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Record W3035320093 · doi:10.6000/1929-7092.2020.09.21

Macroeconomic Uncertainty and Cash Holdings of Top 50 Listed Firms in Vietnam Stock Exchange

2020· article· en· W3035320093 on OpenAlexvenueno aff
Van Dung Ha

Bibliographic record

VenueJournal of Reviews on Global Economics · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary economicsStock exchangeCash flowCash on cash returnOperating cash flowExchange rateEconomicsCash and cash equivalentsBusinessLeverage (statistics)Financial economicsFinancial systemFinance

Abstract

fetched live from OpenAlex

This study investigates the impact of macroeconomic uncertainty on cash holdings of top 50 listed firms in Vietnam Stock Exchange.The average of natural logarithm of inflation rate, change in exchange rate, deficit to GNP, and external debt to GNP ratio is used for macroeconomic uncertainty while the ratio of cash and cash equivalent to total assets measures firm cash holdings.Using a dataset of 300 observations from top 50 listed firms in both Ho Chi Minh City Stock Exchange and Hanoi stock Exchange from 2013-2018, the paper employs the basic quantitative methods of Pooled Ordinary Least Squared, Fxed effects model, and Random effects model for analysis.The results indicate that higher macroeconomic uncertainty may lead to higher cash holdings of listed firms in Vietnam Stock Exchange.Some other determinants of firm cash holdings can be named as firm size, the ratio of market and booked value of firm, cash flow, net working capital, firm investment, leverage, and firm dividend.One macroeconomic indicator (the growth rate of money supply) is also found to have positive impacts of cash holdings of firms in Vietnam Stock Exchange.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.242
Teacher spread0.208 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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