Seneca Foods Beneish M-Score Historical Data
The historical data trend for Seneca Foods'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.
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Seneca Foods Annual Data
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Trend |
Mar14 |
Mar15 |
Mar16 |
Mar17 |
Mar18 |
Mar19 |
Mar20 |
Mar21 |
Mar22 |
Mar23 |
| Beneish M-Score |
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-2.39 |
-1.15 |
-2.47 |
-2.02 |
-1.73 |
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Seneca Foods Quarterly Data
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Mar19 |
Jun19 |
Sep19 |
Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
| Beneish M-Score |
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-1.87 |
-1.73 |
-1.91 |
-2.36 |
-2.05 |
Competitive Comparison
For the Packaged Foods subindustry, Seneca Foods'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.
Seneca Foods Beneish M-Score Distribution
For the Consumer Packaged Goods industry and Consumer Defensive sector, Seneca Foods's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Seneca Foods's Beneish M-Score falls into.
Seneca Foods 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 Seneca Foods 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 * 0.978 | + | 0.528 * 0.8869 | + | 0.404 * 0.9807 | + | 0.892 * 0.9808 | + | 0.115 * 1.2026 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0798 | + | 4.679 * 0.120494 | - | 0.327 * 1.1083 |
| = | -2.05 |
* 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 (Dec23) TTM: | Last Year (Dec22) TTM: |
Total Receivables was $91 Mil.
Revenue was 444.481 + 407.475 + 298.664 + 331.063 = $1,482 Mil.
Gross Profit was 54.033 + 58.118 + 55.289 + -14.092 = $153 Mil.
Total Current Assets was $1,078 Mil.
Total Assets was $1,490 Mil.
Property, Plant and Equipment(Net PPE) was $351 Mil.
Depreciation, Depletion and Amortization(DDA) was $39 Mil.
Selling, General, & Admin. Expense(SGA) was $84 Mil.
Total Current Liabilities was $224 Mil.
Long-Term Debt & Capital Lease Obligation was $638 Mil.
Net Income was 17.675 + 24.779 + 23.111 + -33.057 = $33 Mil.
Non Operating Income was 1.867 + 1.329 + 1.197 + 0.076 = $4 Mil.
Cash Flow from Operations was -101.847 + -51.303 + 25.024 + -23.316 = $-151 Mil.
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Total Receivables was $95 Mil.
Revenue was 473.254 + 439.842 + 265.193 + 332.389 = $1,511 Mil.
Gross Profit was 53.789 + 41.779 + 22.843 + 20.26 = $139 Mil.
Total Current Assets was $894 Mil.
Total Assets was $1,305 Mil.
Property, Plant and Equipment(Net PPE) was $358 Mil.
Depreciation, Depletion and Amortization(DDA) was $49 Mil.
Selling, General, & Admin. Expense(SGA) was $80 Mil.
Total Current Liabilities was $247 Mil.
Long-Term Debt & Capital Lease Obligation was $435 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) |
| = | (91.081 / 1481.683) | / | (94.947 / 1510.678) |
| = | 0.061471 | / | 0.062851 |
| | = | 0.978 |
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) |
| = | (138.671 / 1510.678) | / | (153.348 / 1481.683) |
| = | 0.091794 | / | 0.103496 |
| | = | 0.8869 |
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 - (1078.295 + 351.398) / 1489.537) | / | (1 - (893.818 + 357.57) / 1304.844) |
| = | 0.040176 | / | 0.040967 |
| | = | 0.9807 |
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 |
| = | 1481.683 | / | 1510.678 |
| | = | 0.9808 |
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)) |
| = | (49.196 / (49.196 + 357.57)) | / | (39.29 / (39.29 + 351.398)) |
| = | 0.120944 | / | 0.100566 |
| | = | 1.2026 |
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) |
| = | (84.237 / 1481.683) | / | (79.537 / 1510.678) |
| = | 0.056852 | / | 0.05265 |
| | = | 1.0798 |
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) |
| = | ((638.068 + 224.049) / 1489.537) | / | ((434.549 + 246.9) / 1304.844) |
| = | 0.578782 | / | 0.522246 |
| | = | 1.1083 |
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 |
| = | (32.508 - 4.469 | - | -151.442) | / | 1489.537 |
| | = | 0.120494 |
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.
Seneca Foods has a M-score of -2.05 suggests that the company is unlikely to be a manipulator.
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