Sands Lab Beneish M-Score Historical Data
The historical data trend for Sands Lab'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.
Sands Lab Beneish M-Score Chart
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Sands Lab Annual Data
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Trend |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
Dec24 |
| Beneish M-Score |
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- |
- |
- |
7.63 |
-3.39 |
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Sands Lab Quarterly Data
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Dec20 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
Dec24 |
| Beneish M-Score |
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7.63 |
0.90 |
-1.06 |
-0.56 |
-3.39 |
Competitive Comparison of Sands Lab's Beneish M-Score
For the Software - Infrastructure subindustry, Sands Lab'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.
Sands Lab's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Sands Lab's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Sands Lab's Beneish M-Score falls into.
Sands Lab 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 Sands Lab 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 * 0.1873 | + | 0.528 * 1 | + | 0.404 * 1.1906 | + | 0.892 * 0.7078 | + | 0.115 * 1.6106 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.2033 | + | 4.679 * -0.041482 | - | 0.327 * 0.436 |
| = | -3.39 |
* 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 (Dec24) TTM: | Last Year (Dec23) TTM: |
Total Receivables was ₩769 Mil.
Revenue was 4868.704 + 1792.997 + 660.732 + 974.499 = ₩8,297 Mil.
Gross Profit was 4868.704 + 1792.997 + 660.732 + 974.499 = ₩8,297 Mil.
Total Current Assets was ₩36,724 Mil.
Total Assets was ₩50,354 Mil.
Property, Plant and Equipment(Net PPE) was ₩7,654 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩791 Mil.
Selling, General, & Admin. Expense(SGA) was ₩8,829 Mil.
Total Current Liabilities was ₩2,710 Mil.
Long-Term Debt & Capital Lease Obligation was ₩9 Mil.
Net Income was 161.476 + -686.318 + -359.25 + -1416.424 = ₩-2,301 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -841.558 + 2770.32 + -757.783 + -1382.671 = ₩-212 Mil.
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Total Receivables was ₩5,803 Mil.
Revenue was 8864.303 + 1269.159 + 790.661 + 797.791 = ₩11,722 Mil.
Gross Profit was 8864.303 + 1269.159 + 790.661 + 797.791 = ₩11,722 Mil.
Total Current Assets was ₩38,901 Mil.
Total Assets was ₩46,362 Mil.
Property, Plant and Equipment(Net PPE) was ₩2,839 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩504 Mil.
Selling, General, & Admin. Expense(SGA) was ₩10,367 Mil.
Total Current Liabilities was ₩5,717 Mil.
Long-Term Debt & Capital Lease Obligation was ₩26 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) |
| = | (769.226 / 8296.932) | / | (5803.435 / 11721.914) |
| = | 0.092712 | / | 0.495093 |
| | = | 0.1873 |
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) |
| = | (11721.914 / 11721.914) | / | (8296.932 / 8296.932) |
| = | 1 | / | 1 |
| | = | 1 |
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 - (36724.19 + 7653.786) / 50354.427) | / | (1 - (38901.441 + 2838.978) / 46362.312) |
| = | 0.118688 | / | 0.099691 |
| | = | 1.1906 |
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 |
| = | 8296.932 | / | 11721.914 |
| | = | 0.7078 |
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)) |
| = | (504.455 / (504.455 + 2838.978)) | / | (791.099 / (791.099 + 7653.786)) |
| = | 0.150879 | / | 0.093678 |
| | = | 1.6106 |
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) |
| = | (8829.499 / 8296.932) | / | (10366.729 / 11721.914) |
| = | 1.064188 | / | 0.884389 |
| | = | 1.2033 |
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) |
| = | ((9.175 + 2710.173) / 50354.427) | / | ((25.875 + 5717.205) / 46362.312) |
| = | 0.054004 | / | 0.123874 |
| | = | 0.436 |
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 |
| = | (-2300.516 - 0 | - | -211.692) | / | 50354.427 |
| | = | -0.041482 |
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.
Sands Lab has a M-score of -3.39 suggests that the company is unlikely to be a manipulator.
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