How important are climate change risks for predicting clean energy stock prices? Evidence from machine learning predictive modeling and interpretation
Bibliographic record
Abstract
The clean energy equity sector plays an important role in the transition to a low-carbon economy. This paper explores the role of climate change risks in predicting the direction of clean energy stock prices (solar, wind, nuclear). We employ machine learning models, including random forests, boosting, extremely randomized trees, and support vector machines , to make our predictions. Variable importance is determined using Shapley/SHAP values. Notably, tree-based ensemble and boosting models show an accuracy exceeding 85 % for the 10 day to 20 day forecast period. For the stock prices of solar, wind, and nuclear energy, inflation expectations and technical indicators (which account for behavioral factors) such as on-balance volume and Williams’ accumulation/distribution are important features within this forecast range. For wind and solar energy stocks moving averages are also important additional features while for nuclear energy stocks economic policy uncertainty and stock market volatility are additional important features. In the five day to twenty day forecast horizon, climate change risks are not important features. These results align with a body of literature that raises concerns about equity prices not fully reflecting climate change risks. An equally weighted portfolio of wind, solar, and nuclear energy stock prices that used trading signals from an Extra Trees prediction model outperformed a buy and hold portfolio in terms of risk adjusted returns. These results are robust to trading costs and weekly or monthly portfolio rebalancing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".