Fastly Beneish M-Score Historical Data
The historical data trend for Fastly'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.
Fastly Beneish M-Score Chart
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Fastly Annual Data
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Trend |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-1.80 |
4.63 |
-3.70 |
-2.57 |
-2.68 |
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Fastly Quarterly Data
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Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
| Beneish M-Score |
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-2.72 |
-2.85 |
-2.72 |
-2.68 |
-2.79 |
Competitive Comparison of Fastly's Beneish M-Score
For the Software - Application subindustry, Fastly'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.
Fastly's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Fastly's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Fastly's Beneish M-Score falls into.
Fastly 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 Fastly 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 * 1.0811 | + | 0.528 * 0.9244 | + | 0.404 * 1.0614 | + | 0.892 * 1.1653 | + | 0.115 * 0.8632 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9133 | + | 4.679 * -0.131887 | - | 0.327 * 0.7045 |
| = | -2.79 |
* 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 (Mar24) TTM: | Last Year (Mar23) TTM: |
Total Receivables was $107.5 Mil.
Revenue was 133.52 + 137.777 + 127.816 + 122.831 = $521.9 Mil.
Gross Profit was 73.234 + 75.774 + 66.086 + 64.214 = $279.3 Mil.
Total Current Assets was $460.2 Mil.
Total Assets was $1,505.9 Mil.
Property, Plant and Equipment(Net PPE) was $252.5 Mil.
Depreciation, Depletion and Amortization(DDA) was $73.0 Mil.
Selling, General, & Admin. Expense(SGA) was $319.0 Mil.
Total Current Liabilities was $120.2 Mil.
Long-Term Debt & Capital Lease Obligation was $391.1 Mil.
Net Income was -43.427 + -23.386 + -54.311 + -10.704 = $-131.8 Mil.
Non Operating Income was -0.089 + 14.893 + -4.332 + 35.957 = $46.4 Mil.
Cash Flow from Operations was 11.132 + -7.377 + -8.39 + 24.99 = $20.4 Mil.
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Total Receivables was $85.3 Mil.
Revenue was 117.564 + 119.321 + 108.504 + 102.518 = $447.9 Mil.
Gross Profit was 60.254 + 62.583 + 52.679 + 46.052 = $221.6 Mil.
Total Current Assets was $661.6 Mil.
Total Assets was $1,862.4 Mil.
Property, Plant and Equipment(Net PPE) was $276.5 Mil.
Depreciation, Depletion and Amortization(DDA) was $66.4 Mil.
Selling, General, & Admin. Expense(SGA) was $299.7 Mil.
Total Current Liabilities was $125.2 Mil.
Long-Term Debt & Capital Lease Obligation was $772.5 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) |
| = | (107.517 / 521.944) | / | (85.344 / 447.907) |
| = | 0.205993 | / | 0.19054 |
| | = | 1.0811 |
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) |
| = | (221.568 / 447.907) | / | (279.308 / 521.944) |
| = | 0.494674 | / | 0.53513 |
| | = | 0.9244 |
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 - (460.21 + 252.479) / 1505.923) | / | (1 - (661.64 + 276.505) / 1862.41) |
| = | 0.526743 | / | 0.496274 |
| | = | 1.0614 |
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 |
| = | 521.944 | / | 447.907 |
| | = | 1.1653 |
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)) |
| = | (66.371 / (66.371 + 276.505)) | / | (72.987 / (72.987 + 252.479)) |
| = | 0.193571 | / | 0.224254 |
| | = | 0.8632 |
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) |
| = | (318.998 / 521.944) | / | (299.736 / 447.907) |
| = | 0.611173 | / | 0.669192 |
| | = | 0.9133 |
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) |
| = | ((391.134 + 120.227) / 1505.923) | / | ((772.511 + 125.2) / 1862.41) |
| = | 0.339566 | / | 0.482016 |
| | = | 0.7045 |
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 |
| = | (-131.828 - 46.429 | - | 20.355) | / | 1505.923 |
| | = | -0.131887 |
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.
Fastly has a M-score of -2.79 suggests that the company is unlikely to be a manipulator.
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