Economic policy uncertainty and stock market in G7 Countries: A panel threshold effect perspective
Bibliographic record
Abstract
Based on the literature, it is commonly understood that stock prices (SP) are influenced by economic policy uncertainty (PU), with a rise in PU typically having a negative impact on SP. However, the relationship between PU and SP may not always be linear due to the varying risk preferences of individuals. Risk preference theory posits that individuals respond differently to different levels of risk. Therefore, this study aims to investigate whether PU determines SP asymmetrically (i.e., in a non-linear manner) by considering risk preferences and addressing a gap in the literature. To answer this question, the study employs a panel threshold approach to examine the effect of PU on SP in the Group of Seven (G7) countries, namely Canada, France, Germany, Italy, Japan, UK, and the US. In contrast to previous research, this study finds evidence of an asymmetric effect of PU on SP in the G7 countries. Specifically, the panel threshold results reveal that the impact of increased PU on SP is positive up to a certain level (Threshold1), beyond which it becomes negative (Threshold2). These findings are in line with information asymmetry hypothesis, prospect theory, behavioural finance hypothesis, and market liquidity hypothesis and shed light on the asymmetric behaviour of SP in response to varying levels of PU. The implications of these findings are significant for understanding how to manage risks effectively in the financial markets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".