The Impact Of Weekly Money Supply Announcements ON Stock Market Returns: A Multiprocess Mixture Model Approach
Bibliographic record
Abstract
This study provides evidence concerning the impact of anticipated and unanticipated components of the weekly money supply announcements on stock market returns in the United States and Canada on the date after the announcement. The innovative aspect of this study is the use of a multiprocess mixture model recently proposed by Gordon and Smith (1990) for modeling time series that are subject to several forms of potential discontinuous change and outliers. The technique involves running multiple models in parallel with recursive Bayesian updating procedures which extend the standard Kalman filter. The results provide strong evidence in favor of the efficient markets hypothesis that only the unanticipated component of the money supply announcement influences the stock market returns in both the United States and Canada.The use of OLS estimated in the present study produces results which suggest that both anticipated and unanticipated components of the money supply announcement exert a statistically significant influence on stock market returns in both countries. In contract, the multiprocess mixture model estimation method produces results which support the efficient markets hypothesis. The difference in findings between OLS and multiprocess estimation methods is attributed to the ability of the multiprocess techniques to model discontinuous structural shifts in the parameters and accommodate outliers in the stock return-weekly money relationship. The multiprocess mixture method provides evidence that numerous discontinuities and outliers exist in the stock market returns-weekly money relation and produces posterior probabilities for the multiple models running in parallel. These probabilities suggest that the OLS model has low posterior probability relative to the structural shift and outlier models which suggest poor inferences regarding the significance of anticipated and unanticipated money arise from the use of OLS estimation techniques.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".