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{{More citations needed|date=May 2020}}
'''The Beneish model''' is a ] that uses ]s calculated with accounting data of a specific company in order to check if it is likely (high probability) that the reported ] of the company have been manipulated. '''The Beneish model''' is a ] that uses ]s calculated with accounting data of a specific company in order to check if it is likely (high probability) that the reported ] of the company have been manipulated.


== How to calculate == == How to calculate ==
⚫ The Beneish M-score is calculated using 8 variables (financial ratios):<ref name=":0">{{Cite web|url=https://ru.scribd.com/doc/33484680/The-Detection-of-Earnings-Manipulation-Messod-D-Beneish|title=The Detection of Earnings Manipulation |author=Messod D. Beneish|website=Scribd|access-date=2017-01-08}}</ref><ref>{{Cite web|url=https://ycharts.com/glossary/terms/beneish_m_score|title=Beneish M Score Definition|website=ycharts.com|access-date=2017-01-08}}</ref>
The Beneish M-Score is calculated using 8 financial ratios.

⚫ === Variables (financial ratios)<ref name=":0">{{Cite web|url=https://ru.scribd.com/doc/33484680/The-Detection-of-Earnings-Manipulation-Messod-D-Beneish|title=The Detection of Earnings Manipulation Messod D. Beneish|website=Scribd|access-date=2017-01-08}}</ref><ref>{{Cite web|url=https://ycharts.com/glossary/terms/beneish_m_score|title=Beneish M Score Definition|website=ycharts.com|access-date=2017-01-08}}</ref> ===
* Days Sales in Receivables Index * Days Sales in Receivables Index
(DSRI)
(DSRI) DSRI = (Net Receivables<sub>t</sub> / Sales<sub>t</sub>) / (Net Receivables<sub>t-1</sub> / Sales<sub>t-1</sub>) DSRI = (Net Receivables<sub>t</sub> / Sales<sub>t</sub>) / (Net Receivables<sub>t-1</sub> / Sales<sub>t-1</sub>)
* Gross Margin Index (GMI) * Gross Margin Index (GMI)
GMI = / GMI = /
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TATA = (Income from Continuing Operations<sub>t</sub> - Cash Flows from Operations<sub>t</sub>) / Total Assets<sub>t</sub> TATA = (Income from Continuing Operations<sub>t</sub> - Cash Flows from Operations<sub>t</sub>) / Total Assets<sub>t</sub>


The formula to calculate the M-score is:<ref name=":0" />
=== Formula ===
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 <ref name=":0" /> :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 == == How to interpret ==
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 -2.22 - the company is unlikely to be a manipulator. * 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===
If M-Score is greater than -2.22 - the company is likely to be a manipulator.
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.<ref>{{cite journal|title=Aggregate Financial Misreporting and the Predictability of U.S. Recessions and GDP Growth|journal=The Accounting Review |date=2023 |pages= 129–159|doi=10.2308/TAR-2021-0160|ssrn=3790566 |last1=Beneish |first1=Messod D. |last2=Farber |first2=David B. |last3=Glendening |first3=Matthew |last4=Shaw |first4=Kenneth W. |volume=98 |issue=5 }}</ref><ref>{{Cite news |last=Zumbrun |first=Josh |date=2023-03-24 |title=Accounting-Fraud Indicator Signals Coming Economic Trouble |language=en-US |work=Wall Street Journal |url=https://www.wsj.com/articles/accounting-fraud-indicator-signals-coming-economic-trouble-506568a0 |access-date=2023-06-13 |issn=0099-9660}}</ref>


== Important notices == == Important notices ==
* Beneish M-Score is a probabilistic model, so it cannot detect companies that manipulate their earnings with 100% accuracy. * 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. It means that the M-Score for fraud detection cannot be applied among financial firms (banks, insurance). * 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 == == Example of successful application ==
Enron Corporation was correctly identified as an earnings manipulator by students from Cornell University using M-Score. Noticeably, Wall Street financial analysts were still recommending to buy ] shares at that point of time. 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 ] shares at that point in time..<ref>{{Cite web|date=2007-01-29|title=Business School Students Caught Enron Early|url=https://cornellsun.com/2007/01/29/business-school-students-caught-enron-early/|access-date=2021-10-31|website=The Cornell Daily Sun|language=en-US}}</ref><ref>{{Cite web|title=Cornell Research Report on Enron 1998|url=https://pdfslide.net/documents/cornell-research-report-on-enron-1998.html|access-date=2021-10-31|website=pdfslide.net|language=en}}</ref>


== Further reading on financial statement manipulation == == Further reading on financial statement manipulation ==
A sequence of articles on Alpha Architect blog.<ref>{{Cite web|url=http://blog.alphaarchitect.com/2015/04/20/attention-value-investors-how-to-predict-accounting-trickery/|title=Attention Value Investors: How to Predict Accounting Trickery|date=2015-04-20|website=Alpha Architect|access-date=2017-01-28}}</ref><ref>{{Cite web|url=http://blog.alphaarchitect.com/2011/09/07/the-accrual-anomaly-for-dummies/|title=The Accrual Anomaly For Dummies|date=2011-09-07|website=Alpha Architect|access-date=2017-01-28}}</ref><ref>{{Cite web|url=http://blog.alphaarchitect.com/2012/06/25/managing-the-risks-of-permanent-capital-impairment-part-1-of-4/|title=Managing the Risks of Permanent Capital Impairment (Part 1 of 4)|date=2012-06-25|website=Alpha Architect|access-date=2017-01-28}}</ref> * A sequence of articles on Alpha Architect blog.<ref>{{Cite web|url=https://alphaarchitect.com/2015/04/attention-value-investors-how-to-predict-accounting-trickery/|title=Attention Value Investors: How to Predict Accounting Trickery|date=2015-04-20|website=Alpha Architect|access-date=2017-01-28}}</ref><ref>{{Cite web|url=https://alphaarchitect.com/2011/09/the-accrual-anomaly-for-dummies/|title=The Accrual Anomaly For Dummies|date=2011-09-07|website=Alpha Architect|access-date=2017-01-28}}</ref><ref>{{Cite web|url=https://alphaarchitect.com/2012/06/managing-the-risks-of-permanent-capital-impairment-part-1-of-4/|title=Managing the Risks of Permanent Capital Impairment (Part 1 of 4)|date=2012-06-25|website=Alpha Architect|access-date=2017-01-28}}</ref>
⚫ * An article on Investopedia about different types of financial statement manipulation ("smoke and mirrors", "elder abuse", "fleeing town", and others).<ref>{{Cite news|url=http://www.investopedia.com/articles/07/statementmanipulation.asp|title=Common Clues Of Financial Statement Manipulation|last=Beattie|first=Andrew|date=2006-11-26|newspaper=Investopedia|access-date=2017-01-28}}</ref>

⚫ An article on Investopedia about different types of financial statement manipulation (Smoke and Mirrors, Elder Abuse, Fleeing Town and others).<ref>{{Cite news|url=http://www.investopedia.com/articles/07/statementmanipulation.asp|title=Common Clues Of Financial Statement Manipulation|last=Beattie|first=Andrew|date=2006-11-26|newspaper=Investopedia|language=en-US|access-date=2017-01-28}}</ref>


== See also == == See also ==
* ] * ]
* ] * ]
* ]
* ]
* ]


== References == == References ==
{{Reflist}} {{Reflist}}


] ]
]
]
] ]
]
] ]
] ]

Latest revision as of 21:32, 31 October 2024

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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

The Beneish M-score is calculated using 8 variables (financial ratios):

  • Days Sales in Receivables Index

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

  • Gross Margin Index (GMI)

GMI = /

  • Asset Quality Index (AQI)

AQI = /

  • 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 = /

  • 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:

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

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

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.

Important notices

  • 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

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..

Further reading on financial statement manipulation

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

See also

References

  1. ^ 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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