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 |
| Beneish M-Score |
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- |
- |
- |
7.63 |
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Sands Lab Quarterly Data
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|
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
| Beneish M-Score |
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- |
-1.84 |
7.63 |
0.90 |
-1.06 |
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 * 1.0133 | + | 0.528 * 1 | + | 0.404 * 3.3492 | + | 0.892 * 1.2467 | + | 0.115 * 1.2651 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.3984 | + | 4.679 * 0.030141 | - | 0.327 * 0.5731 |
| = | -1.06 |
* 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 (Jun24) TTM: | Last Year (Jun23) TTM: |
Total Receivables was ₩1,270 Mil.
Revenue was 660.732 + 974.499 + 8864.303 + 1269.159 = ₩11,769 Mil.
Gross Profit was 660.732 + 974.499 + 8864.303 + 1269.159 = ₩11,769 Mil.
Total Current Assets was ₩40,693 Mil.
Total Assets was ₩49,545 Mil.
Property, Plant and Equipment(Net PPE) was ₩3,119 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩635 Mil.
Selling, General, & Admin. Expense(SGA) was ₩10,948 Mil.
Total Current Liabilities was ₩1,371 Mil.
Long-Term Debt & Capital Lease Obligation was ₩14 Mil.
Net Income was -359.25 + -1416.424 + 2219.745 + -914.693 = ₩-471 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -757.783 + -1382.671 + -405.901 + 582.383 = ₩-1,964 Mil.
|
Total Receivables was ₩1,005 Mil.
Revenue was 790.661 + 797.791 + 6139.126 + 1712.539 = ₩9,440 Mil.
Gross Profit was 790.661 + 797.791 + 6139.126 + 1712.539 = ₩9,440 Mil.
Total Current Assets was ₩38,203 Mil.
Total Assets was ₩41,255 Mil.
Property, Plant and Equipment(Net PPE) was ₩1,627 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩443 Mil.
Selling, General, & Admin. Expense(SGA) was ₩6,280 Mil.
Total Current Liabilities was ₩1,999 Mil.
Long-Term Debt & Capital Lease Obligation was ₩13 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) |
| = | (1269.99 / 11768.693) | / | (1005.356 / 9440.117) |
| = | 0.107913 | / | 0.106498 |
| | = | 1.0133 |
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) |
| = | (9440.117 / 9440.117) | / | (11768.693 / 11768.693) |
| = | 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 - (40692.815 + 3118.618) / 49544.954) | / | (1 - (38202.773 + 1626.979) / 41255.257) |
| = | 0.115724 | / | 0.034553 |
| | = | 3.3492 |
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 |
| = | 11768.693 | / | 9440.117 |
| | = | 1.2467 |
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)) |
| = | (443.219 / (443.219 + 1626.979)) | / | (635.254 / (635.254 + 3118.618)) |
| = | 0.214095 | / | 0.169226 |
| | = | 1.2651 |
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) |
| = | (10947.817 / 11768.693) | / | (6280.008 / 9440.117) |
| = | 0.930249 | / | 0.665247 |
| | = | 1.3984 |
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) |
| = | ((14.329 + 1370.703) / 49544.954) | / | ((13.384 + 1998.912) / 41255.257) |
| = | 0.027955 | / | 0.048777 |
| | = | 0.5731 |
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 |
| = | (-470.622 - 0 | - | -1963.972) | / | 49544.954 |
| | = | 0.030141 |
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 -1.06 signals that the company is likely to be a manipulator.
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