Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Beneish M-Score: Detectie van Winstmanipulatie× | Logistische Regressie× | |
|---|---|---|
| Vakgebied≠ | Financiering | Onderzoeksstatistiek |
| Familie≠ | Regression model | Process / pipeline |
| Jaar van ontstaan≠ | 1999 | 1958 |
| Grondlegger≠ | Messod Beneish | David Roxbee Cox |
| Type≠ | Probabilistic forensic accounting model | Method |
| Oorspronkelijke bron≠ | Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Aliassen≠ | Beneish Model, M-Score Model, Earnings Manipulation Score, Beneish M-Skoru | logit model, binomial logistic regression, LR |
| Verwant | 3 | 3 |
| Samenvatting≠ | The 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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