The Real-Time Impact of Political Risk on Market Valuations: Evidence from Peru
Bibliographic record
Abstract
This study examines the impact of political risk on financial markets by leveraging high-frequency (minute-by-minute) price data and precise event timestamps from media outlets’ Twitter feeds during Pedro Castillo’s failed coup attempt in Peru. Unlike previous research that relies on low-frequency data and protracted political changes, our analysis demonstrates that daily closing prices may misleadingly suggest negligible impact. In contrast, high-frequency data reveal that markets promptly and accurately incorporated news of the coup attempt and, in turn, its failure into asset prices. Our analysis shows that breakdowns in democratic governance negatively affect asset prices, while the restoration of the rule of law, in the form Congressional checks on the Executive branch, boosts them. Moreover, our analyses suggest that domestic companies and sectors with less mobile assets are more vulnerable to these political risks. Our findings underscore the crucial role of high-frequency data in accurately capturing how institutions and political risk affects equity markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".