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Record W3124299238 · doi:10.1506/g8t2-k05v-1850-52u4

Predicting Business Failures in Canada*

2007· article· en· W3124299238 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAccounting Perspectives · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Distress and Bankruptcy Prediction
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEconometricsBankruptcyEconomicsMathematicsFinance

Abstract

fetched live from OpenAlex

ABSTRACT Empirical researchers and practitioners frequently use the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980). This poses a potential problem for practitioners in Canada and researchers working with Canadian data because the Altman and Ohlson models were developed using U.S. data. We compare Canadian bankruptcy prediction models developed by Springate (1978), Altman and Levallee (1980), and Legault and Véronneau (1986) against the Altman and Ohlson models using recent data to determine the robustness of all models over time and the applicability of the Altman and Ohlson models to the Canadian environment. Our results indicate that the models developed by Springate (1978) and Legault and Véronneau (1986) yield similar results to the Ohlson (1980) model while being simpler and requiring less data. The Altman (1968) and Altman and Levallee (1980) models generally have lower performance than the other models. All models have stronger performance with the original coefficients than with re‐estimated coefficients. Our results regarding the Altman and Ohlson models are consistent with Begley, Ming, and Watts (1996), who found that the original version of the Ohlson model is superior to the Altman model and is robust over time. Les chercheurs empiriques et les praticiens ont souvent recours aux modèles de prédiction des faillites élaborés par Altman (1968) et Ohlson (1980). Or, le fait que ces auteurs aient utilisé des données des États‐Unis dans l'élaboration de leurs modèles soulève un problème particulier pour les praticiens canadiens et les chercheurs qui traitent des données canadiennes. Les auteurs comparent les modèles canadiens de prédiction des faillites mis au point par Springate (1978), Altman et Levallée (1980) et Legault et Véronneau (1986) aux modèles proposés par Altman et Ohlson, en se servant de données récentes pour évaluer la robustesse de tous ces modèles dans le temps et l'applicabilité des modèles d'Altman et Ohlson au contexte canadien. L'analyse révèle que les modèles de Springate (1978) et de Legault et Véronneau (1986) produisent des résultats similaires à ceux du modèle d'Ohlson (1980), bien qu'ils soient plus simples et exigent moins de données. De façon générale, les modèles d'Altman (1968) et d'Altman et Levallee (1980) sont moins performants que les autres modèles. Tous les modèles sont plus efficaces lorsqu'ils font usage des coefficients initiaux que lorsqu'ils sont appliqués à de nouvelles estimations des coefficients. Les résultats obtenus en ce qui a trait aux modèles d'Altman et d'Ohlson corroborent ceux de Begley, Ming et Watts (1996) qui constatent que la version initiale du modèle d'Ohlson est supérieure au modèle d'Altman et résiste au passage du temps.

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)0.001
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 score0.025
Threshold uncertainty score0.824

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 studies0.0000.000
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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