec 2019, Riyadh, Saudi Arabia.
ISBN 978-3-030-36364-2
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.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Forecasting; Multivariate time series; Behavioral finance; Time series analysis
Advances in Data Science, Cyber Security and IT Applications. Communications in Computer and Information Science
1097(1).
Springer. ISBN 978-3-030-36364-2