방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Beneish M-Score: 부정 회계 탐지× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 재무학 | 연구 통계 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 1999 | 1958 |
| 창시자≠ | Messod Beneish | David Roxbee Cox |
| 유형≠ | Probabilistic forensic accounting model | Method |
| 원전≠ | 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 ↗ |
| 별칭≠ | Beneish Model, M-Score Model, Earnings Manipulation Score, Beneish M-Skoru | logit model, binomial logistic regression, LR |
| 관련 | 3 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|