GENERALIZED FISHER HYPOTHESIS VALIDITY FOR CANADA, UK, AND SUISSE STOCK MARKETS: EVIDENCE FROM PANEL ARDL MODELS
Bibliographic record
Abstract
In this paper we propose a decision support tool for the investor in terms of asset allocation. The key question is to know whether equities are perfect hedge against inflation if either we invest in only one market or if we go to all the considered markets. We chose three democratic countries having common monetary policy based on the Inflation rate stabilization targeting (including Canada, UK, and Suisse) over the period 1999M01-2018M04. We see how the stock return evolution is related to inflation rate Pre, during, and Post 2008 Global financial crisis (GFC). Then, some dynamic version of the Generalized Fisher hypothesis (GFH) models are explored by some univariate and panel autoregressive dynamic linear (ARDL) frameworks. We conclude that during crisis period, being on either Suisse or Canadian stock market, investors can have important abnormal gains. Then including the UK in a portfolio allows investors to limit losses caused by inflation in the UK stock market alone.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".