ScholarGate
Assistent

Sammenlign metoder

Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.

Beneish M-Score: Oppdagelse av resultatmanipulasjon×DuPont-analyse×
FagfeltFinansFinans
FamilieRegression modelRegression model
Opprinnelsesår19992008
OpphavspersonMessod BeneishDuPont Corporation; Soliman
TypeProbabilistic forensic accounting modelProfitability decomposition framework
Opprinnelig kildeBeneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36. DOI ↗Soliman, M. T. (2008). The use of DuPont analysis by market participants. The Accounting Review, 83(3), 823–853. DOI ↗
AliasBeneish Model, M-Score Model, Earnings Manipulation Score, Beneish M-SkoruDuPont Decomposition, DuPont Identity, Return on Equity Decomposition, DuPont Analizi
Relaterte32
SammendragThe Beneish M-Score is a statistical model developed by Messod Beneish in 1999 to identify whether a company has manipulated its reported earnings. The model combines eight financial-statement ratios into a single composite score using coefficients estimated from a probit regression on a sample of detected earnings manipulators. A score above −2.22 indicates a heightened probability of manipulation, making the M-Score a widely used tool in forensic accounting and investment due-diligence.DuPont Analysis is a financial performance framework that decomposes Return on Equity (ROE) into three multiplicative components: net profit margin, asset turnover, and the equity multiplier. Originally developed by engineers at DuPont Corporation in the early 1920s, the method gained renewed academic prominence through Soliman (2008), who demonstrated that market participants exploit DuPont decompositions to forecast future earnings and to distinguish sustainable from transient profitability.
ScholarGateDatasett
  1. v1
  2. 1 Kilder
  3. PUBLISHED
  1. v1
  2. 1 Kilder
  3. PUBLISHED

Gå til søk Last ned lysbilder

ScholarGateSammenlign metoder: Beneish M-Score · DuPont Analysis. Hentet 2026-06-19 fra https://scholargate.app/no/compare