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Record W3153795534 · doi:10.5267/j.ac.2021.4.017

Determinants of corporate cash holdings: Evidence from the Moroccan market

2021· article· en· W3153795534 on OpenAlexvenueno aff
Boubker Mouline, Hicham Sadok

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCash flow forecastingCash managementCash flow statementCash flowOperating cash flowMarket liquidityLeverage (statistics)BusinessCash conversion cycleCashCapital structureStock exchangeFinanceWorking capitalDebtEconomicsFree cash flow

Abstract

fetched live from OpenAlex

Determining cash holdings is amongst the most important financial decisions made by heads of corporations. This decision relies on theoretical convictions and views as well as firm specific characteristics. This article analyzes the determinants of cash management in Moroccan corporations. By mobilizing all the theories of optimal financial structure, our research attempts to focus on the field of knowledge in the financial management of cash surpluses. No analysis has been carried out concerning cash and cash equivalents in Moroccan firms. These results could, therefore, contextualize the existing knowledge in this research theme and better understand the behavior of companies and their main trends in terms of cash flow, as well as the objectives and motivations of managers. The sample studied consists of 42 Moroccan companies, which are all publicly traded on the Casablanca Stock Exchange over 13 years (2007-2019). This research uses an empirical econometric study based on a positivist approach with a hypothetical-deductive method. We use panel regression analysis and perform all the necessary tests to determine the exact nature of this dataset. Our results show some evidence that a strong positive correlation exists between liquidity level and cash-flow as well as family shareholding. It is also found that the cash holdings of these companies are significantly negatively affected by how large or small the firm is, working capital requirement, debt leverage, as well as growth opportunity of the firm.

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)0.002
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 score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.035
GPT teacher head0.224
Teacher spread0.189 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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