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Predicting defaults of highly-levered firms with an adapted Altman model

2014· article· en· W1541188564 on OpenAlexaboutno aff
HK Levy, Y Kanat, A Kunin, D Tooshknov, P Tzruya

Bibliographic record

VenueInvestment Analysts Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Distress and Bankruptcy Prediction
Canadian institutionsnot available
Fundersnot available
KeywordsDefaultLeverage (statistics)Real estateBusiness cyclePredictabilityFinancial crisisEconomicsBusinessQuarter (Canadian coin)BankruptcyFinancial economicsEconometricsFinanceMacroeconomicsMathematics

Abstract

fetched live from OpenAlex

The purpose of this paper is to analyse whether applying Altman's model, with specific adjustments to highly levered firms, can reliably predict bankruptcies one quarter, one year, and two years in advance. Our focus on highly levered firms is motivated by leverage-driven theories of financial crises, where highly levered firms are the early defaulters once the expansionary phase of the business cycle turns into a recessionary phase. We find high predictability of defaults of firms in the Real-estate and Holding-companies sectors in Israel one year before the recent crisis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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