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Record W2746788028 · doi:10.1111/jifm.12067

Trade credit financing and stock price crash risk

2017· article· en· W2746788028 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of International Financial Management and Accounting · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsQueen's University
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsEndogeneityTrade creditBusinessStock (firearms)Information asymmetryFinanceStock priceMonetary economicsFinancial systemEconomicsEconometrics

Abstract

fetched live from OpenAlex

Abstract This study investigates the association between trade credit financing and stock price crash risk within China's context. We find that firms using more trade credit financing have significantly lower future stock price crash risk. This negative association is more pronounced for firms with greater information asymmetry and for firms located in less developed financial markets. This finding is robust to the endogeneity concern, alternative measures of stock price crash risk, and the inclusion of other factors identified in prior studies that might affect stock price crash risk. Further evidence suggests that both the monitoring mechanism and the disclosure mechanism drive the documented relation. Our study suggests that access to trade credit can significantly reduce the likelihood of crash risk in a country like China with less developed formal bank financing. Our study also suggests that investors can effectively avoid stock price crash risk by using the trade credit information disclosed in financial statements.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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