The Predictive Ability of the Bond Stock Earnings Yield Differential Model
Bibliographic record
Abstract
This is a survey of the bond stock prediction model in international equity markets which is useful for predicting the time varying equity risk premium (ERP) and for strategic asset allocation of bond-stock equity mixes. The model has two versions. Beginning with Ziemba and Schwartz (1991), the BSEYD model our is the difference between the most liquid long bond, usually thirty or ten or five years, and the trailing equity yield. The idea is that asset allocation between stocks and bonds is related to their relative yields and, when the bond yield is too high, there is a shift out of stocks into bonds that can cause an equity market correction. This model predicted the 1987 US, the 1990 Japan, the 2000, 2002 and 2007 US corrections. The FED model is a special case of the BSYED model with bond and stock yields assumed to be equal. A ratio model and the FED model have origins in reports and statements from the Federal Reserve System under Alan Greenspan, from 1996. Hence the ERP can be negative or positive and is thus partially predictable. Despite its predictive ability, the bond-stock model has been criticized as being theoretically unsound because it compares a nominal quantity, the long bond yield, with a real quantity, the earnings yield on stocks. However, inflation and mis-conception arguments may justify the model. Theoretical models of fair priced equity indices can be derived and compared to actual index values to ascertain danger levels. This paper surveys this literature with a focus on their economic and financial implications and its application to the study of stock market strategies and corrections in five worldwide equity markets.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".