Hawkins Beneish M-Score
Hawkins Beneish M-Score Chart
Hawkins Historical Beneish M-Score DataPro Data Export
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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.
View Beneish M-Score for HWKN.
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HWKN Beneish M-Score Benchmarks
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