Forecasting Political Stability in GCC Countries

TL;DR

This paper develops a machine learning model to forecast political stability in GCC countries, using diverse indicators and achieving high accuracy, providing valuable insights for policymakers to enhance governance and mitigate risks.

Contribution

It introduces a novel application of XGBoost with feature selection on comprehensive datasets to predict political stability in GCC countries for the next five years.

Findings

Oman, UAE, and Qatar are forecasted to remain relatively stable.

Saudi Arabia and Bahrain may face continued political instability.

Economic factors like GDP and foreign investment are key predictors.

Abstract

Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on predicting the political stability index for these six countries using machine learning techniques. The study uses data from the World Banks comprehensive dataset, comprising 266 indicators covering economic, political, social, and environmental factors. Employing the Edit Distance on Real Sequence method for feature selection and XGBoost for model training, the study forecasts political stability trends for the next five years. The model achieves high accuracy, with mean absolute percentage error values under 10, indicating reliable predictions. The forecasts suggest that Oman, the UAE, and Qatar will experience relatively stable political conditions, while Saudi Arabia and Bahrain may continue to face negative political stability indices. The findings underscore the significance of economic factors such as GDP and foreign investment, along with variables related to military expenditure and international tourism, as key predictors of political stability. These results provide valuable insights for policymakers, enabling proactive measures to enhance governance and mitigate potential risks.