MétaCan
Menu
Back to cohort
Record W2890411362 · doi:10.3386/w16995

Cash Holdings and Credit Risk

2011· preprint· en· W2890411362 on OpenAlexaff
Viral V. Acharya, Sergei Davydenko, Ilya A. Strebulaev

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBank of CanadaMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsBusinessCashCredit riskFinancial systemFinance

Abstract

fetched live from OpenAlex

Intuition suggests that firms with higher cash holdings are safer and should have lower credit spreads.Yet empirically, the correlation between cash and spreads is robustly positive and higher for lower credit ratings.This puzzling finding can be explained by the precautionary motive for saving cash.In our model endogenously determined optimal cash reserves are positively related to credit risk, resulting in a positive correlation between cash and spreads.In contrast, spreads are negatively related to the "exogenous'' component of cash holdings that is independent of credit risk factors.Similarly, although firms with higher cash reserves are less likely to default over short horizons, endogenously determined liquidity may be related positively to the longer-term probability of default.Our empirical analysis confirms these predictions, suggesting that precautionary savings are central to understanding the effects of cash on credit risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.269
GPT teacher head0.407
Teacher spread0.138 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations31
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicCorporate Finance and GovernanceFrench-language works237,207