# Taffler Z-Score

`D18-F04-A08` · 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/taffler-z-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 { tafflerZScore } from "fintech-algorithms/fundamental-analysis-and-valuation/quality-and-distress/taffler-z-score";
```

## Signature

```ts
tafflerZScore(data)
```

Computes the transformed Taffler UK industrial Z-Score from profitability, working-capital adequacy, financial risk and the no-credit interval, and reports which side of the zero cutoff the score falls on.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `data` | `{ current_liabilities: number; total_liabilities: number; total_assets: number; profit_before_tax: number; current_assets: number; quick_assets: number; daily_operating_expenses: number }` | yes | One accounting record. `profit_before_tax` is scaled by `current_liabilities`, `current_assets` by `total_liabilities`, and `current_liabilities` by `total_assets`. The no-credit interval is `quick_assets` less `current_liabilities` divided by `daily_operating_expenses`, so that input must already be a per-day figure. |

## Returns

`{ state: string; method: string; ratios: { pbt_to_current_liabilities: number; current_assets_to_total_liabilities: number; current_liabilities_to_total_assets: number; no_credit_interval_days: number }; z_score: number; screen: string; index_cutoff: number }`

`ratios` holds the four terms, with `no_credit_interval_days` expressed in days. `z_score` applies the constant 3.2 with coefficients 12.18, 2.5, -10.68 and 0.0289 in that order. `index_cutoff` is 0 and `screen` is `distress-side` for a negative score and `non-distress-side` otherwise. `method` is `taffler-1983-uk-industrial-transformed` 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 current_liabilities, total_liabilities, total_assets or daily_operating_expenses is zero or negative — 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
{
  "current_liabilities": 250,
  "total_liabilities": 550,
  "total_assets": 1000,
  "profit_before_tax": 140,
  "current_assets": 450,
  "quick_assets": 320,
  "daily_operating_expenses": 3
}
```

### Call

```ts
tafflerZScore(data)
```

### Returns

object with 6 fields: state, method, ratios, z_score, screen, index_cutoff

```json
{
  "state": "calculated",
  "method": "taffler-1983-uk-industrial-transformed",
  "ratios": {
    "pbt_to_current_liabilities": 0.56,
    "current_assets_to_total_liabilities": 0.8181818181818182,
    "current_liabilities_to_total_assets": 0.25,
    "no_credit_interval_days": 23.333333333333332
  },
  "z_score": 10.07058787878788,
  "screen": "non-distress-side",
  "index_cutoff": 0
}
```

## Other exports

`calculate`, `altmanZScore`, `piotroskiFScore`, `beneishMScore`, `sloanAccrualMeasure`, `ohlsonOScore`, `zmijewskiXScore`, `springateSScore`, `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/taffler-z-score/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/fundamental-analysis-and-valuation/quality-and-distress/taffler-z-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