| Course | ACC 423 Detection/Prevention Fraudulent Financial Statements |
|---|---|
| Module | Module 4 |
| Paper type | undergraduate red flag analysis project using the Beneish M-score |
| Length | About 1,000 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | BS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 423 Module 4
Reading the Numbers for Signs of Manipulation: A Beneish M-Score and Red Flag Analysis of a Composite Water Heater Maker
[Student Name]
Southern New Hampshire University
ACC 423: Detection and Prevention of Fraudulent Financial Statements
Project One
[Instructor Name]
[Date]
The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.
Reading the Numbers for Signs of Manipulation: A Beneish M-Score and Red Flag Analysis of a Composite Water Heater Maker
Introduction
An audit committee member at the water heater maker asked a simple question after the year-end earnings release: do the reported numbers look like those of a company that is managing its results? This project answers it with two tools. The first is the eight-variable model developed by Beneish (1999), which compares a company's year-over-year changes in eight ratios with those of companies later found to have manipulated earnings. The second is traditional red flag analysis of the statements and notes. All figures below are as originally reported, before the corrections identified in Modules Two and Three.
Inputs
Table 1. Reported Data (millions of dollars)
| Item | Prior year | Current year |
|---|---|---|
| Sales | 372.0 | 410.0 |
| Cost of goods sold | 279.0 | 303.4 |
| Accounts receivable | 46.5 | 66.0 |
| Current assets | 128.0 | 152.0 |
| Property, plant and equipment, net | 96.0 | 108.0 |
| Securities | 4.0 | 4.0 |
| Total assets | 262.0 | 300.0 |
| Depreciation | 11.6 | 11.3 |
| Selling, general and administrative expense | 52.1 | 54.5 |
| Long-term debt | 58.0 | 72.0 |
| Current liabilities | 61.0 | 70.0 |
| Net income | 24.6 | |
| Cash flow from operations | (2.0) |
The Eight Variables
Table 2. Beneish Variables and Weighted Contributions
| Variable | What it measures | Value | Coefficient | Contribution |
|---|---|---|---|---|
| DSRI, days sales in receivables index | Receivables relative to sales, this year versus last | 1.288 | 0.920 | 1.185 |
| GMI, gross margin index | Prior margin divided by current margin | 0.962 | 0.528 | 0.508 |
| AQI, asset quality index | Share of assets other than current assets, PP&E and securities | 0.925 | 0.404 | 0.374 |
| SGI, sales growth index | Current sales divided by prior sales | 1.102 | 0.892 | 0.983 |
| DEPI, depreciation index | Prior depreciation rate divided by current rate | 1.138 | 0.115 | 0.131 |
| SGAI, SG&A index | SG&A to sales, this year versus last | 0.949 | negative 0.172 | negative 0.163 |
| TATA, total accruals to total assets | (Net income minus operating cash flow) divided by total assets | 0.089 | 4.679 | 0.415 |
| LVGI, debt index | Debt to assets, this year versus last | 1.042 | negative 0.327 | negative 0.341 |
| Constant | negative 4.840 | |||
| M-score | negative 1.748 |
Interpreting the Score
The M-score of negative 1.75 is just above Beneish's threshold of negative 1.78, placing the company on the side associated with manipulators. Several variables drive the result.
The days sales in receivables index of 1.29 means receivables grew much faster than sales: $66.0 million against $46.5 million, a 42 percent increase on 10 percent sales growth. That is the trace of the extended terms and channel stuffing found in Module Two.
Total accruals of 0.089 of total assets reflect net income of $24.6 million alongside negative operating cash flow. Earnings that do not turn into cash are among the clearest signals in the model, and its heavy coefficient turns a small ratio into a meaningful contribution.
The depreciation index of 1.14 shows the depreciation rate fell, consistent with capitalized costs being added to the asset base and with longer effective lives. The sales growth index adds weight because growth firms face more pressure to sustain growth, which is part of why Beneish found them more likely to manipulate.
Two variables point the other way. The gross margin index is below 1 because margins improved, which the model treats as lowering risk; in this case the improvement came partly from the warranty reserve release, which the model cannot see. Asset quality also improved slightly, because the capitalized start-up costs were recorded as equipment rather than as other assets, again hiding the problem from the index designed to catch it.
Red Flags Outside the Model
Several signals do not appear in the model at all. Nearly a third of the year's sales landed in the final quarter, well above the usual quarter-four share. The warranty expense rate fell while the company's own service data show rising claims. Capital spending rose 38 percent while capacity rose 12 percent. Inventory days rose despite sales growth. The CFO's earnings call described the quarter as the strongest in company history but did not mention payment terms. Dechow et al. (2011) found that models combining accrual measures with nonfinancial and market signals predict misstatements better than accrual measures alone, which supports looking beyond the score.
What the Score Would Be After Correction
It is instructive to rerun the model with the corrections identified in Modules Two and Three. Reversing $12.6 million of revenue and the related receivables lowers the receivables index, and expensing the start-up costs, restoring the warranty accrual and writing down inventory reduce net income without changing operating cash flow, which cuts total accruals sharply. Gross margin falls back toward the prior year, raising the margin index. On corrected figures the score falls well below the threshold, into the range typical of companies without manipulation. That is the expected pattern: the model flags the reported numbers precisely because of the items the earlier modules found, and once they are removed the company looks like an ordinary manufacturer with modest growth. The exercise also shows why the score is useful as a screening tool. It pointed to the same accounts that detailed work later confirmed, using only published statements and an hour of arithmetic.
Limits and Recommendation
The M-score does not prove manipulation. It was estimated on firms from an earlier period, it misclassifies many honest growth companies and it can miss schemes that disguise themselves in the very ratios it uses, as the asset quality index did here. Dechow et al. (1996) also show that manipulation is typically discovered only after accruals reverse, so the score is a leading indicator at best. The appropriate response is inquiry, not accusation. The audit committee should ask management and the external auditors about receivable terms, the warranty rate change and the composition of capital spending before the annual report is filed, and should ask for written answers it can keep in its minutes.
Conclusion
On reported figures, the water heater maker's eight-variable M-score is negative 1.75, just across the threshold associated with manipulation, driven by receivables growth, accruals and depreciation. Additional red flags outside the model point to the same accounts. The analysis does not establish fraud, but it gives the audit committee specific, documented reasons to ask specific questions.
References
Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17-82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13(1), 1-36. https://doi.org/10.1111/j.1911-3846.1996.tb00489.x
What the ACC 423 Module 4 instructions ask for
Project One in ACC 423 usually asks you to analyze a company's financial statements for signs of manipulation, often using a quantitative model such as the Beneish M-score alongside traditional ratio analysis. Expect to compute each model variable from two years of data, combine them into the score, compare it with the threshold, explain which variables drive the result and identify additional red flags from the statements, notes and disclosures. Many versions ask for recommendations to an investor, auditor or audit committee. Show inputs and formulas for every variable, explain what each measures in business terms and state clearly that a model score indicates elevated risk, not proof.
How this ACC 423 Module 4 project one example is built
The sample computes the eight Beneish variables for the water heater maker. The days sales in receivables index of 1.29 reflects receivables growing 42 percent on 10 percent sales growth. The sales growth index is 1.10, and total accruals to total assets are 0.089 because net income of $24.6 million came with negative operating cash flow. The gross margin index is below 1 because margins rose, and asset quality improved slightly. The weighted sum gives an M-score of negative 1.75, above Beneish's negative 1.78 threshold. Red flags outside the model include fourth-quarter revenue concentration, a falling warranty rate and capital spending outpacing capacity. The report recommends audit committee inquiry.
Where the ACC 423 Module 4 rubric puts the points
Rubrics for ACC 423 Project One typically score the accuracy of each variable, the combined score and threshold comparison, interpretation of drivers, additional red flags, limitations and recommendations, plus report quality. Top papers show inputs for every variable, apply the published coefficients correctly, explain which variables contribute most, and connect them to specific accounts and business events. Graders reward a balanced conclusion that treats the score as a reason to investigate and names the accounts to examine first. Common deductions include sign errors on the coefficients for SGAI and LVGI, using year-end data from the wrong years, reading a score just past the threshold as proof of fraud and omitting non-model red flags.
ACC 423 Module 4 help: the mistakes that cost points
Projects that use the M-score often lose points through arithmetic slips: reversing current and prior years in an index, using total assets where the formula calls for current assets, or forgetting that two coefficients are negative. Another frequent problem is overstating the conclusion; a score near the threshold is a reason to look harder, not a finding. If your project uses another model, such as the Dechow F-score or an accrual-based measure, the same structure of inputs, score, drivers and limits applies to it. Build a table showing each variable, its value, its coefficient and its weighted contribution; graders can then check every number and see exactly what drives the result.
Get ACC 423 Module 4 written to your instructions
Share the ACC 423 Project One guidelines with the statements and rubric, and we will compute each model variable with its inputs, interpret the score and its drivers, add red flags outside the model and state what the analysis does and does not show. The first request is free, and most are ready in two days. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
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ACC 423 Module 4 questions, answered
Where can I find a free ACC 423 Module 4 Project One sample?
This page shows a complete ACC 423 Module 4 Project One computing an eight-variable Beneish M-score and red flag analysis.
What is the Beneish M-score?
A model that combines eight financial ratios measuring changes in receivables, margins, asset quality, sales growth, depreciation, SG&A, debt and accruals to estimate the likelihood of earnings manipulation.
What M-score threshold indicates likely manipulation?
Beneish's original work used negative 1.78 for the eight-variable model; scores above it, closer to zero, suggest a higher likelihood of manipulation.
Does a high M-score prove fraud?
No. It indicates characteristics shared by firms that manipulated earnings. Growth firms and companies in transition can score high for legitimate reasons.
Which M-score variable is most important?
Coefficients and data vary, but total accruals to total assets and the days sales in receivables index often contribute most, because manipulation commonly inflates both.