What moves the stock market? : an examination of the co-movement between stock prices and aggregate economic activitycby Mei Dong.
Bibliographic record
Abstract
IThis paper investigates whether there is a long run co-movement between the stock market and aggregate economic activity.Following the testing framework suggested by Cheung and Ng (1998), the paper examines six major countries including United States, United Kingdom, Canada, Germany, Japan and Australia.Quarterly data from 1969 to 1998 are used to estimate the long run relationship between a country's stock market and its aggregate economic activity.From a Vector Error Correction model including a cointegration term, the stock return is explained by the deviation of the stock price index from its long run equilibrium level and other macroeconomic variables.A significant cointegration term implies that aggregate economic activity is one of the forces that influence the long run stock market behavior.However, the empirical results from this paper do not provide strong support for this long run equilibrium between the stock prices and aggregate economic activity.Possible explanations are provided for this mixed international evidence.* The numbers with * indicate the rejection of the null hypothesis at 5% significance level.** The 5% critical value of Trace Statistic is 15.41.The 5% critical value of Amax statistic is 14.07.*** The number of lags in each cointegration test is determined by the Schwartz Criteria.Number of lags for the other countries is 1.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".