How does investor attention with different levels of informational advantage affect market returns?
Bibliographic record
Abstract
Purpose The aim of this research is to analyze the impact of investor attention, with different levels of informational advantage (local and foreign), on stock market returns. Design/methodology/approach A panel vector autoregression (PVAR) model was used to analyze ten developed markets (Germany, Canada, Spain, the United States of America, France, the Netherlands, Italy, Japan, the United Kingdom and Switzerland) and ten emerging markets (South Africa, Brazil, China, India, Indonesia, Malaysia, Mexico, Pakistan, Russia and Turkey). Attention measures, based on Google Trends search volume, covered the period from January 2017 to December 2021 for the main models and from January 2015 to December 2019 for robustness tests. Findings The results show that local attention has a significant negative impact on returns in both emerging and developed markets, suggesting an informational advantage for local investors over foreign investors. However, the impact is temporary and may also be associated with attentional pressures unrelated to fundamentals. Originality/value This study expands the understanding of the complex and transient relationship between geographically differentiated attention allocation and returns, analyzing its dynamics in emerging and developed 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 | 0.001 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".