Twitter Beneish M-Score Historical Data
The historical data trend for Twitter'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.
Twitter Beneish M-Score Chart
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Twitter Annual Data
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Trend |
Dec12 |
Dec13 |
Dec14 |
Dec15 |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
| Beneish M-Score |
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-3.04 |
-2.29 |
-2.18 |
-3.20 |
-2.31 |
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Twitter Quarterly Data
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Sep17 |
Dec17 |
Mar18 |
Jun18 |
Sep18 |
Dec18 |
Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
| Beneish M-Score |
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-2.33 |
-2.46 |
-2.31 |
-2.45 |
-2.53 |
Competitive Comparison of Twitter's Beneish M-Score
For the Internet Content & Information subindustry, Twitter'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.
Twitter's Beneish M-Score Distribution in the Interactive Media Industry
For the Interactive Media industry and Communication Services sector, Twitter's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Twitter's Beneish M-Score falls into.
Twitter 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 Twitter 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 * 0.8678 | + | 0.528 * 1.0558 | + | 0.404 * 1.217 | + | 0.892 * 1.1745 | + | 0.115 * 0.987 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.1297 | + | 4.679 * -0.028379 | - | 0.327 * 1.1306 |
| = | -2.53 |
* 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 (Jun22) TTM: | Last Year (Jun21) TTM: |
Total Receivables was $973 Mil.
Revenue was 1176.66 + 1200.984 + 1567.22 + 1283.817 = $5,229 Mil.
Gross Profit was 635.984 + 693.534 + 1052.129 + 799.338 = $3,181 Mil.
Total Current Assets was $7,274 Mil.
Total Assets was $13,579 Mil.
Property, Plant and Equipment(Net PPE) was $3,548 Mil.
Depreciation, Depletion and Amortization(DDA) was $613 Mil.
Selling, General, & Admin. Expense(SGA) was $1,939 Mil.
Total Current Liabilities was $1,017 Mil.
Long-Term Debt & Capital Lease Obligation was $6,529 Mil.
Net Income was -270.007 + 513.286 + 181.694 + -536.757 = $-112 Mil.
Non Operating Income was 17.605 + 963.968 + 20.759 + -745.076 = $257 Mil.
Cash Flow from Operations was 29.696 + 126.091 + -528.442 + 388.98 = $16 Mil.
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Total Receivables was $954 Mil.
Revenue was 1190.427 + 1036.018 + 1289.041 + 936.233 = $4,452 Mil.
Gross Profit was 773.495 + 655.01 + 856.117 + 574.845 = $2,859 Mil.
Total Current Assets was $9,728 Mil.
Total Assets was $15,323 Mil.
Property, Plant and Equipment(Net PPE) was $3,038 Mil.
Depreciation, Depletion and Amortization(DDA) was $517 Mil.
Selling, General, & Admin. Expense(SGA) was $1,462 Mil.
Total Current Liabilities was $2,258 Mil.
Long-Term Debt & Capital Lease Obligation was $5,274 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) |
| = | (972.591 / 5228.681) | / | (954.265 / 4451.719) |
| = | 0.186011 | / | 0.214359 |
| | = | 0.8678 |
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) |
| = | (2859.467 / 4451.719) | / | (3180.985 / 5228.681) |
| = | 0.642329 | / | 0.608372 |
| | = | 1.0558 |
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 - (7273.581 + 3547.755) / 13579.287) | / | (1 - (9728.141 + 3037.807) / 15323.225) |
| = | 0.2031 | / | 0.166889 |
| | = | 1.217 |
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 |
| = | 5228.681 | / | 4451.719 |
| | = | 1.1745 |
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)) |
| = | (516.555 / (516.555 + 3037.807)) | / | (612.555 / (612.555 + 3547.755)) |
| = | 0.14533 | / | 0.147238 |
| | = | 0.987 |
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) |
| = | (1939.362 / 5228.681) | / | (1461.617 / 4451.719) |
| = | 0.370908 | / | 0.328326 |
| | = | 1.1297 |
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) |
| = | ((6529.242 + 1017.263) / 13579.287) | / | ((5274.344 + 2257.919) / 15323.225) |
| = | 0.555736 | / | 0.491559 |
| | = | 1.1306 |
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 |
| = | (-111.784 - 257.256 | - | 16.325) | / | 13579.287 |
| | = | -0.028379 |
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.
Twitter has a M-score of -2.53 suggests that the company is unlikely to be a manipulator.
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