Bibliographic record
Abstract
This study argues that the central bank’s short-term rate best captures the risk-free rate of various assets, and that a payment’s risk approaches zero as maturity approaches zero. Expecting that the T-bill risk reflects a country’s inflation risk, we measure the risk as a one-year payment’s risk to be comparable across assets. A GDP growth rate lower than the risk-free rate is actually negative, because the risk-free rate reflects the expected inflation effect, whereas the inflation risk reflects the unexpected inflation effect (i.e., the opportunity cost of alternative investment in nondepreciating assets). We employ two factors to model the risk-free rate, which the market expects the current monetary policy to bring towards the neutral level over a certain period. Our 4-factor models include a risk constant over maturity that captures the depreciation cost of the inter-bank short-term lending. We solve the model factors using repeated trials to minimize the prediction errors. We show that the 4-factor independence model, which assumes the independence of the two returns, outperforms all the other models and specifications, explaining 99.3% of the US T-bill returns on average. This paper is the gateway to asset risk analysis.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".