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Record W3146199195

Government Economic Policy Uncertainty, Corporate Cash Holdings, and the Value of Cash

2017· article· en· W3146199195 on OpenAlexaff
Hieu V. Phan, Nam H. Nguyen, Hien Thu Nguyen, Shantaram P. Hegde

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOperating cash flowCashBusinessCash flowShareholderCash managementMonetary economicsCash conversion cycleCash flow forecastingCash flow statementInvestment (military)Precautionary savingsTerminal valueFinanceEconomicsCorporate governance
DOInot available

Abstract

fetched live from OpenAlex

This research examines the relationships between government economic policy uncertainty and corporate cash holdings and the value of cash. We find robust evidence that policy uncertainty is positively related to corporate cash holdings due to firms’ precautionary motives and investment delays. Policy uncertainty adversely affects the value of cash to shareholders of firms with high growth opportunities because it motivates these firms to hold cash for precautionary purpose while discouraging them from deploying cash for investment. In contrast, policy uncertainty has a positive effect on the value of cash to shareholders of firms with low growth opportunities because it enables these firms to exploit profitable acquisition opportunities while discouraging them from overinvesting in capital expenditures.

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)0.002
metaresearch head score (Gemma)0.019
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
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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