Bibliographic record
Abstract
Abstract As a group, professional portfolio managers have been largely unable to outperform the market buy‐and‐hold benchmark. Likewise, professional forecasters have been unable to predict recessions reliably. The paper contributes to the literature in two significant respects. First, the Recession Probability Model herein correctly forecasts out‐of‐sample the probability of a downturn and the binary state over a 45‐year validation sample. This is important as it is around cyclical turning points that forecast errors are largest, and dependable forecasts are most useful. Reliable recession forecasts are essential for risk‐management, planning capital outlays, and for portfolio management. Moreover, accurate forecasts of the turn allow policy‐makers to mitigate the social cost of recessions. Second, the paper shows that it is extremely profitable to switch from equities to T‐bills when the one‐quarter‐ahead probability of recession reaches a certain threshold. Several market‐timing rules dominate the buy‐and‐hold in terms of the risk‐adjusted measures of Treynor, Sharpe, and Jensen. One trading rule achieves triple the terminal wealth of the buy‐and‐hold.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".