Forecasting Sovereign Credit Risk Amidst a Political Crisis: A Machine Learning and Deep Learning Approach
Bibliographic record
Abstract
The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (RNN, LSTM, BiLSTM, and GRU) to predict CDS-based implied default probabilities. We compare the predictive accuracy of the tested models with the results showing that Linear Regression outperforms all other techniques, while deep learning architectures, such as RNN and GRU, demonstrate a competitive performance. To validate the sovereign credit risk prediction, we use the forecasted implied default probability from the Linear Regression model to determine the corresponding forecasted implied rating according to the Thomson Reuters StarMine Sovereign Risk model. The results reveal significant differences in the perceived creditworthiness of Egypt, Morocco, and Saudi Arabia, reflecting each country’s economic fundamentals and their ability to manage global shocks, particularly those related to the Russo-Ukrainian war. Specifically, Egypt is perceived as the most vulnerable, Morocco occupies an intermediate position, and Saudi Arabia is seen as having a low credit risk. This study provides valuable managerial insights by enhancing tools for the sovereign credit risk analysis, offering reliable decision-making in volatile global markets. The alignment between forecasted ratings and default probabilities underscores the practical relevance of the results, guiding stakeholders in effectively managing credit risks amidst economic uncertainty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".