On the stock market liquidity and the business cycle: A multi country approach
Bibliographic record
Abstract
We provide original results on national and global stock market liquidity and its interaction with macro-economic variables for six of the G7 economies, namely: Canada, France, Germany, Italy, Japan and UK, building on the methodology and on the US evidence by Naes et al. (2011). Using a number of additional tests, we find that different markets do not behave in a uniform manner. National liquidity has diminished ability in Granger causing macroeconomic variables for our sample countries, and in additional tests the same holds for an extended US sample, contrary to Naes et al. As regards global liquidity there is a two-way causality with macroeconomic indicators for the six nations in our sample while for the US there is no causality in either direction. We also show that there is no superior information in small firm liquidity in Granger causing macroeconomic variables even for the US in contrast to the sample period employed by Naes et al. implying an unstable relationship over time for the US.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".