World and regional factors in stock market returns
Bibliographic record
Abstract
Purpose This paper aims to test the hypothesis that the national stock market returns are driven by a world factor, regional factors and idiosyncratic factors, and to measure the importance of each factor. Design/methodology/approach The state‐space model is applied to describe the sample returns and estimate a world factor, regional factors and idiosyncratic factors by Kalman filtering. Weekly and daily returns calculated from MSCI country indexes from January 1988 to December 2004 of 11 national stock markets in four regions, i.e. North America (the USA and Canada), South America (Brazil, Mexico and Chile), Europe (the UK, Germany and France), and Asia (Japan, Hong Kong, and Singapore) are used. Findings The results support the hypothesis that national market returns are driven by a world factor, regional factors and idiosyncratic factors. National markets do not always respond mainly to the world factor; regional factors and idiosyncratic factors play important roles as well. They also respond to world news at a slower rate than regional news. Research limitations/implications This paper does not identify the source or origins of news directly but the factors are assumed as random variables and are estimated under certain strict assumptions. Originality/value This paper applies Kalman filtering to estimate a world factor and regional factors and test the importance of each factor directly, an extension of previous studies that mostly showed strong independence among 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".