Climate Change, Firm Performance, and Investor Surprises
Bibliographic record
Abstract
We link records of firm performance, equity analyst forecast errors, and stock returns around companies’ earnings announcements to firm-specific measures of heat exposure for more than 17,000 firms in 93 countries from 1995 to 2019. We find that increased exposure to extremely high temperatures reduces firms’ revenues and operating income. A one-standard-deviation increase in the number of hot days decreases revenues (operating income) by 0.6% (1.8%) of the average quarterly revenue (operating income). Moreover, we provide evidence that increased heat exposure impacts negatively on firm financial performance relative to analyst predictions and on earnings announcement returns. These findings indicate that capital market participants do not fully anticipate the economic consequences of heat as a first order physical climate risk. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Funding: N. Pankratz gratefully acknowledges financial support from the French Social Investment Forum and the Principles for Responsible Investment [PhD Research Grant 2017]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4685 .
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".