Defaults and Returns in the High-Yield Bond and Distressed Debt Market: Review and Outlook
Bibliographic record
Abstract
High-yield bond default rates on US, Canadian and Mexican high-yield bonds decreased slightly in 2013 and remained well below historical averages. The rate decreased from 1.62% at year-end 2012 to 1.04% for all of 2013. Defaults include straight corporate bonds whose firms went bankrupt, missed an interest payment and did not cure it within the grace or forbearance period, or completed a distressed exchange. The 2013 rate is based on a mid-year market size of $1.39 trillion, up by a sizeable $180 billion from a year earlier. In all, $14.5 billion of defaults were recorded in 2013 (Table 8.1). The historical weighted-average annual default rate is 3.61% over the 43-year period (1971–2013). This weighted-average rate is down compared to 3.82% at the end of 2012. Our weights are based on the par value of high-yield bonds outstanding in each year. The arithmetic annual average default rate dropped to 3.14% from 3.19% one year earlier. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".