ScholarGate
Asystent

Porównaj metody

Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.

Beneish M-Score: wykrywanie manipulacji wynikami finansowymi×Analiza DuPont×
DziedzinaFinanseFinanse
RodzinaRegression modelRegression model
Rok powstania19992008
TwórcaMessod BeneishDuPont Corporation; Soliman
TypProbabilistic forensic accounting modelProfitability decomposition framework
Źródło pierwotneBeneish, 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 ↗
Inne nazwyBeneish Model, M-Score Model, Earnings Manipulation Score, Beneish M-SkoruDuPont Decomposition, DuPont Identity, Return on Equity Decomposition, DuPont Analizi
Pokrewne32
PodsumowanieThe 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.
ScholarGateZbiór danych
  1. v1
  2. 1 Źródła
  3. PUBLISHED
  1. v1
  2. 1 Źródła
  3. PUBLISHED

Przejdź do wyszukiwania Pobierz slajdy

ScholarGatePorównaj metody: Beneish M-Score · DuPont Analysis. Pobrano 2026-06-19 z https://scholargate.app/pl/compare