Modified Jones Discretionary Accrual Model

Install and import#

bash

npm install fintech-algorithms

import { modifiedJonesDiscretionaryAccrualModel } from "fintech-algorithms/fundamental-analysis-and-valuation/quality-and-distress/modified-jones-discretionary-accrual-model";

Signature#

modifiedJonesDiscretionaryAccrualModel(data)

Fits the modified Jones accrual model on an industry-year peer group, excluding the target firm, then applies the fitted coefficients to the target to split its scaled total accruals into non-discretionary and discretionary parts.

Parameters#

Name	Type	Notes
`data`	`{ target_id: string; industry_year_sample: { entity_id: string; prior_total_assets: number; total_accruals: number; revenue_change: number; receivables_change: number; net_ppe: number }[] }`	`target_id` names the firm being measured and must be a non-empty string present in the sample. `industry_year_sample` must be an array of at least five records with unique `entity_id` values. Each record supplies `prior_total_assets` as the scaler, `total_accruals` as the regressand, and `revenue_change`, `receivables_change` and `net_ppe` as the regressors.

Returns#

{ state: string; method: string; target_id: string; estimation_count: number; coefficients: { inverse_assets: number; revenue_less_receivables: number; net_ppe: number }; peer_residuals: number[]; scaled_total_accruals: number; nondiscretionary_accruals: number; discretionary_accruals: number; absolute_discretionary_accruals: number; interpretation: string }

estimation_count is the number of peers used, which is the sample size less the target. coefficients holds the three fitted slopes on inverse prior assets, revenue change net of receivables change, and net PPE, all scaled by prior assets. peer_residuals are the estimation-sample residuals. scaled_total_accruals is the target's accruals over its prior assets, nondiscretionary_accruals is the fitted value and discretionary_accruals is the difference, with absolute_discretionary_accruals its magnitude. interpretation notes that the result is a signed residual with no universal cutoff. method is modified-jones-1995-peer-estimation and state is calculated.

Errors#

When data is not a plain object — throws TypeError
When target_id is missing, not a string, or empty — throws TypeError
When any numeric field read is missing or not a finite number — throws TypeError
When industry_year_sample is not an array, holds fewer than five records, or contains a non-object entry — throws RangeError
When entity_id values are not unique, or none of them equals target_id — throws RangeError
When prior_total_assets on any record used is zero or negative — throws RangeError
When the peer regression has no more observations than predictors, or its design matrix is singular — throws RangeError

Complexity: time O(n), space O(n).

Worked example#

executed 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

{
  "target_id": "TARGET",
  "industry_year_sample": [
    {
      "entity_id": "TARGET",
      "prior_total_assets": 900,
      "total_accruals": 48,
      "revenue_change": 130,
      "receivables_change": 35,
      "net_ppe": 390
    },
    {
      "entity_id": "P1",
      "prior_total_assets": 700,
      "total_accruals": 25,
      "revenue_change": 80,
      "receivables_change": 12,
      "net_ppe": 260
    },
    {
      "entity_id": "P2",
      "prior_total_assets": 820,
      "total_accruals": 34,
      "revenue_change": 110,
      "receivables_change": 18,
      "net_ppe": 330
    }
  ]
}

Call#

modifiedJonesDiscretionaryAccrualModel(data)

Returns#

object with 11 fields: state, method, target_id, estimation_count, coefficients, peer_residuals, scaled_total_accruals, nondiscretionary_accruals, …

{
  "state": "calculated",
  "method": "modified-jones-1995-peer-estimation",
  "target_id": "TARGET",
  "estimation_count": 7,
  "coefficients": {
    "inverse_assets": -0.5874747557833828,
    "revenue_less_receivables": 0.22667306767603043,
    "net_ppe": 0.033894017127085604
  },
  "peer_residuals": [
    0.0019446595725300733,
    0.0031079596068906493,
    0.002200572287491008,
    -0.0015260908118726463,
    -0.002277193589259173,
    -0.0029399533725206935
  ],
  "scaled_total_accruals": 0.05333333333333334,
  "nondiscretionary_accruals": 0.03796125928111433,
  "discretionary_accruals": 0.015372074052219006,
  "absolute_discretionary_accruals": 0.015372074052219006,
  "interpretation": "signed residual; no universal manipulation cutoff"
}

Other exports#

This module also exports calculate, altmanZScore, piotroskiFScore, beneishMScore, sloanAccrualMeasure, ohlsonOScore, zmijewskiXScore, springateSScore, tafflerZScore, fulmerHScore, groverGScore, dechowFScoreForMisstatementRisk, dechowDichevAccrualQuality. 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.

Diagrams#

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

Detecting Earnings Management — Patricia M. Dechow, Richard G. Sloan, and Amy P. Sweeney
Beginners' Guide to Financial Statements — U.S. Securities and Exchange Commission
Conceptual Framework for Financial Reporting — International Accounting Standards Board
Evidence boundary

Modified Jones Discretionary Accrual Model

Install and import#

Signature#

Parameters#

Returns#

Errors#

Worked example#

Input#

Call#

Returns#

Other exports#

Diagrams#

How it works#

References#

The rest of the Quality and Distress family#