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Record W2330595743 · doi:10.1108/mf-01-2015-0003

The corporate governance and financing of small-cap firms in Canada

2016· article· en· W2330595743 on OpenAlexaffabout
Christina Atanasova, Evan Gatev, Daniel Shapiro

Bibliographic record

VenueManagerial Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCorporate governanceEndogeneityCapital structureBusinessLeverage (statistics)AccountingCorporate financeEquity (law)Stock exchangeFinanceEnterprise valueShareholder valueDebtShareholderEconomics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to examine the interaction between corporate governance and capital structure for small publicly traded firms in Canada. Design/methodology/approach – The authors hand-collect data for all companies listed on the Canadian junior stock exchange and construct measures of corporate governance. The authors focus on a time period when the sample firms were unregulated in their governance choices. Since firms decide simultaneously on the level of corporate governance provisions and capital structure, the authors use simultaneous equation models as well as instrumental variables analysis to address endogeneity. Findings – The authors find that a strong relation exists between small-firm capital structure and corporate governance practices. Firms with low level of collateralizable assets have low leverage and chose better corporate governance provisions. All else equal, the firms with better corporate governance are more likely to issue new equity than debt. Overall the results support theories that predict a link between corporate governance and financing policy, where small-cap firms with low debt capacity incur costly shareholder protection to facilitate access to equity financing. Originality/value – The authors contribute to prior research by providing the first empirical evidence on the choice and impact of corporate governance on capital structure for junior small- and micro-cap firms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.360
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.166
Teacher spread0.152 · 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.

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

Citations10
Published2016
Admission routes2
Has abstractyes

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