BECU AI Beneish M-Score Historical Data
The historical data trend for BECU AI'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.
BECU AI Beneish M-Score Chart
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BECU AI Annual Data
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Trend |
Dec16 |
Dec17 |
Dec18 |
Dec19 |
Dec20 |
Dec21 |
Dec22 |
Dec23 |
| Beneish M-Score |
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-2.46 |
-5.42 |
- |
- |
-2.89 |
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BECU AI Quarterly Data
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Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
| Beneish M-Score |
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-2.62 |
-2.89 |
-2.35 |
-2.29 |
-2.48 |
Competitive Comparison of BECU AI's Beneish M-Score
For the Software - Application subindustry, BECU AI'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.
BECU AI's Beneish M-Score Distribution in the Software Industry
For the Software industry and Technology sector, BECU AI's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where BECU AI's Beneish M-Score falls into.
BECU AI 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 BECU AI 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 * 1.0269 | + | 0.528 * 1 | + | 0.404 * 1.3203 | + | 0.892 * 1.0886 | + | 0.115 * 1.0207 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0133 | + | 4.679 * -0.046328 | - | 0.327 * 1.0525 |
| = | -2.48 |
* 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 (Sep24) TTM: | Last Year (Sep23) TTM: |
Total Receivables was ₩2,933 Mil.
Revenue was 4531.477 + 4150.424 + 4087.212 + 4895.417 = ₩17,665 Mil.
Gross Profit was 4531.477 + 4150.424 + 4087.212 + 4895.417 = ₩17,665 Mil.
Total Current Assets was ₩14,573 Mil.
Total Assets was ₩16,615 Mil.
Property, Plant and Equipment(Net PPE) was ₩1,118 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩658 Mil.
Selling, General, & Admin. Expense(SGA) was ₩11,564 Mil.
Total Current Liabilities was ₩4,239 Mil.
Long-Term Debt & Capital Lease Obligation was ₩199 Mil.
Net Income was -394.649 + -136.228 + 34.222 + 662.11 = ₩165 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = ₩0 Mil.
Cash Flow from Operations was -272.489 + -607.049 + -322.242 + 2137.002 = ₩935 Mil.
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Total Receivables was ₩2,624 Mil.
Revenue was 4516.21 + 3996.171 + 3484.49 + 4229.52 = ₩16,226 Mil.
Gross Profit was 4516.21 + 3996.171 + 3484.49 + 4229.52 = ₩16,226 Mil.
Total Current Assets was ₩14,281 Mil.
Total Assets was ₩16,066 Mil.
Property, Plant and Equipment(Net PPE) was ₩1,108 Mil.
Depreciation, Depletion and Amortization(DDA) was ₩673 Mil.
Selling, General, & Admin. Expense(SGA) was ₩10,483 Mil.
Total Current Liabilities was ₩3,813 Mil.
Long-Term Debt & Capital Lease Obligation was ₩265 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) |
| = | (2932.94 / 17664.53) | / | (2623.511 / 16226.391) |
| = | 0.166036 | / | 0.161682 |
| | = | 1.0269 |
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) |
| = | (16226.391 / 16226.391) | / | (17664.53 / 17664.53) |
| = | 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 - (14572.587 + 1118.303) / 16615.43) | / | (1 - (14281.004 + 1107.753) / 16065.828) |
| = | 0.055643 | / | 0.042144 |
| | = | 1.3203 |
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 |
| = | 17664.53 | / | 16226.391 |
| | = | 1.0886 |
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)) |
| = | (673.131 / (673.131 + 1107.753)) | / | (657.659 / (657.659 + 1118.303)) |
| = | 0.377976 | / | 0.370311 |
| | = | 1.0207 |
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) |
| = | (11564.192 / 17664.53) | / | (10483.024 / 16226.391) |
| = | 0.654656 | / | 0.646048 |
| | = | 1.0133 |
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) |
| = | ((199.122 + 4239.392) / 16615.43) | / | ((264.952 + 3812.86) / 16065.828) |
| = | 0.267132 | / | 0.253819 |
| | = | 1.0525 |
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 |
| = | (165.455 - 0 | - | 935.222) | / | 16615.43 |
| | = | -0.046328 |
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.
BECU AI has a M-score of -2.48 suggests that the company is unlikely to be a manipulator.
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