Cyclicality in the prices of risk: what more can we learn from explainable AI?
Bibliographic record
Abstract
We uncover the temporal patterns of the prices of risk through industry portfolios with varying sensitivities to the economic and financial cycles. Conditioning on the highs and lows of the cycles is key for statistical significance of the intertemporal component. Unlike market risk, its price decreases during an economic downturn but increases under tight funding conditions. Predictive machine learning models and their SHAP values suggest that a limited number of firm characteristics convey the most informative signals about asset risk premia. Valuation ratios are more important determinants for Cyclical relative to Defensive industries, whereas Return characteristics become crucial during recessions. Valuation Insight The prices of risk that affect discount factors and present values are found to vary substantially over time depending separately on industry sensitivity to economic and financial cycles. Based on predictive machine learning models, the firm characteristics are uncovered that provide the most information about discount factors at the industry level. Valuation ratios are more important indicators of discount factors for cyclical industries than for defensive industries.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".