The Interaction between Market Sentiments in the U.S. Financial Market and Global Equity Market
Bibliographic record
Abstract
This paper adopts the volatility index and Baker-Wurgler index as the U.S. financial market sentiment measures. Using monthly data from June 1965 to December 2010, we identify the causal relationships between sentiment and the performance of global equity markets. We include 23 G20 market indices, 28 European indices, 25 Asia-Pacific indices, and 10 Americas indices, and employ Granger causality procedure to explore the linkages. We find that the international equity markets are not greatly affected by the U.S. financial market sentiment. The type of extreme sentiment, whether it is optimistic or pessimistic, is irrelevant to its influential power. The equity markets that are affected by the volatility index do not cluster in any region. In contrast, the majority of global equity markets can Granger cause the U.S. investor sentiments, with optimistic market atmosphere being more affected. The equity markets in the Americas and Europe are highly influential to the U.S. investors, compared to the Asian 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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".