Money Supply, Banking Liquidity and Stock Index Returns: Evidence from Four Major Capital Markets
Bibliographic record
Abstract
Prior studies that have examined the drivers of stock returns have mostly focussed on firm-specific factors. However, King demonstrated that firm-specific factors explain only 38% of the variation in stock prices, while the dominant driver was macroeconomic factors (52%) with industry-related factors accounting for the remaining 10% of stock price variation. Against this background, the present study, to our knowledge, is the first attempt to model Friedman's still-unproven money-supply led banking liquidity effect and the subsequent effect on stock prices, that will be represented as stock index returns in this study. We, thereafter, proceed to build a model to connect money supply and banking liquidity to overall stock index returns. For the purpose, we apply a system of equations, and use quarterly macroeconomic data series of G-4 countries (Canada, Japan, the UK, and the US) covering a 54-year period. We control for monetary regime changes such as a shift from monetary targeting to inflation targeting, structural breaks following the global financial crisis (GFC) and monetary policy changes. To test robustness of our findings, we provide causality tests linking money supply to liquidity as well as stock index returns and earnings before applying bootstrapping method to refine the parameter estimates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".