Forecasting Credit Cycles: The Case of the Leveraged Finance Market in 2024 and Outlook
Bibliographic record
Abstract
There are certain times in our economic and financial environments when it makes sense to assess carefully and dispassionately where we are in the credit cycle and how this cycle relates to the business cycle. Now, mid-2024, is one of those times, as the economic uncertainties are at substantial levels. This note reflects my long history of studying credit cycles dating back to the early 1970s. My current assessment is that the Benign Credit Cycle we have enjoyed since 2010, with the exception of a few months in 2016 and early 2020, ended in 2023. We recently reached an inflection point for an average credit risk scenario. This assessment is based on an analysis of a number of historical indicators over the last 50 years. This conclusion is tempered by the possibility that the U.S. credit picture will continue its heightened risk trend toward a Stressed Scenario by the end of 2024, and combined with a “hard-landing” economic recession, we could witness another financial-credit crisis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".