IMPACT OF PUBLIC TRUST IN U.S. FINANCIAL INSTITUTIONS ON GLOBAL STOCK MARKET RETURNS
Bibliographic record
Abstract
Public trust in financial institutions is crucial for determining the value of securities.This study examines how new macroeconomic indicators that capture public trust in U.S. financial institutions impact stock markets in North America, Latin America, Europe, Asia, the Gulf Cooperation Council (GCC), Africa, and major emerging economies.We employ five distinct indicators that measure public trust levels in commercial banks, mutual funds, stock markets, large corporations, and overall financial trust, and investigate their relative impact on 21 international stock market indexes.The results suggest that three out of the five financial trust indicators significantly impact U.S. and international stock market returns, with varying degrees of strength.Specifically, the financial trust index, public trust in large corporations, and public trust in commercial banks significantly affect global markets, while trust in stock markets and mutual funds are insignificant.The financial trust index has the maximum impact and affects stock markets in economies with strong trade links to the U.S., such as Canada, the U.K., China, and Mexico.Similarly, public trust in large corporations significantly impacts stock markets in manufacturing countries like Canada, China, the U.K., Germany, France, Spain, and GCC economies.On the other hand, public trust in commercial banks affects the stock markets of economies with strong financial services industries, such as China, the U.K., Switzerland, Germany, Hong Kong, and South Korea.These findings suggest that financial trust is a critical factor in valuing securities in international markets, making it an additional risk factor that is priced in global markets.Understanding these dynamics can enhance market stability, improve investor confidence, and foster sustainable global economic growth.
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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 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".