ESCO Technologies Beneish M-Score Historical Data
The historical data trend for ESCO 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.
ESCO Technologies Beneish M-Score Chart
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ESCO Technologies Annual Data
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Sep14 |
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Sep19 |
Sep20 |
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Sep22 |
Sep23 |
| Beneish M-Score |
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-2.41 |
-2.21 |
-2.71 |
-2.51 |
-2.26 |
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ESCO Technologies Quarterly Data
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Mar19 |
Jun19 |
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Dec23 |
| Beneish M-Score |
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-2.42 |
-2.35 |
-2.44 |
-2.26 |
-2.35 |
Competitive Comparison of ESCO Technologies's Beneish M-Score
For the Scientific & Technical Instruments subindustry, ESCO 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.
ESCO Technologies's Beneish M-Score Distribution in the Hardware Industry
For the Hardware industry and Technology sector, ESCO Technologies's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where ESCO Technologies's Beneish M-Score falls into.
ESCO 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 ESCO 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 * 1.0785 | + | 0.528 * 0.9837 | + | 0.404 * 0.9925 | + | 0.892 * 1.0935 | + | 0.115 * 0.9525 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0059 | + | 4.679 * 1.6E-5 | - | 0.327 * 1.0257 |
| = | -2.35 |
* 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 $332.8 Mil.
Revenue was 218.314 + 272.647 + 248.749 + 229.136 = $968.8 Mil.
Gross Profit was 84.163 + 108.223 + 101.475 + 86.84 = $380.7 Mil.
Total Current Assets was $603.2 Mil.
Total Assets was $1,772.5 Mil.
Property, Plant and Equipment(Net PPE) was $197.9 Mil.
Depreciation, Depletion and Amortization(DDA) was $51.6 Mil.
Selling, General, & Admin. Expense(SGA) was $219.8 Mil.
Total Current Liabilities was $304.7 Mil.
Long-Term Debt & Capital Lease Obligation was $187.7 Mil.
Net Income was 15.169 + 31.997 + 27.943 + 17.878 = $93.0 Mil.
Non Operating Income was -0.206 + -0.199 + -0.966 + -0.314 = $-1.7 Mil.
Cash Flow from Operations was 8.746 + 47.688 + 34.663 + 3.547 = $94.6 Mil.
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Total Receivables was $282.2 Mil.
Revenue was 205.501 + 256.498 + 219.066 + 204.928 = $886.0 Mil.
Gross Profit was 79.118 + 102.175 + 84.612 + 76.553 = $342.5 Mil.
Total Current Assets was $534.8 Mil.
Total Assets was $1,636.0 Mil.
Property, Plant and Equipment(Net PPE) was $197.9 Mil.
Depreciation, Depletion and Amortization(DDA) was $48.5 Mil.
Selling, General, & Admin. Expense(SGA) was $199.8 Mil.
Total Current Liabilities was $292.8 Mil.
Long-Term Debt & Capital Lease Obligation was $150.3 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) |
| = | (332.788 / 968.846) | / | (282.176 / 885.993) |
| = | 0.343489 | / | 0.318486 |
| | = | 1.0785 |
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) |
| = | (342.458 / 885.993) | / | (380.701 / 968.846) |
| = | 0.386524 | / | 0.392943 |
| | = | 0.9837 |
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 - (603.202 + 197.947) / 1772.526) | / | (1 - (534.771 + 197.878) / 1635.989) |
| = | 0.548018 | / | 0.552168 |
| | = | 0.9925 |
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 |
| = | 968.846 | / | 885.993 |
| | = | 1.0935 |
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)) |
| = | (48.538 / (48.538 + 197.878)) | / | (51.608 / (51.608 + 197.947)) |
| = | 0.196976 | / | 0.2068 |
| | = | 0.9525 |
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) |
| = | (219.776 / 968.846) | / | (199.794 / 885.993) |
| = | 0.226843 | / | 0.225503 |
| | = | 1.0059 |
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) |
| = | ((187.709 + 304.693) / 1772.526) | / | ((150.346 + 292.76) / 1635.989) |
| = | 0.277797 | / | 0.270849 |
| | = | 1.0257 |
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 |
| = | (92.987 - -1.685 | - | 94.644) | / | 1772.526 |
| | = | 1.6E-5 |
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.
ESCO Technologies has a M-score of -2.35 suggests that the company is unlikely to be a manipulator.
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