Powernet Technologies Beneish M-Score Historical Data
The historical data trend for Powernet Technologies'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.
Powernet Technologies Beneish M-Score Chart
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Powernet Technologies Annual Data
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Dec15 |
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Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-0.93 |
-2.48 |
-2.34 |
-2.29 |
-3.37 |
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Powernet Technologies Quarterly Data
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Jun19 |
Sep19 |
Dec19 |
Mar20 |
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Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
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Dec22 |
Mar23 |
Jun23 |
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Dec23 |
Mar24 |
| Beneish M-Score |
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-3.19 |
-3.09 |
-2.77 |
-3.37 |
-2.54 |
Competitive Comparison of Powernet Technologies's Beneish M-Score
For the Electronic Components subindustry, Powernet Technologies'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.
Powernet Technologies's Beneish M-Score Distribution in the Hardware Industry
For the Hardware industry and Technology sector, Powernet Technologies's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Powernet Technologies's Beneish M-Score falls into.
Powernet Technologies 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 Powernet Technologies 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 * 1.2558 | + | 0.528 * 0.8319 | + | 0.404 * 1.0856 | + | 0.892 * 0.9763 | + | 0.115 * 1.0905 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 0.8007 | + | 4.679 * -0.050474 | - | 0.327 * 1.0894 |
| = | -2.54 |
* 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 (Mar24) TTM: | Last Year (Mar23) TTM: |
Total Receivables was ₩27,441 Mil.
Revenue was 53573.792 + 39050.016 + 50071.812 + 51904.83 = ₩194,600 Mil.
Gross Profit was 7291.603 + 6184.005 + 7777.52 + 6893.086 = ₩28,146 Mil.
Total Current Assets was ₩97,414 Mil.
Total Assets was ₩168,049 Mil.
Property, Plant and Equipment(Net PPE) was ₩18,664 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩3,806 Mil.
Selling, General, & Admin. Expense(SGA) was ₩4,615 Mil.
Total Current Liabilities was ₩64,264 Mil.
Long-Term Debt & Capital Lease Obligation was ₩125 Mil.
Net Income was 6569.403 + -1787.399 + 780.515 + 3225.655 = ₩8,788 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was 3215.316 + 10735.487 + 3168.776 + 150.653 = ₩17,270 Mil.
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Total Receivables was ₩22,382 Mil.
Revenue was 45132.117 + 41481.454 + 55116.01 + 57592.756 = ₩199,322 Mil.
Gross Profit was 5149.117 + 6801.96 + 7192.327 + 4839.949 = ₩23,983 Mil.
Total Current Assets was ₩86,003 Mil.
Total Assets was ₩146,360 Mil.
Property, Plant and Equipment(Net PPE) was ₩18,663 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩4,228 Mil.
Selling, General, & Admin. Expense(SGA) was ₩5,904 Mil.
Total Current Liabilities was ₩44,009 Mil.
Long-Term Debt & Capital Lease Obligation was ₩7,466 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) |
| = | (27441.267 / 194600.45) | / | (22382.177 / 199322.337) |
| = | 0.141013 | / | 0.112291 |
| | = | 1.2558 |
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) |
| = | (23983.353 / 199322.337) | / | (28146.214 / 194600.45) |
| = | 0.120324 | / | 0.144636 |
| | = | 0.8319 |
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 - (97413.904 + 18664.274) / 168049.47) | / | (1 - (86002.881 + 18663.335) / 146360.335) |
| = | 0.309262 | / | 0.284873 |
| | = | 1.0856 |
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 |
| = | 194600.45 | / | 199322.337 |
| | = | 0.9763 |
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)) |
| = | (4227.901 / (4227.901 + 18663.335)) | / | (3805.743 / (3805.743 + 18664.274)) |
| = | 0.184695 | / | 0.16937 |
| | = | 1.0905 |
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) |
| = | (4615.441 / 194600.45) | / | (5904.17 / 199322.337) |
| = | 0.023718 | / | 0.029621 |
| | = | 0.8007 |
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) |
| = | ((124.767 + 64264.313) / 168049.47) | / | ((7465.903 + 44008.945) / 146360.335) |
| = | 0.383156 | / | 0.351699 |
| | = | 1.0894 |
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 |
| = | (8788.174 - 0 | - | 17270.232) | / | 168049.47 |
| | = | -0.050474 |
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.
Powernet Technologies has a M-score of -2.54 suggests that the company is unlikely to be a manipulator.
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