# Beneish M-Score

`D18-F04-A03` · 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/beneish-m-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 { beneishMScore } from "fintech-algorithms/fundamental-analysis-and-valuation/quality-and-distress/beneish-m-score";
```

## Signature

```ts
beneishMScore(data)
```

Builds the eight Beneish indices from a current and prior-year accounting pair and combines them into the M-Score with the 1999 eight-variable coefficients, reporting whether the score clears the screening cutoff.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `data` | `{ sales: number; prior_sales: number; receivables: number; prior_receivables: number; cost_of_goods_sold: number; prior_cost_of_goods_sold: number; current_assets: number; prior_current_assets: number; net_ppe: number; prior_net_ppe: number; securities: number; prior_securities: number; total_assets: number; prior_total_assets: number; depreciation: number; prior_depreciation: number; sga_expense: number; prior_sga_expense: number; current_liabilities: number; prior_current_liabilities: number; long_term_debt: number; prior_long_term_debt: number; income_from_continuing_operations: number; operating_cash_flow: number }` | yes | A flat record holding each line item for the current year and its prior-year twin. `sales`, `prior_sales`, `receivables` and `prior_receivables` drive DSRI; `cost_of_goods_sold` and its prior drive GMI; `current_assets`, `net_ppe`, `securities`, `total_assets` and their priors drive AQI; `depreciation` and `prior_depreciation` with net PPE drive DEPI; `sga_expense` and its prior drive SGAI; `current_liabilities`, `long_term_debt` and their priors drive LVGI; and `income_from_continuing_operations` less `operating_cash_flow` over `total_assets` gives TATA. |

## Returns

`{ state: string; method: string; indices: { dsri: number; gmi: number; aqi: number; sgi: number; depi: number; sgai: number; lvgi: number; tata: number }; contributions: { intercept: number; dsri: number; gmi: number; aqi: number; sgi: number; depi: number; sgai: number; tata: number; lvgi: number }; m_score: number; screen: string; cutoff: number }`

`indices` holds the eight raw indices and `contributions` holds each one after its coefficient, together with the -4.84 `intercept`. The weights applied are 0.92 DSRI, 0.528 GMI, 0.404 AQI, 0.892 SGI, 0.115 DEPI, -0.172 SGAI, 4.679 TATA and -0.327 LVGI. `m_score` is their sum, `cutoff` is -1.78 and `screen` is `above-screening-cutoff` when the score exceeds it and `below-screening-cutoff` otherwise. `method` is `beneish-1999-eight-variable` 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 sales, prior_sales, total_assets or prior_total_assets is zero or negative — throws RangeError
- When receivables or prior_receivables is negative — throws RangeError
- When the current or prior gross margin computes to zero — throws RangeError
- When the prior asset-quality denominator is zero — throws RangeError
- When a depreciation rate denominator, or a DSRI, SGAI or LVGI denominator, is 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
{
  "sales": 1200,
  "prior_sales": 1000,
  "receivables": 180,
  "prior_receivables": 100,
  "cost_of_goods_sold": 720,
  "prior_cost_of_goods_sold": 650,
  "current_assets": 500,
  "prior_current_assets": 430,
  "net_ppe": 400,
  "prior_net_ppe": 390,
  "securities": 10,
  "prior_securities": 10,
  "total_assets": 1000,
  "prior_total_assets": 900
}
```

Showing 14 of 24 fields.

### Call

```ts
beneishMScore(data)
```

### Returns

object with 7 fields: state, method, indices, contributions, m_score, screen, cutoff

```json
{
  "state": "calculated",
  "method": "beneish-1999-eight-variable",
  "indices": {
    "dsri": 1.4999999999999998,
    "gmi": 0.8749999999999999,
    "aqi": 1.1571428571428575,
    "sgi": 1.2,
    "depi": 0.947565543071161,
    "sgai": 0.9895833333333333,
    "lvgi": 0.9519230769230771,
    "tata": 0.035
  },
  "contributions": {
    "intercept": -4.84,
    "dsri": 1.38,
    "gmi": 0.46199999999999997,
    "aqi": 0.46748571428571445,
    "sgi": 1.0704,
    "depi": 0.10897003745318352,
    "sgai": -0.1702083333333333,
    "tata": 0.16376500000000002,
    "lvgi": -0.3112788461538462
  },
  "m_score": -1.6688664277482819,
  "screen": "above-screening-cutoff",
  "cutoff": -1.78
}
```

## Other exports

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