# Peer-Multiple Regression

`D18-F03-A05` · Fundamental Analysis and Valuation → Relative Valuation · archetype `record-transform` · difficulty 5/5 · verification **verified**

Full page: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/relative-valuation/peer-multiple-regression/
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 { peerMultipleRegression } from "fintech-algorithms/fundamental-analysis-and-valuation/relative-valuation/peer-multiple-regression";
```

## Signature

```ts
peerMultipleRegression(rawInputs)
```

Fits an ordinary least squares regression of each peer's log multiple on an intercept, expected growth, return on equity and net debt to EBITDA, then exponentiates the target's fitted value into an implied multiple and applies it to the target's valuation metric.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `rawInputs` | `unknown` | yes | Declared `unknown` and narrowed at runtime. It must be a plain object with a `target` object and a `peers` array. Every regression row reads `expected_growth_percent` and `return_on_equity_percent` (percentage points, divided by 100 before fitting) and `net_debt_to_ebitda`, each a finite number of any sign. `peers` holds at least six objects, each also carrying a nonempty and unique string `id` and a positive `multiple`. `target` also carries a positive `valuation_metric` and an optional `current_value`. |

## Returns

`Record<string, unknown>`

`model`, `peer_count`, `feature_order`, `coefficients` keyed `intercept`, `expected_growth_decimal`, `return_on_equity_decimal` and `net_debt_to_ebitda`, `r_squared_log_space` measured against the log multiples rather than the levels, `fitted_peers` (per peer: `id`, `observed_multiple`, `fitted_multiple`, `log_residual`), `target_predicted_log_multiple`, `implied_multiple`, `target_valuation_metric`, `implied_value` (implied multiple times the metric), `premium_discount_to_current` measured against `target.current_value` and null when that is absent, `smearing_correction` fixed at `not-applied`, and `state`, always `valuation-complete`.

## Errors

- When rawInputs, target or a peer row is not a plain object, or a feature or metric is not a finite number — throws TypeError
- When peers is not an array of at least six rows, or a peer id is missing, blank or duplicated — throws RangeError
- When a peer multiple, target.valuation_metric or target.current_value is not greater than zero — throws RangeError
- When the normal-equation matrix is rank deficient, meaning Gaussian elimination finds a pivot of magnitude at or below 1e-12 — throws RangeError

## Complexity

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

## Worked example

This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

### Input

`rawInputs`:

```json
{
  "target": {
    "expected_growth_percent": 11,
    "return_on_equity_percent": 15,
    "net_debt_to_ebitda": 1,
    "valuation_metric": 5,
    "current_value": 35
  },
  "peers": [
    {
      "id": "R1",
      "multiple": 3.6692966676192453,
      "expected_growth_percent": 4,
      "return_on_equity_percent": 8,
      "net_debt_to_ebitda": 3
    },
    {
      "id": "R2",
      "multiple": 4.6043430528623635,
      "expected_growth_percent": 6,
      "return_on_equity_percent": 11,
      "net_debt_to_ebitda": 2.4
    },
    {
      "id": "R3",
      "multiple": 5.124331102904436,
      "expected_growth_percent": 8,
      "return_on_equity_percent": 14,
      "net_debt_to_ebitda": 1.8
    }
  ]
}
```

### Call

```ts
peerMultipleRegression(rawInputs)
```

### Returns

object with 13 fields: model, peer_count, feature_order, coefficients, r_squared_log_space, fitted_peers, target_predicted_log_multiple, implied_multiple, …

```json
{
  "model": "ols-log-multiple-on-growth-roe-and-leverage",
  "peer_count": 8,
  "feature_order": [
    "intercept",
    "expected_growth_decimal",
    "return_on_equity_decimal",
    "net_debt_to_ebitda"
  ],
  "coefficients": {
    "intercept": 1.5364545140362829,
    "expected_growth_decimal": 1.6438862781409074,
    "return_on_equity_decimal": 1.6061067701607943,
    "net_debt_to_ebitda": -0.13216626876457388
  },
  "r_squared_log_space": 0.9870676723439116,
  "fitted_peers": [
    {
      "id": "R1",
      "observed_multiple": 3.6692966676192453,
      "fitted_multiple": 3.7969560280762247,
      "log_residual": -0.0341997004810608
    },
    {
      "id": "R2",
      "observed_multiple": 4.6043430528623635,
      "fitted_multiple": 4.457376616557734,
      "log_residual": 0.032439609592552765
    },
    {
      "id": "R3",
      "observed_multiple": 5.124331102904436,
      "fitted_multiple": 5.232666945553791,
      "log_residual": -0.020921080333833775
    }
  ],
  "target_predicted_log_multiple": 1.8260317513913278,
  "implied_multiple": 6.209198064103231,
  "target_valuation_metric": 5,
  "implied_value": 31.045990320516154,
  "premium_discount_to_current": -0.11297170512810994,
  "smearing_correction": "not-applied",
  "state": "valuation-complete"
}
```

## Other exports

`calculate`, `pEComparableValuation`, `evEbitdaComparableValuation`, `priceToBookValuation`, `pegRatio`. 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 input-expected).

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/relative-valuation/peer-multiple-regression/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/fundamental-analysis-and-valuation/relative-valuation/peer-multiple-regression/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