Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century
Bibliographic record
Abstract
In this study, we contribute to the rapidly growing climate-finance literature by shedding light on the question of whether climate risks have predictive value for stock market returns. We measure climate risks in terms of both the change in the northern hemisphere temperature anomaly and its volatility and the change in the global temperature anomaly and its volatility. We study monthly data for eight advanced countries (Canada, France, Germany, Italy, Japan, Switzerland, the United Kingdom (UK), and the United States (US)). Our sample period runs from 1916 to 2021. We control for cross-market spillovers of stock market returns and volatility as well as other risks including oil-price returns and volatility, geopolitical risks, and the gold-to-silver price ratio as a measure of investor risk aversion. Given this large array of control variables, we apply the Lasso estimator to trace out the incremental predictive value of climate risks for subsequent stock market returns. We find that climate risks do not have systematic predictive value for subsequent stock market returns. We then extend our analysis in two ways. First, we show that climate risks have short-term out-of-sample predictive value for the connectedness of stock market returns. Second, we show that climate risks have predictive power for stock market returns when we study monthly historical UK data for the sample period from 1772 to 2021.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".