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All Journal GREENOMIKA Journal of Current Research in Blockchain International Journal Research on Metaverse
Prompreing, Kattareeya
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Journal : Journal of Current Research in Blockchain

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Evaluating the Influence of Economic Indicators on Country Risk Premiums Using Random Forest: A Comprehensive Study on Global Country Data Prompreing, Kattareeya
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.32

Abstract

This study investigates the relationships between key macroeconomic indicators—Gross Domestic Product (GDP), Unemployment Rate, and Country Risk Premium—using a combination of correlation analysis, Random Forest Regression, and data visualization techniques. The correlation matrix revealed a weak negative relationship between GDP and Country Risk Premium (r = -0.19), suggesting that economic prosperity modestly reduces perceived investment risk. Conversely, Unemployment Rate exhibited a very weak positive correlation with Country Risk Premium (r = 0.065), indicating that labor market instability may slightly increase financial risk. The Random Forest model achieved a mean squared error (MSE) of 2.55 and an R-squared value of 0.018, highlighting the limited predictive power of GDP and Unemployment Rate alone. Feature importance analysis showed that GDP accounted for 53.7% of the model's predictive power, while Unemployment Rate contributed 46.3%, underscoring the relevance of both variables. Visualizations, including scatter plots and boxplots, provided further insights into the variability and complexity of Country Risk Premium. The findings suggest that while GDP and Unemployment Rate are important predictors, additional factors such as political stability or inflation rates may be necessary to improve predictive accuracy. This study contributes to the understanding of financial risk determinants and highlights the potential of advanced modeling techniques in economic research.
Co-Authors Muhammad Sadat, Andi Prompreing, Theera Saidani, Basrah Sholikhah