# Ohlson O-Score

`D18-F04-A05` · Fundamental Analysis and Valuation → Quality and Distress · archetype `record-transform` · difficulty 3/5 · verification **verified**

Full page: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/quality-and-distress/ohlson-o-score/
Agent skill: `npx skills add IslamBaraka90/Fintech-Algorithms-Library` — https://docs.thefintechbuilder.com/guides/agent-skill/

## Install and import

```bash
npm install fintech-algorithms
```

```ts
import { ohlsonOScore } from "fintech-algorithms/fundamental-analysis-and-valuation/quality-and-distress/ohlson-o-score";
```

## Signature

```ts
ohlsonOScore(data)
```

Computes Ohlson's 1980 Model 1 O-Score from nine accounting variables, including the two indicator terms for negative equity and two consecutive loss years, and maps the linear index through a logistic function to a bankruptcy probability.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `data` | `{ total_assets: number; price_level_index: number; total_liabilities: number; current_assets: number; current_liabilities: number; working_capital: number; net_income: number; prior_net_income: number; funds_from_operations: number }` | yes | One accounting record plus the deflator. `total_assets` is divided by `price_level_index` before the logarithm that forms the SIZE term; `total_liabilities`, `current_assets`, `current_liabilities`, `working_capital`, `net_income`, `prior_net_income` and `funds_from_operations` supply the remaining eight variables. |

## Returns

`{ state: string; method: string; variables: { size: number; tlta: number; wcta: number; clca: number; oeneg: number; nita: number; futl: number; intwo: number; chin: number }; o_score: number; logistic_probability: number; screen: string; cutoff_probability: number }`

`variables` holds the nine terms, with `oeneg` set to 1 when total liabilities exceed total assets and `intwo` set to 1 when both current and prior net income are negative. `o_score` applies the intercept -1.32 and coefficients -0.407 SIZE, 6.03 TLTA, -1.43 WCTA, 0.0757 CLCA, -1.72 OENEG, -2.37 NITA, -1.83 FUTL, 0.285 INTWO and -0.521 CHIN. `logistic_probability` is the logistic transform of that index, `cutoff_probability` is 0.038 and `screen` is `above-original-cutoff` when the probability exceeds it and `below-original-cutoff` otherwise. `method` is `ohlson-1980-model-1` and `state` is `calculated`.

## Errors

- When data is not a plain object — throws TypeError
- When any field read is missing or not a finite number — throws TypeError
- When total_assets, price_level_index, total_liabilities or current_assets is zero or negative — throws RangeError
- When current_liabilities is negative — throws RangeError
- When net_income and prior_net_income are both zero, leaving the CHIN denominator at zero — throws RangeError

## Complexity

Time `O(1)`, space `O(1)`.

## Worked example

Captured by running this function on the input its own test provides. Real output of real code — but not asserted against a published figure.

### Input

`data`:

```json
{
  "total_assets": 1000,
  "price_level_index": 1,
  "total_liabilities": 550,
  "current_assets": 450,
  "current_liabilities": 250,
  "working_capital": 200,
  "net_income": 80,
  "prior_net_income": 50,
  "funds_from_operations": 110
}
```

### Call

```ts
ohlsonOScore(data)
```

### Returns

object with 7 fields: state, method, variables, o_score, logistic_probability, screen, cutoff_probability

```json
{
  "state": "calculated",
  "method": "ohlson-1980-model-1",
  "variables": {
    "size": 6.907755278982137,
    "tlta": 0.55,
    "wcta": 0.2,
    "clca": 0.5555555555555556,
    "oeneg": 0,
    "nita": 0.08,
    "futl": 0.2,
    "intwo": 0,
    "chin": 0.23076923076923078
  },
  "o_score": -1.734731612220943,
  "logistic_probability": 0.14998335476443922,
  "screen": "above-original-cutoff",
  "cutoff_probability": 0.038
}
```

## Other exports

`calculate`, `altmanZScore`, `piotroskiFScore`, `beneishMScore`, `sloanAccrualMeasure`, `zmijewskiXScore`, `springateSScore`, `tafflerZScore`, `fulmerHScore`, `groverGScore`, `dechowFScoreForMisstatementRisk`, `dechowDichevAccrualQuality`, `modifiedJonesDiscretionaryAccrualModel`. Every module additionally exports `run` as an alias of its primary
function, and a `meta` object carrying its catalog id, domain, family, shape and article URL.

## Verification and provenance

Tier: **verified** (via D).

The worked example below is the figure published in this algorithm's article, replayed and asserted by the test suite on every build. The arithmetic cannot drift without the build failing.

Both tiers guarantee the signature. Full explanation: https://docs.thefintechbuilder.com/guides/verification/

Generated from the docs.json payload shipped inside fintech-algorithms@0.13.0.
The signature and parameter list are checked against the compiled implementation at build time,
so a description that contradicts the code fails the build rather than reaching this file.

## Links

- Article (how it works, step by step): https://thefintechbuilder.com/fundamental-analysis-and-valuation/quality-and-distress/ohlson-o-score/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/fundamental-analysis-and-valuation/quality-and-distress/ohlson-o-score/impl.ts
- Package on npm: https://www.npmjs.com/package/fintech-algorithms
- Domain index for agents: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/llms.txt