FactSet Research Systems Beneish M-Score (Annual)
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FactSet Research Systems Historical Beneish M-Score (Annual) Data
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About Beneish M Score
The Beneish M Score helps to uncover companies who are likely to be manipulating their reported earnings. Companies with a higher score are more likely to be manipulators. This is a probabilistic model, so it will not detect manipulators with 100% accuracy.
The best cut-off point depends on the costs mistakenly classifying in one of two ways:
1) Classifying firm that is manipulating earnings as a non-manipulator (Type I error), and
2) Classifying a firm as a manipulator when it actually was not manipulating (Type II Error).
Here are optimal cut-offs according to Beneish, presented as the score followed by the cost of Type I error relative to cost of Type II error):
M Score HTML Table:
|Score||Relative Error Costs|
(Type I:Type II)
|M Score > -1.49||(10:1)||M Score > -1.78||(20:1)||M Score > -1.89||(40+:1)|
Beneish excluded financial institutions from his sample when calculating the M-Score, so extreme care should be taken when looking at M-Scores of financial firms - their business models are different from the manufacturing and other service firms that Beneish used in his study.
If you want more details, here is the original Beneish M Score Paper, or you can learn about our calculation by clicking "Learn More" below.
FDS Beneish M-Score (Annual) Benchmarks
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FDS Beneish M-Score (Annual) Excel Add-In Codes
- Metric Code: beneish_m_score
- Latest data point: =YCP("FDS", "beneish_m_score")
- Last 5 data points: =YCS("FDS", "beneish_m_score", -4)
To find the codes for any of our financial metrics, see our Complete Reference of Metric Codes.
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