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, broadcasts, radio, newspapers, and websites. We extracted two time series from GDELT, filtered for Saudi Arabian news. The two time series represent 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.
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References
Ogaki, M., Tanaka, S.C.: Behavioral Economics. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6439-5
Elshendy, M., Fronzetti Colladon, A.: Big data analysis of economic news: hints to forecast macroeconomic indicators. Int. J. Eng. Bus. Manag. 9, 1–12 (2017)
Mills, T.C.: Time Series Econometrics. Springer, Cham (2015). https://doi.org/10.1057/9781137525338
Li, X., Shang, W., Wang, S.: Text-based crude oil price forecasting: a deep learning approach. Int. J. Forecast. 35, 1548–1560 (2018)
Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013)
Zhang, G., Xu, L., Xue, Y.: Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Cluster Comput. 20(1), 789–803 (2017)
Fakhrazari, A., Vakilzadian, H.: A survey on time series data mining. In: IEEE International Conference on Electro Information Technology, pp. 476–481 (2017)
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1–34 (2012)
Kunst, R.M., Franses, P.H.: The impact of seasonal constants on forecasting seasonally cointegrated time series. J. Forecast. 17(2), 109–124 (1998)
Pfaff, B.: Analysis of Integrated and Cointegrated Time Series with R. Springer, New York (2008). https://doi.org/10.1007/978-0-387-75967-8
Shanmugam, R., Brockwell, P.J., Davis, R.A.: Introduction to time series and forecasting. Technometrics 39(4), 426 (1997)
Kunst, R.: Multivariate forecasting methods. Building 28–39 (2000). https://homepage.univie.ac.at/robert.kunst/prognos4.pdf
Beran, J.: Mathematical Foundations of Time Series Analysis: A Concise Introduction. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74380-6
Hunter, J., Burke, S.P., Canepa, A.: Multivariate Modelling of Non-Stationary Economic Time Series. Palgrave Macmillan, London (2005)
Elshendy, M., Colladon, A.F., Battistoni, E., Gloor, P.A.: Using four different online media sources to forecast the crude oil price. J. Inf. Sci. 44(3), 408–421 (2018)
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)
Wenjing, S., Huang, Y.: Comparison of multivariate garch models with application to zero-coupon bond volatility. Thesis, pp. 1–55 (2010)
Kartsonakis Mademlis, D., Dritsakis, N.: Volatility between oil prices and stock returns of dow jones index: a bivariate GARCH (BEKK) approach. In: Tsounis, N., Vlachvei, A. (eds.) ICOAE 2018. SPBE, pp. 209–221. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02194-8_16
Bauwens, L., Laurent, S., Rombouts, J.V.K.: Multivariate GARCH models: a survey. J. Appl. Econom. 21(1), 79–109 (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)
Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018)
Leetaru, K., Schrodt, P.A.: GDELT: global data on events, location and tone. International Studies Association (2012)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)
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Alamro, R., McCarren, A., Al-Rasheed, A. (2019). Predicting Saudi Stock Market Index by Incorporating GDELT Using Multivariate Time Series Modelling. In: Alfaries, A., Mengash, H., Yasar, A., Shakshuki, E. (eds) Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science, vol 1097. Springer, Cham. https://doi.org/10.1007/978-3-030-36365-9_26
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