The Potential Influence of COVID-19 on the Arab World Economy

Authors

  • Ahmed N.K. Alfarra Harbin Institute of Technology, China
  • Ahmed Hagag Benha University, Egypt

Keywords:

COVID-19, Forecasting, LSTM, RNN, ARIMA Model

Abstract

This paper predicts Coronavirus Disease (COVID-19)'s potential influence on the Arab country's economy by using two predicting models: the Autoregressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) model. The World Bank offers data of the Arab countries' Gross Domestic Product (GDP) over the period 1968-2019.  As we show up at the pinnacle of the COVID-19 pandemic, quite possibly the most critical inquiry going up against us is: what is the potential impact of the pandemic on the rate of GDP in Arab countries during the pandemic period? LSTM is recurrent neural networks (RNN), which are competent in understanding temporal dependencies. Therefore, the model based on LSTM achieved a great fit with the real data, which is what made us rely on its results more than the ARIMA model. The results of the LSTM model showed that the COVID-19 pandemic caused a decrease in GDP by approximately 17.22% and 5.41% in 2020 and 2021, respectively, with respect to the real GDP announced by the World Bank. In addition, we trained the LSTM-based model on real data from 1968 to 2020 and predicted the GDP growth rate in the next five years until 2025. Thus, what is certain now is that the Arab world states have to encounter the challenges presented by the current ecosystem. Transition to digital economy is needed, additional volume of data with high-level accuracy is required to improve precise and robust models to attain projections with a reduced amount of margin of error.

References

Agarwal, A., A. Alomar, A. Sarker, D. Shah, D. Shen, and C. Yang. "Two Burning Questions on COVID-19: Did Shutting Down the Economy Help? Can We (Partially) Reopen the Economy without Risking The Second Wave?" In Papers (2005.00072; Papers) arXiv.org (2020). https://ideas.repec.org/p/arx/papers/2005.00072.html.

Alfarra, A.N., and H. Xiaofeng. "Basel III, and Banking Risk: Do Basel III Factors Could Predict the Risk of Middle-Eastern Countries?" European Journal of Business and Management 10, no. 27 (2018): 1-9.

______, H. Xiaofeng, A. Hagag, M.A. Eissa. "Potential Influence of Information Systems on Bank Risk." IAENG International Journal of Computer Science 44, no. 2 (2017): 188-96.

______, and A. Hagag. "Forecasting of the American Digital Economy Using ARIMA Model." International Conference on Electronic Engineering (ICEEM): Egypt, 2021.

ArunKumar, K.E., D. Kalaga, C. Kumar, M. Kawaji, and M. Timothy. "Comparative Analysis of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) Cells, Autoregressive Integrated Moving Average (ARIMA), seasonal Autoregressive Integrated Moving Average (SARIMA) for Forecasting COVID-19 trends." Alexandria Engineering Journal Brenza 61, no. 10 (2022): 7585-603.

Awodumi, O.B., and A.O. Adewuyi. "The Role of Non-Renewable Energy Consumption in Economic Growth and Carbon Emission: Evidence from Oil Producing Economies in Africa." Energy Strategy Reviews 27 (2020): 100434.

Borovykh, A., S. Bohte, and C.W. Oosterlee. "Dilated Convolutional Neural Networks for Time Series Forecasting." Journal of Computational Finance, Forthcoming 22, no. 4 (2019): 73-101.

Büyük?ahin, Ü.Ç., and ?. Ertekin. "Improving forecasting Accuracy of Time Series Data Using a New ARIMA-ANN hybrid Method and Empirical Mode Decomposition." Neurocomputing 361 (2019): 151-63.

Choi, H.K. "Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model." arXiv preprint arXiv: 1808.01560 (2018).

Erdmann, A., and J.M. Ponzoa. "Digital Inbound Marketing: Measuring the Economic Performance of Grocery E-Commerce in Europe and the USA." Technol Forecast Soc Change 162 (2021): 120373.

Fischer, T., and C. Krauss. "Deep learning with Long Short-Term Memory Networks for Financial Market Predictions." European Journal of Operational Research 270, no. 2 (2018): 654-69.

Box, G.E., G.M. Jenkins, G.C. Reinsel, and G.M. Ljung. Time Series Analysis: Forecasting and Control. New Jersey: John Wiley & Sons (2016).

Ghosh, P., A. Neufeld, and J.K. Sahoo. "Forecasting Directional Movements of Stock Prices for Intraday Trading Using LSTM and Random Forests." Finance Research Letters 46 (2022): 102280.

Harikrishnan, R., A. Gupta, N. Tadanki, N. Berry, and R. Bardae. "Machine Learning Based Model to Predict Stock Prices: A Survey." IOP Conference Series: Materials Science and Engineering 1084, no. 1 (2021): 012019.

He, Z., and H. Tao. "Epidemiology and ARIMA Model of Positive-Rate of Influenza Viruses among Children in Wuhan, China: A Nine-year Retrospective Study." International Journal of Infectious Diseases 74 (2018): 61-70.

Hochreiter, S., and J. Schmidhuber. "Long Short-term Memory." Neural Computation 9, no. 8 (1997): 1735-780.

Kingma, D.P., and J. Ba. "Adam: A Method for Stochastic Optimization." 6th International Conference on Learning Representations, Canada, April 30 - May 3, 2018.

Kurihara, Y., A. Fukushima. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries." Applied Economics, and Finance 6, no. 3 (2019): 1-6.

Li, X., X. Ma, F. Xiao, F. Wang, and S. Zhang. "Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction." Energies 13, no. 22 (2020): 6121.

Ma, L., C. Hu, R. Lin, and Y. Han. "ARIMA Model Forecast Based on EViews Software." In IOP Conference Series: Earth and Environmental Science 208, no. 1 (2018): 012017.

Mallqui, D.C., and R.A. Fernandes. "Predicting the Direction, Maximum, Minimum and Closing Prices of Daily Bitcoin Exchange Rate Using Machine Learning Techniques." Applied Soft Computing 75 (2019): 596-606.

Nejati, M., and M. Bahmani. "The Economic Impacts of Foreign Direct Investment in Oil and Gas Sector: A CGE Analysis for Iranian Economy." Energy Strategy Reviews 32 (2020): 100579.

Norouzi, N., G.Z. de Rubens, S. Choupanpiesheh, and P. Enevoldsen. "When Pandemics Impact Economies and Climate Change: Exploring the Impacts of COVID-19 on oil and Electricity Demand in China." Energy Research & Social Science 68 (2020): 101654.

Papaioannou, P., T. Dionysopoulos, L. Russo, F. Giannino, D. Janetzko, and C. Siettos. "S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes." Procedia Computer Science 113 (2017): 484-89.

Qiu, J., B. Wang, and C. Zhou. "Forecasting stock Prices with Long-Short Term Memory Neural Network Based on Attention Mechanism." PloS one 15, no. 1 (2020): e0227222.

Sezer, O.B., and A.M. Ozbayoglu. "Algorithmic Financial Trading With Deep Convolutional Neural Networks: Time Series To Image Conversion Approach." Applied Soft Computing 70 (2018): 525-38.

Sharma, A., P. Tiwari, A. Gupta, and P. Garg. "Use of LSTM and ARIMAX Algorithms to Analyze Impact of Sentiment Analysis in Stock Market Prediction." In Intelligent Data Communication Technologies and Internet of Things, Springer Singapore (2021): 377-94.

Sharma, R.R., M. Kumar, S. Maheshwari, K.P. Ray. "EVDHM-ARIMA-based Time Series Forecasting Model and its Application for COVID-19 Cases." IEEE Transactions on Instrumentation and Measurement 70 (2020): 1-10.

Siami-Namini, S., and A.S. Namin. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM." arXiv preprint arXiv: 1803.06386 (2018).

______, N. Tavakoli, and A.S. Namin. "A Comparison of ARIMA and LSTM in Forecasting Time Series." 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Florida, 17–20 December 2018.

______, N. Tavakoli, and A.S. Namin. "The Performance of LSTM and BiLSTM in Forecasting Time Series." In IEEE International Conference on Big Data (Big Data), Los Angeles, 9-12 December 2019.

______, S., N. Tavakoli, and A.S. Namin. "A Comparative Analysis of Forecasting Financial Time Series Using Arima, LSTM, and BILSTM." arXiv preprint arXiv: 1911.09512 (2019).

Singh, K., R. Tiwari, P. Johri, and A.A. Elngar. "Feature Selection and Hyper-Parameter Tuning Technique using Neural Network For Stock Market Prediction." Journal of Information Technology Management 12, Special Issue (2020): 89-108.

Singh, S., K.S. Parmar, J. Kumar, and S.J.S. Makkhan. "Development of new Hybrid Model of Discrete Wavelet Decomposition and Autoregressive Integrated Moving Average (ARIMA) Models in Application to One Month Forecast the Casualties Cases of COVID-19." Chaos Solitons Fractals 135 (2020): 109866.

Solomon, E.M., and A. van Klyton. "The Impact of Digital Technology Usage on Economic Growth in Africa." Utilities Policy 67 (2020): 101104.

Xue, J., S. Zhou, Q. Liu, X. Liu, and J. Yin. "Financial Time Series Prediction using ?2, 1RF-ELM." Neurocomputing 277 (2018): 176-86.

Yu, Y., X. Si, C. Hu, and J. Zhang. "A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures." Neural Computation 31, no. 7 (2019): 1235-1270.

Zhao, C.C., Z.Y. Shang. "Application of ARMA Model on Prediction of Per Capita GDP in Chengdu City." Ludong University Journal (Natural Science Edition) 28 (2012): 223-26.

Zulfigarov, F., and M. Neuenkirch. "The Impact of Oil Price Changes on Selected Macroeconomic Indicators in Azerbaijan." Economic Systems 44, no. 4 (2020): 100814.

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Published

2024-06-28

How to Cite

N.K. Alfarra, A. ., & Hagag, A. (2024). The Potential Influence of COVID-19 on the Arab World Economy. International Journal of Economics, Management and Accounting, 32(1), 1–28. Retrieved from https://journals.iium.edu.my/enmjournal/index.php/enmj/article/view/960

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