What is the Beneish M-Score?
The Beneish M-Score is a mathematical model designed to detect the possibility of a company manipulating its reported earnings. It uses a combination of eight different financial ratios to identify whether a company has manipulated its earnings. The model is the creation of Professor Messod Beneish, an accounting professor at Indiana University’s Kelley School of Business.
The M-Score serves as a red flag or signal that a company’s financial statements may contain misrepresentations and need to be carefully scrutinized. It has gained prominence as a forensic accounting tool used by auditors, financial analysts, and investors to assess the integrity of a company’s reported financials.
Definition of the Beneish M-Score
In technical terms, the Beneish M-Score is a probabilistic model that uses financial ratios calculated from a company’s financial statements to identify the likelihood of earnings manipulation. It is a linear combination of eight variables (financial ratios) with coefficients derived from the model’s original dataset.
The model produces an M-Score, which is a number indicating the degree to which the earnings may have been manipulated. An M-Score of less than -2.22 suggests that the company is not a manipulator, while an M-Score of greater than -2.22 signals that the company is likely to be a manipulator.
The Creator of the Beneish M-Score
The Beneish M-Score was developed by Dr. Messod Beneish, an accounting professor at Indiana University’s Kelley School of Business. Dr. Beneish first published his research on earnings manipulation detection in a 1999 paper titled “The Detection of Earnings Manipulation.”
In his research, Dr. Beneish used financial statement data from a sample of 74 companies identified as earnings manipulators by the SEC. He matched these firms with 2,332 non-manipulator firms and used probit regression to identify significant financial ratios that could discriminate manipulators from non-manipulators.
How the Beneish M-Score is Calculated
The Beneish M-Score is calculated using eight financial ratios derived from a company’s financial statements. These ratios are combined in a weighted formula to arrive at the final M-Score.
To calculate the M-Score, one needs access to the company’s balance sheet, income statement, and cash flow statement for the past two years. The data required includes metrics such as Total Revenues, Cost of Goods Sold, Net Income, Total Assets, Receivables, Property, Plant & Equipment, Depreciation, Selling, General & Administrative Expenses, and Operating Cash Flow.
The 8 Financial Ratios Used in the Beneish M-Score
The eight ratios used in the Beneish M-Score calculation are:
- DSRI (Days Sales in Receivables Index): Measures whether receivables and revenues are in or out of balance in two consecutive years.
- GMI (Gross Margin Index): Measures the deterioration in the gross margin.
- AQI (Asset Quality Index): Measures the proportion of assets where potential benefit is less certain.
- SGI (Sales Growth Index): Measures the increase in sales between years.
- DEPI (Depreciation Index): Measures the rate of depreciation between years.
- SGAI (Sales, General and Administrative Expenses Index): Measures change in SGA expenses relative to sales.
- LVGI (Leverage Index): Measures change in leverage between years.
- TATA (Total Accruals to Total Assets): Measures the extent to which managers make discretionary accounting choices to alter earnings.
The Beneish M-Score Formula
The Beneish M-Score formula is as follows:
M = -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
Where:
- M is the Beneish M-Score
- DSRI is the Days Sales in Receivables Index
- GMI is the Gross Margin Index
- AQI is the Asset Quality Index
- SGI is the Sales Growth Index
- DEPI is the Depreciation Index
- SGAI is the Sales, General and Administrative Expenses Index
- LVGI is the Leverage Index
- TATA is Total Accruals to Total Assets
Here’s a simplified example of how to calculate the M-Score:
| Ratio | Value | Coefficient | Product |
|---|---|---|---|
| DSRI | 1.031 | 0.920 | 0.948 |
| GMI | 1.193 | 0.528 | 0.630 |
| AQI | 1.039 | 0.404 | 0.420 |
| SGI | 1.134 | 0.892 | 1.012 |
| DEPI | 0.759 | 0.115 | 0.087 |
| SGAI | 1.041 | -0.172 | -0.179 |
| LVGI | 0.966 | -0.327 | -0.316 |
| TATA | 0.031 | 4.679 | 0.145 |
| Sum of Products | 2.747 | ||
| Constant | -4.840 | ||
| M-Score | -2.093 | ||
In this example, the calculated M-Score is -2.093, which falls in the “gray” zone between -1.78 and -2.22, indicating a possibility of earnings manipulation but not a strong likelihood.
Interpreting the Beneish M-Score Results
The interpretation of the Beneish M-Score is straightforward. The model provides a clear threshold for determining the likelihood that a firm is manipulating its reported earnings.
The M-Score is designed to capture distortions that could result from earnings manipulation. A higher M-Score indicates a greater probability of manipulation. However, it’s important to note that the M-Score is not a 100% guarantee of manipulation; rather, it’s a probabilistic assessment.
Beneish M-Score Thresholds for Earnings Manipulation
The generally accepted Beneish M-Score interpretation is:
- An M-Score greater than -1.78 suggests a strong likelihood of a firm being a manipulator.
- An M-Score between -1.78 and -2.22 indicates a possible manipulator.
- An M-Score less than -2.22 suggests the company is unlikely to be a manipulator.
These thresholds were established based on Beneish’s original sample, which correctly identified 76% of manipulators while incorrectly identifying 17.5% of non-manipulators.
Accuracy of the Beneish M-Score Model
In his original study, Dr. Beneish claimed a 76% accuracy rate in identifying manipulators and a 17.5% error rate in mistakenly identifying non-manipulators as manipulators. Subsequent studies have generally confirmed the model’s effectiveness, although the accuracy rates vary depending on the dataset and time period.
It’s crucial to understand that the Beneish M-Score is a probabilistic model and not a definitive indicator of earnings manipulation. A high M-Score does not guarantee that a company is manipulating its earnings, just as a low M-Score does not completely rule out the possibility of manipulation.
Real-World Examples of the Beneish M-Score in Use
The Beneish M-Score has been applied to numerous companies and used in various contexts, from academic research to practical investing. Here are a few notable examples:
Enron Scandal and the Beneish M-Score
One of the most famous examples of the Beneish M-Score’s effectiveness is the Enron scandal. In 2001, Cornell University students used the M-Score to identify Enron as an earnings manipulator months before Wall Street analysts raised concerns. The students found that Enron had an M-Score of -1.89, indicating a high probability of earnings manipulation.
The Enron case served as a strong validation of the Beneish M-Score model. It demonstrated that the model could identify earnings manipulation even in a large, well-established company that was widely respected by analysts and investors.
Amazon (AMZN) M-Score Analysis
To illustrate a more recent example, let’s consider Amazon (AMZN). Based on Amazon’s 2020 financial statements, the calculated M-Score is approximately -1.57. This score is greater than the -1.78 threshold, suggesting a strong likelihood that Amazon may be manipulating its earnings.
However, it’s essential to interpret this result with caution. A high M-Score does not definitively prove manipulation, and further investigation would be needed to confirm any actual earnings manipulation. Moreover, the unique characteristics of Amazon’s business model may affect the reliability of the M-Score.
GMT Research’s Study on Asian Companies
GMT Research, an accounting research firm based in Hong Kong and regulated by Hong Kong’s Securities and Futures Commission, conducted a study of 3,600 Asian companies with a market capitalization over US$1bn. The study found that 33% of these companies had an M-Score greater than -2.22 between 2010 and 2015, indicating a high prevalence of potential earnings manipulation.
Further, the study found that 89% of the companies likely to be manipulating profits were domiciled in Hong Kong and China. The incidence rate in these regions was 20-21%, compared to just 5% elsewhere.
This study highlights the potential for the Beneish M-Score to be used as a screening tool to identify regions or markets with a higher risk of earnings manipulation.
The Importance of the Beneish M-Score
The Beneish M-Score has significant implications for investors, analysts, and regulators. It serves as an early warning system, alerting stakeholders to potential earnings manipulation before it’s uncovered through traditional means.
The Beneish M-Score as an Investment Tool
Investors can use the Beneish M-Score as a screening tool to avoid companies that may be manipulating their earnings. By excluding high M-Score companies, investors can potentially sidestep significant losses from accounting scandals or earnings restatements.
Some studies have shown that using the M-Score as a stock selection technique can generate significant returns. A study by Quant Investing found that a hedged strategy based on the M-Score generated an annual return of nearly 14% over a 21-year period.
Advantages of Using the Beneish M-Score Model
One of the key advantages of the Beneish M-Score is its simplicity. It provides a clear, binary output: a company is either likely to be a manipulator or not based on its M-Score. This makes it easy to incorporate into investment strategies and financial analysis models.
Another advantage is that the M-Score is based on financial data that is publicly available in a company’s financial statements. This means it can be calculated and used by anyone with access to these statements, not just insiders or those with privileged information.
The M-Score is also useful as a filtering mechanism. By eliminating high M-Score companies, investors can focus their analysis on a cleaner universe of companies that are less likely to have manipulated earnings.
In conclusion, the Beneish M-Score is a powerful tool for detecting earnings manipulation. While not infallible, it has proven to be effective in identifying many high-profile cases of accounting fraud. As such, it is an important model for investors, analysts, and regulators to understand and utilize in their work.
See also:
- Piotroski F-Score: Definition, Calculation, and Examples
- Price-to-Cash Flow (P/CF) Ratio Definition, Formula, and Example
- Earnings Per Share (EPS) Calculation: Formula, Examples, and Importance
- Days Sales Outstanding (DSO) Calculation: How To Calculate It
- Return on Equity (ROE) Calculation: Definition, Formula, and Examples
Leave a Comment