Neuroscience and Market Dynamics: The Impact of Smoking Withdrawal Syndrome on the Stock Performance of Tobacco Companies
Bibliographic record
Abstract
This study investigates the effect of the annual 'No-Smoking Day' on the stock performance of British American Tobacco (BATS) and Imperial Brands (IMB) from 1997 to 2023. Our findings reveal a significant negative impact of No-Smoking Wednesdays on BATS, with a moderate but statistically significant effect on IMB. To enhance robustness, we also perform a panel data analysis, which underscores the consistent negative effect of No-Smoking Day on the tobacco sector as a whole. These results suggest that No-Smoking Day generates a calendar-based effect on stock prices, challenging the Efficient Market Hypothesis. Beyond the behavioral effects tied to the anti-smoking campaign, this study introduces a novel perspective by linking investor behavior with neurological factors, particularly Nicotine Withdrawal Syndrome (NWS). NWS, characterized by irritability, anxiety, and mood disturbances, may influence investor sentiment, even among smokers who do not intend to quit. These withdrawal symptoms could induce stress and emotional responses, thereby affecting investor behavior and contributing to negative returns. Our findings align with prior behavioral studies and highlight the role of both psychological and neurobiological factors in shaping market dynamics. Future research should examine the combined effects of anti-smoking campaigns and NWS on investor behavior and market outcomes. Additionally, the varying statistical significance across firms suggests that the diversification of tobacco companies into non-traditional products warrants further investigation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".