Flight to Quality in International Markets: Investors’ Demand for Financial Reporting Quality during Political Uncertainty Events
Bibliographic record
Abstract
Abstract We examine whether international equity mutual fund managers shift their portfolios toward stocks with higher financial reporting quality (FRQ) during periods of high political uncertainty. Our study is motivated by two primary factors. First, prior research shows evidence of fund managers’ “flight to quality” (e.g., to less risky securities) during periods of uncertainty. Second, recent theoretical research concludes that stocks with higher FRQ are assessed as less sensitive to systematic risk (such as political uncertainty). We employ national elections as exogenous increases in systematic risk in the local markets and accordingly use an international sample of mutual funds that focus on local markets. We find that mutual fund managers shift their equity holdings to stocks with higher FRQ during election periods when political uncertainty is higher. Such a flight‐to‐quality effect is less pronounced for elections with larger expected electoral margins in the pre‐election period (i.e., when the incumbent is more likely to win the election) and for countries with higher transactions costs. In contrast, the effect is more pronounced when governments have greater involvement in the local economy. Our inferences are robust to alternative proxies for political uncertainty and FRQ and to numerous other sensitivity analyses.
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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 | 0.002 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".