The Effect of Air Pollution on Investor Behavior: Evidence from the S&P 500
Bibliographic record
Abstract
We provide detailed empirical evidence of a direct effect of air pollution on the efficient operation of the New York Stock Exchange, linking short-term variations in fine particulate matter (PM2.5) in Manhattan to movements in the S&P 500. The effects are substantial – a one standard deviation increases in ambient PM2.5 reduces same-day returns by 11.9% in our preferred specification – and remarkably robust to a variety of specifications and a battery of robustness and falsification checks. Furthermore, the intra-day effects that we observe are difficult to reconcile with competing hypotheses. Despite investors being dispersed geographically we find strong evidence that the effect is strictly local in nature, consistent with the high concentration of market influencers in New York. While we are agnostic as to the underlying mechanism, we provide evidence suggestive of the role of decreased risk tolerance operating through pollution-induced changes in mood or cognitive function.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".