Fortinet Beneish M-Score Historical Data
The historical data trend for Fortinet's Beneish M-Score can be seen below:
* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.
Fortinet Beneish M-Score Chart
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Fortinet Annual Data
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Trend |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.80 |
-2.96 |
-2.96 |
-2.75 |
-2.87 |
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Fortinet Quarterly Data
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Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
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-2.75 |
-2.96 |
-3.06 |
-3.13 |
-2.87 |
Competitive Comparison of Fortinet's Beneish M-Score
For the Software - Infrastructure subindustry, Fortinet's Beneish M-Score, along with its competitors' market caps and Beneish M-Score data, can be viewed below:
* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.
Fortinet's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Fortinet's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Fortinet's Beneish M-Score falls into.
Fortinet Beneish M-Score Calculation
The M-score was created by Professor Messod Beneish. Instead of measuring the bankruptcy risk (Altman Z-Score) or business trend (Piotroski F-Score), M-score can be used to detect the risk of earnings manipulation. This is the original research paper on M-score.
The M-Score Variables:
The M-score of Fortinet for today is based on a combination of the following eight different indices:
| M | = | -4.84 | + | 0.92 * DSRI | + | 0.528 * GMI | + | 0.404 * AQI | + | 0.892 * SGI | + | 0.115 * DEPI |
| = | -4.84 | + | 0.92 * 0.9253 | + | 0.528 * 0.9839 | + | 0.404 * 1.0088 | + | 0.892 * 1.2009 | + | 0.115 * 1.0619 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9953 | + | 4.679 * -0.108157 | - | 0.327 * 0.9935 |
| = | -2.87 |
* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.
| This Year (Dec23) TTM: | Last Year (Dec22) TTM: |
Total Receivables was $1,402 Mil.
Revenue was 1415.1 + 1334.6 + 1292.8 + 1262.3 = $5,305 Mil.
Gross Profit was 1099.2 + 1016.9 + 997 + 954.5 = $4,068 Mil.
Total Current Assets was $4,428 Mil.
Total Assets was $7,259 Mil.
Property, Plant and Equipment(Net PPE) was $1,044 Mil.
Depreciation, Depletion and Amortization(DDA) was $113 Mil.
Selling, General, & Admin. Expense(SGA) was $2,217 Mil.
Total Current Liabilities was $3,719 Mil.
Long-Term Debt & Capital Lease Obligation was $992 Mil.
Net Income was 310.9 + 322.9 + 266.3 + 247.7 = $1,148 Mil.
Non Operating Income was 6.3 + -5.9 + -6.2 + 3.2 = $-3 Mil.
Cash Flow from Operations was 191.7 + 551.2 + 515.1 + 677.5 = $1,936 Mil.
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Total Receivables was $1,262 Mil.
Revenue was 1283 + 1149.5 + 1030.1 + 954.8 = $4,417 Mil.
Gross Profit was 985.7 + 866.5 + 779.3 + 701 = $3,333 Mil.
Total Current Assets was $3,810 Mil.
Total Assets was $6,228 Mil.
Property, Plant and Equipment(Net PPE) was $899 Mil.
Depreciation, Depletion and Amortization(DDA) was $104 Mil.
Selling, General, & Admin. Expense(SGA) was $1,855 Mil.
Total Current Liabilities was $3,078 Mil.
Long-Term Debt & Capital Lease Obligation was $990 Mil.
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1. DSRI = Days Sales in Receivables Index
Measured as the ratio of Revenue in Total Receivables in year t to year t-1.
A large increase in DSR could be indicative of revenue inflation.
| DSRI | = | (Receivables_t / Revenue_t) | / | (Receivables_t-1 / Revenue_t-1) |
| = | (1402 / 5304.8) | / | (1261.7 / 4417.4) |
| = | 0.264289 | / | 0.285621 |
| | = | 0.9253 |
2. GMI = Gross Margin Index
Measured as the ratio of gross margin in year t-1 to gross margin in year t.
Gross margin has deteriorated when this index is above 1. A firm with poorer prospects is more likely to manipulate earnings.
| GMI | = | GrossMargin_t-1 | / | GrossMargin_t |
| = | (GrossProfit_t-1 / Revenue_t-1) | / | (GrossProfit_t / Revenue_t) |
| = | (3332.5 / 4417.4) | / | (4067.6 / 5304.8) |
| = | 0.754403 | / | 0.766777 |
| | = | 0.9839 |
3. AQI = Asset Quality Index
AQI is the ratio of asset quality in year t to year t-1.
Asset quality is measured as the ratio of non-current assets other than Property, Plant and Equipment to Total Assets.
| AQI | = | (1 - (CurrentAssets_t + PPE_t) / TotalAssets_t) | / | (1 - (CurrentAssets_t-1 + PPE_t-1) / TotalAssets_t-1) |
| = | (1 - (4428.3 + 1044.4) / 7258.9) | / | (1 - (3810.4 + 898.5) / 6228) |
| = | 0.24607 | / | 0.243915 |
| | = | 1.0088 |
4. SGI = Sales Growth Index
Ratio of Revenue in year t to sales in year t-1.
Sales growth is not itself a measure of manipulation. However, growth companies are likely to find themselves under pressure to manipulate in order to keep up appearances.
| SGI | = | Sales_t | / | Sales_t-1 |
| = | Revenue_t | / | Revenue_t-1 |
| = | 5304.8 | / | 4417.4 |
| | = | 1.2009 |
5. DEPI = Depreciation Index
Measured as the ratio of the rate of Depreciation, Depletion and Amortization in year t-1 to the corresponding rate in year t.
DEPI greater than 1 indicates that assets are being depreciated at a slower rate. This suggests that the firm might be revising useful asset life assumptions upwards, or adopting a new method that is income friendly.
| DEPI | = | (Depreciation_t-1 / (Depreciaton_t-1 + PPE_t-1)) | / | (Depreciation_t / (Depreciaton_t + PPE_t)) |
| = | (104.3 / (104.3 + 898.5)) | / | (113.4 / (113.4 + 1044.4)) |
| = | 0.104009 | / | 0.097944 |
| | = | 1.0619 |
Note: If the Depreciation, Depletion and Amortization data is not available, we assume that the depreciation rate is constant and set the Depreciation Index to 1.
6. SGAI = Sales, General and Administrative expenses Index
The ratio of Selling, General, & Admin. Expense(SGA) to Sales in year t relative to year t-1.
SGA expenses index > 1 means that the company is becoming less efficient in generate sales.
| SGAI | = | (SGA_t / Sales_t) | / | (SGA_t-1 /Sales_t-1) |
| = | (2217.3 / 5304.8) | / | (1855.1 / 4417.4) |
| = | 0.41798 | / | 0.419953 |
| | = | 0.9953 |
7. LVGI = Leverage Index
The ratio of total debt to Total Assets in year t relative to yeat t-1.
An LVGI > 1 indicates an increase in leverage
| LVGI | = | ((LTD_t + CurrentLiabilities_t) / TotalAssets_t) | / | ((LTD_t-1 + CurrentLiabilities_t-1) / TotalAssets_t-1) |
| = | ((992.3 + 3719) / 7258.9) | / | ((990.4 + 3078.4) / 6228) |
| = | 0.649038 | / | 0.653308 |
| | = | 0.9935 |
8. TATA = Total Accruals to Total Assets
Total accruals calculated as the change in working capital accounts other than cash less depreciation.
| TATA | = | (IncomefromContinuingOperations_t | - | CashFlowsfromOperations_t) | / | TotalAssets_t |
| = | (NetIncome_t - NonOperatingIncome_t | - | CashFlowsfromOperations_t) | / | TotalAssets_t |
| = | (1147.8 - -2.6 | - | 1935.5) | / | 7258.9 |
| | = | -0.108157 |
An M-Score of equal or less than -1.78 suggests that the company is unlikely to be a manipulator. An M-Score of greater than -1.78 signals that the company is likely to be a manipulator.
Fortinet has a M-score of -2.87 suggests that the company is unlikely to be a manipulator.
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