Stock market reactions to interest rate changes and the influence of investor sentiment
Bibliographic record
Abstract
This thesis explores how changes in interest rates and investor sentiment shape stock market performance, focusing on the short-term reactions around multiple event windows following central bank announcements across different regions. Using an event study approach, it measures Cumulative Abnormal Returns (CARs) to understand how markets respond to interest rate changes and shifts in the Consumer Confidence Index (CCI) between 1980 and 2023. Consistent with previous research, the study finds that rising interest rates tend to lead to negative market returns, especially in regions like Canada and Japan, where these reactions are the most pronounced. However, the impact of investor sentiment isn’t uniform across the board. While past studies often suggest widespread effects of sentiment on markets, our findings show that it plays a significant role in regions such as Japan and the European Union, but less so in places like Canada and the UK. The research also dives into how industries respond to the combination of interest rate direction and investor sentiment. Certain sectors in Japan and Europe showed strong reactions, supporting the idea that some industries are more sensitive to these economic factors. On the other hand, markets in Switzerland and the UK were largely unaffected, which contrasts with some earlier findings. Overall, this study confirms and adds to previous research by showing that while macroeconomic factors influence stock markets, the strength and nature of these effects differ across regions and sectors. These insights are valuable for investors and policymakers, especially in times of shifting monetary policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".