The Stock Market and the Consumer Confidence Channel in Canada
Bibliographic record
Abstract
When stock prices rise, so does aggregate consumer spending. A traditional explanation for this phenomenon is based on wealth effects. However, movements of the stock market may affect consumer spending indirectly, by influencing consumer confidence. A bullish stock market may make consumers feel more optimistic about the future of the aggregate economy, and hence increase their spending. This paper investigates the existence of the consumer confidence channel of asset price transmission in Canada. The analysis is based on the indices of consumer confidence from the Conference Board of Canada and the Toronto Stock Exchange index. The results are supportive of the consumer confidence channel at the national level. There is also evidence of asymmetric effects of stock price changes on confidence changes: declines of the stock index have larger and statistically more significant effects relative to its increases. / Lorsque les cours des actions s’élèvent, les dépenses de consommation globale s’accroissent. Une explication traditionnelle de ce phénomène se base sur l’effet de richesse. Cependant, les mouvements de la bourse peuvent aussi affecter les dépenses de consommation indirectement, en influençant les attitudes des consommateurs. Un marché boursier en hausse peut entraîner les consommateurs à se sentir plus optimistes envers l'avenir de l'économie et par conséquence à augmenter leurs dépenses. Cette étude examine l'existence d’un canal de transmission des cours des actions sur la consommation par le biais des attitudes des consommateurs. Notre analyse est basée sur les indices des attitudes des consommateurs du Conference Board du Canada et sur l'indice des actions de la Bourse de Toronto. Les résultats confirment le canal des attitudes des consommateurs au niveau national. De plus, les variations des cours des actions ont des effets asymétriques sur les changements des attitudes des consommateurs: les baisses de l'indice boursier ont un effet plus important qui est aussi statistiquement plus significatif que l’effet des augmentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".