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Record W3092864774 · doi:10.1108/ijaim-03-2020-0034

Risk reporting in financial crises: a tale of two countries

2020· article· en· W3092864774 on OpenAlexaffabout
Kaouthar Lajili, Michael Dobler, Daniel Zéghal, Mitchell John Bryan

Bibliographic record

VenueInternational Journal of Accounting and Information Management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAccountingSample (material)GermanBusinessOriginalityFinancial crisisRisk managementActuarial scienceEconomicsFinancePolitical scienceGeography

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate the attributes and information content of risk reporting in two different institutional and regulatory, namely, Canadian and German, settings during the period surrounding the financial crisis of 2008. Design/methodology/approach For a matched sample of manufacturing firms in the period 2006–2010, this study conducts a detailed content analysis of annual reports to assess and compare the volume and patterns of risk disclosures. Panel regressions are used to explore how risk disclosures related to corporate risk proxies and performance indicators. Findings Over the sample period, Canadian and German firms increase the volume but largely maintain the patterns of risk disclosures. Risk disclosures relate to corporate risk proxies but are not incrementally informative to assess firm performance. Originality/value The paper contributes to research on risk reporting by providing detailed cross-country evidence for a period particularly shaped by significant risk. The findings have implications for the regulation and usefulness of risk reporting.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.006
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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 designNot applicable
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

Citations8
Published2020
Admission routes2
Has abstractyes

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