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Record W2578885335 · doi:10.1111/jbfa.12233

National Culture and the Valuation of Cash Holdings

2017· article· en· W2578885335 on OpenAlexaff
Svetlana Orlova, Ramesh P. Rao, Tony Kang

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsValuation (finance)Uncertainty avoidanceCashIndividualismHofstede's cultural dimensions theoryBusinessValue (mathematics)EconomicsFinanceSocial psychologyPsychologyCollectivism

Abstract

fetched live from OpenAlex

Abstract Prior studies document that national culture traits are systematically related to cash holdings and attribute this to managerial cultural predispositions. However, it is possible that these preferences reflect investors’ cultural preferences and that managers are simply catering to investors’ preferences. It is also not clear whether the cash holding effects previously documented are value maximizing. By examining the impact of national culture traits on cash valuation, we are able to provide insight into these questions. Specifically, we examine the effect of three national culture traits – individualism, uncertainty avoidance and long‐term orientation – on firm cash valuation. Our results suggest that the previously observed effects of cultural traits on cash holdings and attributed to managerial cultural biases do not reflect investors’ preferences and are not value maximizing.

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)0.006
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.250
Teacher spread0.213 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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