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Record W2016826003 · doi:10.1506/v4rb-cdxj-q216-1406

Operational Restructuring Charges and Post‐Restructuring Performance*

2004· article· en· W2016826003 on OpenAlexvenueno aff
Rowland K. Atiase, David E. Platt, Senyo Y. Tse

Bibliographic record

VenueContemporary Accounting Research · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringEarningsCash flowBusinessEconomicsMonetary economicsAccountingFinance

Abstract

fetched live from OpenAlex

Abstract Firms incur restructuring charges as a result of actions intended to improve their operating performance. However, there is little evidence on whether restructuring charges are associated with improved performance. We examine a sample of firms reporting restructuring in 1991‐93 and find that the restructuring firms' earnings increase over the levels immediately before restructuring. Compared with a control sample of firms that report no restructuring, the restructuring firms improve their earnings and operating income, but evidence for improvements in cash flow from operations is mixed. In regression analysis, we find that restructuring charges are significantly positively associated with post‐restructuring changes in earnings relative to the restructuring year, but this association is largely driven by firms with multiple restructurings and firms reporting losses in the restructuring year. We find no association between restructuring charges and post‐restructuring changes in earnings relative to the year before restructuring. Restructuring charges are significantly positively associated with post‐restructuring changes in operating income and cash flow from operations for firms with multiple restructurings. In summary, restructuring charges are associated with improved earnings, but our results suggest that restructuring in the early 1990s did not necessarily guarantee improved operating performance.

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)0.000
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 score0.031
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.272
Teacher spread0.226 · 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

Citations74
Published2004
Admission routes1
Has abstractyes

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