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Record W7027706595

Defaults and Returns in the High Yield Bond Market: The Year 2003 in Review and Market Outlook

2004· report· en· W7027706595 on OpenAlexaboutno aff

Bibliographic record

VenueThe Faculty Digital Archive (New York University) · 2004
Typereport
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsnot available
Fundersnot available
KeywordsDefaultBondYield (engineering)Bond marketRate of returnInterest rateInvestment (military)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

High yield bond defaults in 2003 declined significantly from record 2002 levels closing the year at $38.5 billion for a default rate of 4.66%. The fourth quarter’s rate of 0.36% was the lowest quarterly rate since the fourth quarter of 1997. The default loss rate for 2003 also declined to just 2.76% based on a weighted average recovery rate of about 45% -- a major\nimprovement from the 25% levels of the prior several years. Fourteen of the 86 defaulting\ncompanies had issues that were investment grade sometime prior to default. These fallen\nangels accounted for 33% of defaulting issues and 46.3% of the defaulted volume in 2003.\nThe high-yield bond market returned an impressive 30.62% for the year, the third highest one-year return since 1978 (when we first began tracking returns). The return spread over ten-year US Treasuries was a record high 29.4%, bringing the historic average annual return spread to 2.22% per year. The concurrent yield spread at year-end fell to 3.74%, the lowest year-end figure since 1997 and 4.82% less than one year ago. New issues in 2003 recorded a\nnear record level of $137.4 billion; the vast majority was used for refinancing existing loan and bond issues.\nBased on our mortality rate methodology and assuming different measures of credit risk of\nrecent new issuance, we expect default rates to continue their decline in 2004 to between\n3.2% - 3.8%, with rates increasing in 2005 to above 4.0%.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.317
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.256
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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