Snowflake Beneish M-Score Historical Data
The historical data trend for Snowflake'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.
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Snowflake Annual Data
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Trend |
Jan19 |
Jan20 |
Jan21 |
Jan22 |
Jan23 |
Jan24 |
| Beneish M-Score |
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- |
- |
-2.33 |
-2.91 |
-3.28 |
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Snowflake Quarterly Data
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Jan19 |
Jul19 |
Oct19 |
Jan20 |
Apr20 |
Jul20 |
Oct20 |
Jan21 |
Apr21 |
Jul21 |
Oct21 |
Jan22 |
Apr22 |
Jul22 |
Oct22 |
Jan23 |
Apr23 |
Jul23 |
Oct23 |
Jan24 |
| Beneish M-Score |
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-2.91 |
-3.21 |
-3.16 |
-3.32 |
-3.28 |
Competitive Comparison
For the Software - Application subindustry, Snowflake'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.
Snowflake Beneish M-Score Distribution
For the Software industry and Technology sector, Snowflake's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Snowflake's Beneish M-Score falls into.
Snowflake 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 Snowflake 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 * 0.9531 | + | 0.528 * 0.96 | + | 0.404 * 1.0748 | + | 0.892 * 1.3586 | + | 0.115 * 0.7205 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.9 | + | 4.679 * -0.210267 | - | 0.327 * 1.264 |
| = | -3.28 |
* 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 (Jan24) TTM: | Last Year (Jan23) TTM: |
Total Receivables was $927 Mil.
Revenue was 774.699 + 734.173 + 674.018 + 623.599 = $2,806 Mil.
Gross Profit was 532.895 + 505.225 + 455.626 + 414.185 = $1,908 Mil.
Total Current Assets was $5,039 Mil.
Total Assets was $8,223 Mil.
Property, Plant and Equipment(Net PPE) was $500 Mil.
Depreciation, Depletion and Amortization(DDA) was $120 Mil.
Selling, General, & Admin. Expense(SGA) was $1,715 Mil.
Total Current Liabilities was $2,731 Mil.
Long-Term Debt & Capital Lease Obligation was $254 Mil.
Net Income was -169.352 + -214.251 + -226.867 + -225.627 = $-836 Mil.
Non Operating Income was 47.533 + -4.17 + 4.086 + -2.562 = $45 Mil.
Cash Flow from Operations was 344.58 + 120.907 + 83.191 + 299.444 = $848 Mil.
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Total Receivables was $716 Mil.
Revenue was 589.012 + 557.028 + 497.248 + 422.371 = $2,066 Mil.
Gross Profit was 383.355 + 366.307 + 324.016 + 274.441 = $1,348 Mil.
Total Current Assets was $4,985 Mil.
Total Assets was $7,722 Mil.
Property, Plant and Equipment(Net PPE) was $392 Mil.
Depreciation, Depletion and Amortization(DDA) was $64 Mil.
Selling, General, & Admin. Expense(SGA) was $1,402 Mil.
Total Current Liabilities was $1,994 Mil.
Long-Term Debt & Capital Lease Obligation was $224 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) |
| = | (926.902 / 2806.489) | / | (715.821 / 2065.659) |
| = | 0.330271 | / | 0.346534 |
| | = | 0.9531 |
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) |
| = | (1348.119 / 2065.659) | / | (1907.931 / 2806.489) |
| = | 0.652634 | / | 0.679828 |
| | = | 0.96 |
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 - (5039.264 + 499.592) / 8223.383) | / | (1 - (4984.69 + 392.089) / 7722.322) |
| = | 0.32645 | / | 0.303735 |
| | = | 1.0748 |
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 |
| = | 2806.489 | / | 2065.659 |
| | = | 1.3586 |
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)) |
| = | (63.535 / (63.535 + 392.089)) | / | (119.903 / (119.903 + 499.592)) |
| = | 0.139446 | / | 0.19355 |
| | = | 0.7205 |
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) |
| = | (1714.755 / 2806.489) | / | (1402.328 / 2065.659) |
| = | 0.610997 | / | 0.678877 |
| | = | 0.9 |
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) |
| = | ((254.037 + 2731.23) / 8223.383) | / | ((224.357 + 1993.517) / 7722.322) |
| = | 0.363022 | / | 0.287203 |
| | = | 1.264 |
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 |
| = | (-836.097 - 44.887 | - | 848.122) | / | 8223.383 |
| | = | -0.210267 |
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.
Snowflake has a M-score of -3.28 suggests that the company is unlikely to be a manipulator.
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