Exedy Beneish M-Score Historical Data
The historical data trend for Exedy'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.
Exedy Beneish M-Score Chart
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Exedy Annual Data
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Trend |
Mar15 |
Mar16 |
Mar17 |
Mar18 |
Mar19 |
Mar20 |
Mar21 |
Mar22 |
Mar23 |
Mar24 |
| Beneish M-Score |
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-3.06 |
-2.66 |
-2.50 |
-2.71 |
-2.84 |
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Exedy Quarterly Data
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Dec19 |
Mar20 |
Jun20 |
Sep20 |
Dec20 |
Mar21 |
Jun21 |
Sep21 |
Dec21 |
Mar22 |
Jun22 |
Sep22 |
Dec22 |
Mar23 |
Jun23 |
Sep23 |
Dec23 |
Mar24 |
Jun24 |
Sep24 |
| Beneish M-Score |
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-2.79 |
-2.74 |
-2.84 |
-2.81 |
-2.80 |
Competitive Comparison of Exedy's Beneish M-Score
For the Auto Parts subindustry, Exedy'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.
Exedy's Beneish M-Score Distribution in the Vehicles & Parts Industry
For the Vehicles & Parts industry and Consumer Cyclical sector, Exedy's Beneish M-Score distribution charts can be found below:
* The bar in red indicates where Exedy's Beneish M-Score falls into.
Exedy 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 Exedy 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 * 0.9159 | + | 0.528 * 0.8587 | + | 0.404 * 2.1375 | + | 0.892 * 1.0412 | + | 0.115 * 0.872 |
| - | 0.172 * SGAI | + | 4.679 * TATA | - | 0.327 * LVGI |
| - | 0.172 * 1.0659 | + | 4.679 * -0.134383 | - | 0.327 * 1.0429 |
| = | -2.80 |
* 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 円54,010 Mil.
Revenue was 78352 + 75447 + 76457 + 80686 = 円310,942 Mil.
Gross Profit was 15745 + 12951 + 15289 + 14794 = 円58,779 Mil.
Total Current Assets was 円162,194 Mil.
Total Assets was 円304,807 Mil.
Property, Plant and Equipment(Net PPE) was 円113,589 Mil.
Depreciation, Depletion and Amortization(DDA) was 円16,857 Mil.
Selling, General, & Admin. Expense(SGA) was 円37,684 Mil.
Total Current Liabilities was 円52,910 Mil.
Long-Term Debt & Capital Lease Obligation was 円22,108 Mil.
Net Income was 2775 + 3529 + -18229 + 2837 = 円-9,088 Mil.
Non Operating Income was 0 + 0 + 0 + 0 = 円0 Mil.
Cash Flow from Operations was 4475 + 6756 + 14354 + 6288 = 円31,873 Mil.
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Total Receivables was 円56,632 Mil.
Revenue was 77827 + 73367 + 72304 + 75129 = 円298,627 Mil.
Gross Profit was 13019 + 10748 + 12562 + 12146 = 円48,475 Mil.
Total Current Assets was 円173,282 Mil.
Total Assets was 円342,293 Mil.
Property, Plant and Equipment(Net PPE) was 円153,763 Mil.
Depreciation, Depletion and Amortization(DDA) was 円19,528 Mil.
Selling, General, & Admin. Expense(SGA) was 円33,953 Mil.
Total Current Liabilities was 円55,892 Mil.
Long-Term Debt & Capital Lease Obligation was 円24,884 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) |
| = | (54010 / 310942) | / | (56632 / 298627) |
| = | 0.173698 | / | 0.189641 |
| | = | 0.9159 |
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) |
| = | (48475 / 298627) | / | (58779 / 310942) |
| = | 0.162326 | / | 0.189035 |
| | = | 0.8587 |
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 - (162194 + 113589) / 304807) | / | (1 - (173282 + 153763) / 342293) |
| = | 0.095221 | / | 0.044547 |
| | = | 2.1375 |
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 |
| = | 310942 | / | 298627 |
| | = | 1.0412 |
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)) |
| = | (19528 / (19528 + 153763)) | / | (16857 / (16857 + 113589)) |
| = | 0.112689 | / | 0.129226 |
| | = | 0.872 |
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) |
| = | (37684 / 310942) | / | (33953 / 298627) |
| = | 0.121193 | / | 0.113697 |
| | = | 1.0659 |
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) |
| = | ((22108 + 52910) / 304807) | / | ((24884 + 55892) / 342293) |
| = | 0.246116 | / | 0.235985 |
| | = | 1.0429 |
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 |
| = | (-9088 - 0 | - | 31873) | / | 304807 |
| | = | -0.134383 |
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.
Exedy has a M-score of -2.80 suggests that the company is unlikely to be a manipulator.
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