Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Predicting stock market crashes is extremely valuable for all investors. Several useful prediction models have been developed, focusing on mature financial markets, in North America, Europe, and Japan. The authors investigate whether traditional crash predictors—the price-to-earnings ratio (P/E), the cyclically adjusted price-to-earnings ratio (CAPE), and the bond–stock earnings yield differential model (BSEYD)—predict crashes for the Shanghai Stock Exchange Composite Index and the Shenzhen Stock Exchange Composite Index in mainland China. Using data from the early 1990s to the end of 2016, the authors find that the P/E ratio has predictive value for both exchanges over the entire period. When testing the P/E, CAPE, and BSEYD over a shorter nine-year period, the authors find that all measures had a higher predictive value for the Shenzhen index, where smaller, privately owned companies are listed, than for the Shanghai index, where larger, often state-owned enterprises trade. <b>TOPICS:</b>Tail risks, portfolio management/multi-asset allocation, performance measurement, volatility measures
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it