Market Volatility around U.S. Presidential Election (1928-2016): The Role of Political Uncertainty
Bibliographic record
Abstract
This paper investigates the changes in market volatility around the United States presidential elections and inaugurations between the period of 1928 and 2016 during selected event windows: (-10, -1) vs. (+1, +10), (-20, -1) vs. (+1, +20), … (-90, -1) vs. (+1, +90), respectively. To isolate the corresponding impact of different types of political uncertainty, market volatility is examined under three partitions: magnitude of surprise in voting results, incumbency, and change in ruling party. The result indicates that the market volatility is more willing to settle down after an election with new president or a change in ruling party, mainly due to the comparatively higher volatility induced by such political events during the pre-election window. The results have implications for both individual and institutional investors who are exposed towards volatility risk.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".