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Record W4290976987 · doi:10.2308/bria-2021-037

How Do Auditors Assess Key Inputs in a Discounted Cash Flow Model of Goodwill?

2022· article· en· W4290976987 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehavioral Research in Accounting · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsGoodwillAuditAccountingValuation (finance)Discounted cash flowCash flowKey (lock)BusinessActuarial scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

ABSTRACT Using verbal protocol analysis, this study examines how 21 experienced auditors from four different firms assess the seven key inputs in a discounted cash flow (DCF) model used by management to value goodwill. The analysis compares the auditors' processes against a theoretical model derived from an analysis of accounting and auditing standards and authoritative sources of valuation methodology and identifies systematic omissions and inaccurate applications of key audit steps. It also relates those issues to audit outcomes at the individual input and the overall goodwill evaluation levels. The study's findings can help regulators, standard setters, practitioners and academics to better understand the limitations of auditors' competencies so that they can design strategies for mitigating them.

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.

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)0.006
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0000.002
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 head0.112
GPT teacher head0.359
Teacher spread0.247 · 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