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Record W2985547047 · doi:10.2308/tar-2016-0235

The Valuation of Discontinued Operations and Its Effect on Classification Shifting

2019· article· en· W2985547047 on OpenAlexaff
Steven E. Kaplan, David G. Kenchington, Brian S. Wenzel

Bibliographic record

VenueThe Accounting Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsValuation (finance)EarningsEarnings before interest and taxesIncome taxOperating expenseBusinessTax deductionLabour economicsEconomicsFinanceAccountingMonetary economicsState income taxGross incomePublic economicsTax reform

Abstract

fetched live from OpenAlex

ABSTRACT Research documents that firms shift operating expenses into income-decreasing, but not income-increasing, discontinued operations. We argue that valuation considerations explain this asymmetric result, as acquirers are likely to value the earnings of income-increasing discontinued operations more highly than the earnings of income-decreasing discontinued operations. Using a large sample of hand-collected data, we show that pre-tax earnings and operating expenses are significantly more value-relevant for income-increasing discontinued operations, supporting our economic explanation for why firms do not shift operating expenses into income-increasing discontinued operations. Additional analysis shows that in situations where managers are constrained from shifting operating expenses due to valuation concerns, they shift tax expense (an expense that is less value-relevant) from continuing operations into income-increasing discontinued operations. Overall, we conclude that valuation considerations constrain firms from shifting operating expenses into income-increasing discontinued operations, but do not constrain firms shifting tax expenses.

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)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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 designOther design
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

Citations23
Published2019
Admission routes1
Has abstractyes

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