Wikipedia

Beneish M-score

The Beneish model is a statistical model that uses financial ratios calculated with accounting data of a specific company in order to check if it is likely (high probability) that the reported earnings of the company have been manipulated.

How to calculate

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The Beneish M-score is calculated using 8 variables (financial ratios):[1][2]

  • Days Sales in Receivables Index

(DSRI) DSRI = (Net Receivablest / Salest) / (Net Receivablest-1 / Salest-1)

  • Gross Margin Index (GMI)

GMI = [(Salest-1 - COGSt-1) / Salest-1] / [(Salest - COGSt) / Salest]

  • Asset Quality Index (AQI)

AQI = [1 - (Current Assetst + PP&Et + Securitiest) / Total Assetst] / [1 - ((Current Assetst-1 + PP&Et-1 + Securitiest-1) / Total Assetst-1)]

  • Sales Growth Index (SGI)

SGI = Salest / Salest-1

  • Depreciation Index (DEPI)

DEPI = (Depreciationt-1/ (PP&Et-1 + Depreciationt-1)) / (Depreciationt / (PP&Et + Depreciationt))

  • Sales General and Administrative Expenses Index (SGAI)

SGAI = (SG&A Expenset / Salest) / (SG&A Expenset-1 / Salest-1)

  • Leverage Index (LVGI)

LVGI = [(Current Liabilitiest + Total Long Term Debtt) / Total Assetst] / [(Current Liabilitiest-1 + Total Long Term Debtt-1) / Total Assetst-1]

  • Total Accruals to Total Assets (TATA)

TATA = (Income from Continuing Operationst - Cash Flows from Operationst) / Total Assetst

The formula to calculate the M-score is:[1]

M-score = −4.84 + 0.92 × DSRI + 0.528 × GMI + 0.404 × AQI + 0.892 × SGI + 0.115 × DEPI −0.172 × SGAI + 4.679 × TATA − 0.327 × LVGI

How to interpret

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The threshold value is -1.78 for the model whose coefficients are reported above. (see Beneish 1999, Beneish, Lee, and Nichols 2013, and Beneish and Vorst 2020).

  • If M-score is less than -1.78, the company is unlikely to be a manipulator. For example, an M-score value of -2.50 suggests a low likelihood of manipulation.
  • If M-score is greater than −1.78, the company is likely to be a manipulator. For example, an M-score value of -1.50 suggests a high likelihood of manipulation.

Aggregate recession predictor

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A 2023 research paper will use an aggregate score of many companies to predict recessions. It finds that the score in early 2023 is the highest in some 40 years.[3][4]

Important notices

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  • Beneish M-score is a probabilistic model, so it cannot detect companies that manipulate their earnings with 100% accuracy.
  • Financial institutions were excluded from the sample in Beneish paper when calculating M-score since these institutions make money through different routes. Sales and receivables which are two main ingredients that go into the Beneish formula are not used when analyzing a financial institution.

Example of successful application

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Enron Corporation was correctly identified 1998 as an earnings manipulator by students from Cornell University using M-score Noticeably, Wall Street financial analysts were still recommending to buy Enron shares at that point in time..[5][6]

Further reading on financial statement manipulation

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  • A sequence of articles on Alpha Architect blog.[7][8][9]
  • An article on Investopedia about different types of financial statement manipulation ("smoke and mirrors", "elder abuse", "fleeing town", and others).[10]

See also

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References

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  1. ^ a b Messod D. Beneish. "The Detection of Earnings Manipulation". Scribd. Retrieved 2017-01-08.
  2. ^ "Beneish M Score Definition". ycharts.com. Retrieved 2017-01-08.
  3. ^ Beneish, Messod D.; Farber, David B.; Glendening, Matthew; Shaw, Kenneth W. (2023). "Aggregate Financial Misreporting and the Predictability of U.S. Recessions and GDP Growth". The Accounting Review. 98 (5): 129–159. doi:10.2308/TAR-2021-0160. SSRN 3790566.
  4. ^ Zumbrun, Josh (2023-03-24). "Accounting-Fraud Indicator Signals Coming Economic Trouble". Wall Street Journal. ISSN 0099-9660. Retrieved 2023-06-13.
  5. ^ "Business School Students Caught Enron Early". The Cornell Daily Sun. 2007-01-29. Retrieved 2021-10-31.
  6. ^ "Cornell Research Report on Enron 1998". pdfslide.net. Retrieved 2021-10-31.
  7. ^ "Attention Value Investors: How to Predict Accounting Trickery". Alpha Architect. 2015-04-20. Retrieved 2017-01-28.
  8. ^ "The Accrual Anomaly For Dummies". Alpha Architect. 2011-09-07. Retrieved 2017-01-28.
  9. ^ "Managing the Risks of Permanent Capital Impairment (Part 1 of 4)". Alpha Architect. 2012-06-25. Retrieved 2017-01-28.
  10. ^ Beattie, Andrew (2006-11-26). "Common Clues Of Financial Statement Manipulation". Investopedia. Retrieved 2017-01-28.
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