The Predictive Ability of the Bond-Stock Earnings Yield Differential Model
Bibliographic record
Abstract
AbstractThe Federal Reserve (Fed) model provides a framework for discussing stock market over- and undervaluation. It was introduced by market practitioners after Alan Greenspan’s speech on the market’s irrational exuberance in November 1996 as an attempt to understand and predict variations in the equity risk premium (ERP). The model relates the yield on stocks (measured by the ratio of earnings to stock prices) to the yield on nominal Treasury bonds. The theory behind the Fed model is that an optimal asset allocation between stocks and bonds is related to their relative yields and when the bond yield is too high, a market adjustment is needed resulting in a shift out of stocks into bonds. If the adjustment is large, it causes an equity market correction (a decline of 10% within one year); hence. there is a short-term negative ERP. The model predicted the 1987 US., 1990 Japan, 2000 US., and 2002 US. corrections…
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".