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

Haifa Taha Abd, Ameena Kareem Essa, Firas M. Jassim


The values prediction of the Dow Jones index is essential in the global financial markets systems. The index provides a clear vision of what is happening in the market as a whole. Hence, it offers integrated information on that index to stipulate forecasts characterized as efficient for investors and shareholders. In this study, the ARIMAX model was used to predict the daily Dow Jones index values from 1/1/2020 to 1/ 5/2020 (the spread of COVID-19), considering Brent crude's effect daily prices as an external factor. The Dow Jones Index daily price prediction process went through several stages. The first stage is the time series stationary test phase through the Augmented Dickey-Fuller test. The second stage is achieving stationary by taking the first difference, passing through the stage of identifying the model, and determining the rank based on criteria (AIC), (BIC), (RMSE). The preference of the model was shown in ARIMA (0,1,2) for the Dow Jones index series. The ARIMA (1,1,0) model was shown for crude price Brent series and determining the order of the transfer function of the  ARIMAX model. The comparison stage between the models ARIMA (0,1,2) and ARIMAX(3,1,1)(0,0,1) by residuals scatter plot and (Ljung-Box) test for each model. The results demonstrated the superiority of the ARIMAX model over the ARIMA model. The daily Dow Jones Index values were predicted based on corresponding Brent crude prices according to the ARIMAX (3,1,1) (0,0,1) model. The researchers did not find substantial differences in the index’s behavior, except for a slight decrease in the index's value.


ARIMA; ARIMAX; Dow Jones; Brent Crude: COVID-19.

Full Text:



Elshendy, M., Colladon, A. F., Battistoni, E., & Gloor, P. A. (2018). Using four different online media sources to forecast the crude oil price. Journal of Information Science, 44(3), 408-421.‏

Alamro, R., McCarren, A., & Al-Rasheed, A. (2019, December). Predicting saudi stock market index by incorporating gdelt using multivariate time series modelling. In International Conference on Computing (pp. 317-328). Springer, Cham.‏

Jaraskunlanat, N., & Kijboonchoo, T. (2016). A Study of Factors Affecting the Gold Price in Thailand during 2005–2015. International Research E-Journal on Business and Economics, 2(1).‏

Colladon, A. F., & Scettri, G. (2019). Look inside. Predicting stock prices by analysing an enterprise intranet social network and using word co-occurrence networks. International Journal of Entrepreneurship and Small Business, 36(4), 378-391.‏

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., Yidris, N., & Fattahi, A. (2020). Experimental and numerical investigation of the mechanical behavior of full-scale wooden cross arm in the transmission towers in terms of load-deflection test. Journal of Materials Research and Technology, 9(4), 7937-7946.‏

Clements, A. E., & Todorova, N. (2016). Information flow, trading activity and commodity futures volatility. Journal of Futures Markets, 36(1), 88-104.‏

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., & Yidris, N. (2020). Conceptual design of the cross-arm for the application in the transmission towers by using TRIZ–morphological chart–ANP methods. Journal of Materials Research and Technology, 9(4), 9182-9188.‏

Padhi, S. S., & Pati, R. K. (2017). Quantifying potential tourist behavior in choice of destination using Google Trends. Tourism Management Perspectives, 24, 34-47.‏

Ortega, L., & Khashanah, K. (2014). A neuro‐wavelet model for the short‐term forecasting of high‐frequency time series of stock returns. Journal of Forecasting, 33(2), 134-146.

Sharaf, H. K., Salman, S., Dindarloo, M. H., Kondrashchenko, V. I., Davidyants, A. A., & Kuznetsov, S. V. (2021). The effects of the viscosity and density on the natural frequency of the cylindrical nanoshells conveying viscous fluid. The European Physical Journal Plus, 136(1), 1-19.

Aras, H. (2015). Modelling the Gold Price in Turkish Free Market: Static Approach. Zeszyty Naukowe Uczelni Vistula, (41 (3)/2015 Stosunki Międzynarodowe), 103-121.‏

Sharaf, H. K., Salman, S., Abdulateef, M. H., Magizov, R. R., Troitskii, V. I., Mahmoud, Z. H., ... & Mohanty, H. (2021). Role of initial stored energy on hydrogen microalloying of ZrCoAl (Nb) bulk metallic glasses. Applied Physics A, 127(1), 1-7.‏

Todorova, N., & Clements, A. E. (2018). The volatility-volume relationship in the LME futures market for industrial metals. Resources Policy, 58, 111-124.‏

Jammalamadaka, S. R., Qiu, J., & Ning, N. (2019). Predicting a stock portfolio with the multivariate bayesian structural time series model: Do news or emotions matter?. International Journal of Artificial Intelligence, 17(2), 81-104.‏

Hajirahimi, Z., & Khashei, M. (2021). Parallel hybridization of series (PHOS) models for time series forecasting. Soft Computing, 25(1), 659-672.‏



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development