Ohlson O-Score¶
Estimate a firm's near-term bankruptcy probability by feeding nine accounting and size variables into Ohlson's fitted conditional-logit model, with interpretation constrained to its population, horizon, and data definitions.
Core Idea¶
The Ohlson O-Score is the linear predictor from James Ohlson's 1980 conditional-logit models of corporate bankruptcy. It combines nine terms derived from firm size, leverage, working capital, liquidity, profitability, operating cash flow, recent losses, and change in income. A logistic transformation maps the linear score to an estimated probability under the selected model and prediction horizon.
The construct is a historically estimated statistical model, not a timeless accounting identity. Its coefficients, scaling conventions, sample of U.S. industrial firms, reporting-era definitions, and one- or two-year horizon are part of its interpretation. A larger score generally indicates greater modeled distress risk, but calibration can drift across jurisdictions, sectors, accounting standards, and periods.
Scope of Application¶
The O-Score is a literal financial-risk instrument when its original or explicitly re-estimated logistic specification is applied to compatible firm accounting data.
- Academic bankruptcy research. Benchmarking accounting-based failure prediction against later models.
- Credit screening. Ranking firms for deeper review when the population has been locally validated.
- Portfolio surveillance. Tracking changes in modeled distress risk across reporting periods.
- Audit and going-concern analytics. Supplying one quantitative signal alongside qualitative evidence and professional judgment.
- Model-risk study. Examining calibration drift across sectors, jurisdictions, and accounting regimes.
- Historical replication. Reproducing Ohlson's conditional-logit design and horizons from declared variable definitions.
Clarity¶
Name the exact Ohlson model and horizon, reproduce all nine variable definitions and signs, state the price-level normalization, and distinguish the linear score from its logistic probability transform. Report how missing or denominator-edge cases are handled. Do not call an arbitrary cutoff 'the' O-Score threshold. For current use, give target-population discrimination and calibration evidence and say whether coefficients were preserved or re-estimated.
Manages Complexity¶
The score compresses a multidimensional financial statement into one reproducible risk signal, allowing large firm sets to be ranked and triaged. That compression exposes a stable audit trail from inputs to output but discards narrative context, nonlinear interactions, reporting quality, and new regimes. The concise score becomes dangerous when portability is assumed: a mathematically correct computation can still produce a poorly calibrated probability.
Abstract Reasoning¶
- Fix the event definition, prediction horizon, and eligible firm population. 2. Map financial-statement fields to the original variable definitions and units. 3. Compute ratios, binary indicators, and the income-change term with declared edge-case rules. 4. Apply the specified coefficient vector to obtain the linear O-score. 5. Use the logistic transform only under the corresponding model interpretation. 6. Validate discrimination and calibration on the target population and period.
Knowledge Transfer¶
The model transfers literally only where compatible firm-level accounting fields and a bankruptcy outcome exist; even there, revalidation is required. Its broader parent is Measurement: heterogeneous indicators are combined into a scalar estimate under a declared calibration. Classification is a downstream use, not the identity of the score. Applying the formula to households, projects, or sovereigns without re-estimation is analogy or model misuse.
Relationships to Other Abstractions¶
Current abstraction Ohlson O-Score Domain-specific
Parents (1) — more general patterns this builds on
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Ohlson O-Score is a kind of Measurement Prime
Measurement is the strict parent because the O-Score constructs a scalar risk estimate from defined observables and a calibration model.
Hierarchy path (1) — routes to 1 parentless root
- Ohlson O-Score → Measurement
Neighborhood in Abstraction Space¶
Ohlson O-Score sits in a sparse region of the domain-specific corpus (95th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.
Family — Unclustered & Miscellaneous (1565 abstractions)
Nearest neighbors
- Current Ratio — 0.81
- Receivables turnover ratio — 0.77
- Cash-flow-to-debt ratio — 0.77
- Dividend cover — 0.76
- DuPont Analysis — 0.76
Computed from structural-signature embeddings · 2026-09-08