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Record W4395955836 · doi:10.54097/xajfzq51

Exploring the Association Between Corporate Financial Credit Risk Management and Corporate Value

2024· article· en· W4395955836 on OpenAlexaff
Sipeng Qiu

Bibliographic record

VenueFrontiers in Business Economics and Management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsBusinessCredit riskAssociation (psychology)Value (mathematics)AccountingRisk managementFinanceComputer sciencePsychology

Abstract

fetched live from OpenAlex

In the rapidly changing business environment, corporate financial credit risk management has become a key factor in the stable operation and continuous value enhancement of enterprises. Corporate value is reflected not only in its tangible assets but also in its intangible assets and management capabilities. Among them, financial credit risk management, as an important part of corporate management, is increasingly being paid attention to for its association with corporate value. Financial credit risk management involves the identification, assessment, monitoring, and control of risks related to financial activities, which may stem from market changes, credit defaults, operational errors, and more. Effective financial credit risk management not only helps enterprises reduce potential losses and protect asset safety but also enhances the market reputation and competitiveness of enterprises, thereby increasing their overall value. With the continuous development of financial markets and the strengthening of globalization trends, the financial credit risks faced by enterprises are becoming increasingly complex and varied. Therefore, building a comprehensive financial credit risk management system and enhancing the risk management capabilities of enterprises have become key to achieving sustainable development and creating long-term value.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.045
GPT teacher head0.187
Teacher spread0.141 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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