Innodep Beneish M-Score Historical Data
The historical data trend for Innodep'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.
Innodep Beneish M-Score Chart
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Innodep Annual Data
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Trend |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
Dec24 |
| Beneish M-Score |
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- |
-2.00 |
-1.17 |
-2.65 |
-3.23 |
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Innodep Quarterly Data
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Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
Dec24 |
| Beneish M-Score |
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-2.65 |
-1.95 |
-2.50 |
-2.92 |
-3.23 |
Competitive Comparison of Innodep's Beneish M-Score
For the Information Technology Services subindustry, Innodep'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.
Innodep's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, Innodep's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Innodep's Beneish M-Score falls into.
Innodep 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 Innodep 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 * 0.9083 | + | 0.528 * 0.8715 | + | 0.404 * 1.2579 | + | 0.892 * 1.0326 | + | 0.115 * 0.8977 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.317 | + | 4.679 * -0.109174 | - | 0.327 * 1.4693 |
| = | -3.23 |
* 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 ₩6,393 Mil.
Revenue was 36549.791 + 14061.434 + 25990.343 + 10174.404 = ₩86,776 Mil.
Gross Profit was 14703.393 + 5399.8 + 9351.467 + 3540.519 = ₩32,995 Mil.
Total Current Assets was ₩37,138 Mil.
Total Assets was ₩63,383 Mil.
Property, Plant and Equipment(Net PPE) was ₩13,871 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩2,793 Mil.
Selling, General, & Admin. Expense(SGA) was ₩12,850 Mil.
Total Current Liabilities was ₩21,849 Mil.
Long-Term Debt & Capital Lease Obligation was ₩2,701 Mil.
Net Income was 5917.076 + -2177.285 + 697.672 + -5980.636 = ₩-1,543 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 6210.263 + 1243.444 + 6436.477 + -8513.606 = ₩5,377 Mil.
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Total Receivables was ₩6,816 Mil.
Revenue was 32210.416 + 17361.851 + 22537.403 + 11923.303 = ₩84,033 Mil.
Gross Profit was 11753.33 + 5453.063 + 6611.713 + 4029.27 = ₩27,847 Mil.
Total Current Assets was ₩33,330 Mil.
Total Assets was ₩54,128 Mil.
Property, Plant and Equipment(Net PPE) was ₩12,398 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩2,196 Mil.
Selling, General, & Admin. Expense(SGA) was ₩9,448 Mil.
Total Current Liabilities was ₩12,688 Mil.
Long-Term Debt & Capital Lease Obligation was ₩1,581 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) |
| = | (6392.658 / 86775.972) | / | (6815.696 / 84032.973) |
| = | 0.073669 | / | 0.081107 |
| | = | 0.9083 |
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) |
| = | (27847.376 / 84032.973) | / | (32995.179 / 86775.972) |
| = | 0.331386 | / | 0.380234 |
| | = | 0.8715 |
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 - (37138.476 + 13870.677) / 63382.546) | / | (1 - (33329.916 + 12397.613) / 54127.698) |
| = | 0.195218 | / | 0.155192 |
| | = | 1.2579 |
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 |
| = | 86775.972 | / | 84032.973 |
| | = | 1.0326 |
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)) |
| = | (2195.529 / (2195.529 + 12397.613)) | / | (2792.57 / (2792.57 + 13870.677)) |
| = | 0.150449 | / | 0.167589 |
| | = | 0.8977 |
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) |
| = | (12849.553 / 86775.972) | / | (9448.075 / 84032.973) |
| = | 0.148077 | / | 0.112433 |
| | = | 1.317 |
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) |
| = | ((2700.834 + 21849.29) / 63382.546) | / | ((1581.186 + 12688.225) / 54127.698) |
| = | 0.387333 | / | 0.263625 |
| | = | 1.4693 |
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 |
| = | (-1543.173 - 0 | - | 5376.578) | / | 63382.546 |
| | = | -0.109174 |
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.
Innodep has a M-score of -3.23 suggests that the company is unlikely to be a manipulator.
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