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Record W2980409783 · doi:10.1108/ara-03-2019-0076

Compliance costs and comparability benefits of cross-listing

2019· article· en· W2980409783 on OpenAlexaff
Shiheng Wang, Serena Wu

Bibliographic record

VenueAsian Review of Accounting · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsComparabilityAccountingBusinessCross listingListing (finance)HarmonizationInternational Financial Reporting StandardsFinanceCorporate governance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine two channels through which accounting standard differences could affect cross-listing: compliance costs and/or comparability benefits. Design/methodology/approach The authors use two settings to disentangle the two channels. First, financial reporting requirements are more stringent for cross-listings via direct listings than cross-listings via depositary receipts; as a result, the effect of compliance costs (if any) would be manifested differently in the two venues of cross-listings. Second, some host countries allow foreign firms to report under International Financial Reporting Standards (IFRS) without mandating IFRS for domestic firms; compared to host countries that mandate IFRS for both domestic and foreign firms, these IFRS-permitting countries provide a setting to test the importance of comparability benefits while holding constant compliance costs. Findings The authors find that prior to IFRS adoption, direct listings decrease with accounting standards differences between two countries while depositary receipts increase with such differences, consistent with the costs of complying with host country’s accounting standards affecting firms’ cross-listing decisions. After the harmonization of accounting standards, the authors find that IFRS-mandating host countries gain cross-listings from other IFRS-mandating jurisdictions, while IFRS-permitting countries do not experience such gains. These combined results suggest that accounting related compliance costs and comparability benefits both influence cross-listing decisions. Originality/value The paper employs unique settings that enable an in-depth examination of the role of compliance costs vs that of comparability benefits on cross-listing decisions. The settings employed by the authors allow them to disentangle the two channels and provide an important insight that accounting standard-related compliance costs and comparability benefits both affect cross-listing decisions.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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