A stochastic model for predicting the response time of green vs brown stocks to climate change news risk
Bibliographic record
Abstract
We model the dynamic evolution of the attention process over the duration of climate change news events as a Brownian motion with an absorbing barrier, where attention to the news event ceases. In this framework, the duration of the underlying news event is a random variable whose probability distribution is the Inverse Gaussian (IG). We show that the IG distribution of news duration can be used to predict the response time of asset prices to climate news risk. We test the empirical validity of our model by constructing two novel climate news duration data sets: a daily duration and an hour-by-hour intra-news duration. At the daily frequency, our model predicts the response time of green versus brown firms’ stock prices to climate news risk. We demonstrate how this response time can enhance the precision of conventional risk management statistics, e.g., Value at Risk and expected shortfall, and in consequence improves the efficiency of managing firms’ exposures to such risk. At the high frequency, we extend the autoregressive conditional duration (ACD) model and show that, in an IG-ACD-GARCH framework, climate change news arrivals contribute to the volatility of green (but not brown) firms’ returns. This finding is attributed to public and investors’ concerns about climate change or to their belief that climate transition policies are ineffective in combating climate change.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".