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Record W1536217346 · doi:10.34989/sdp-2008-16

Financial Constraints and the Cash-Holding Behaviour of Canadian Firms

2021· preprint· en· W1536217346 on OpenAlexaffabout
Darcey McVanel, Nikita Perevalov

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCashFinanceBusinessCash flow forecastingCash flowEconomicsMonetary economics

Abstract

fetched live from OpenAlex

The proportion of assets held by the average Canadian firm in the form of cash has increased steadily since the early 1990s, and is now roughly twice as large as in 1990. The literature has established that the cash-holding behaviour of firms is highly correlated with financial constraints and firm characteristics. The authors use a firm-level data set covering Canadian firms from 1980 to 2006 to understand which firm characteristics are associated with higher cash holdings. They find that financial constraints are likely important for explaining firms' higher cash holdings, and that the recent increase in the cash holdings of Canadian firms can be almost entirely explained by changes in firm characteristics. Specifically, higher recent cash holdings are correlated with the average Canadian firm having become smaller, having more variable cash flow, holding lower levels of cash substitutes, having higher expenditure on research and development, and being more likely to be financially distressed. The authors also find that the average Canadian firm has a cash ratio that is only slightly higher than would be predicted by out-of-sample forecasts over the 1990s and 2000s, though the divergence between the actual and predicted values has been increasing in recent years.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.202
Teacher spread0.185 · 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 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

Citations6
Published2021
Admission routes2
Has abstractyes

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