| Course | GF510 Risk Analysis and Management |
|---|---|
| Unit | Unit 5 |
| Paper type | Credit exposure analysis |
| Length | About 890 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MS in Finance |
| Updated | October 2026 |
Free sample paper for GF510 Unit 5
$4.2 Million on Open Terms: A Unit 5 Credit Exposure Analysis of a Pool Maker's Largest Dealer
[Student Name]
Purdue University Global
GF510: Risk Analysis and Management
Unit 5 Credit Exposure Analysis
[Instructor Name]
[Date]
Ledgerwood Pools, Sabal Creek Pools and every figure are composite; the model, study and texts cited are real.
The Exposure in Context
Sabal Creek is a composite pool builder with fourteen branches in Texas and Florida and sales of about $96 million. It buys about $14 million of shells a year from Ledgerwood, 6 percent of Ledgerwood's sales, on net sixty-day terms with a credit limit of $4.2 million. At the latest month end it owed $3.1 million. Ledgerwood earns a gross margin of about 25 percent on these sales, roughly $3.5 million a year, so the relationship is worth protecting as well as measuring.
What the Statements Show
Three annual reports supplied by the dealer give the following picture.
Debt. Total debt of $27.5 million against EBITDA of $6.7 million, or 4.1 times. Two years ago the ratio was 2.9 times.
Interest coverage. Operating profit covered interest 3.8 times two years ago and 2.2 times last year, as rates on the dealer's floating credit line rose and sales slowed.
Liquidity. A current ratio of 1.3, adequate but thin for a business whose customers pay in stages.
Payment behavior. The dealer's average days to pay Ledgerwood have lengthened from 41 to 63, past the sixty-day terms.
Each measure on its own is a caution rather than an alarm. Together they describe a firm that borrowed to expand when demand was strong and is now carrying that debt into a slower market.
The Private-Firm Z-Score
Altman's original model predicted bankruptcy from five ratios using the market value of equity (Altman, 1968). For a private firm, a revised version substitutes book equity and re-estimates the weights, and a later review across many countries found that it keeps useful accuracy (Altman et al., 2017). For Sabal Creek, with total assets of $48 million:
1. Working capital to total assets: $6.0 million over $48 million, 0.125, times 0.717 gives 0.090.
2. Retained earnings to total assets: $7.0 million over $48 million, 0.146, times 0.847 gives 0.124.
3. Operating profit to total assets: $5.3 million over $48 million, 0.110, times 3.107 gives 0.342.
4. Book equity to total liabilities: $12 million over $36 million, 0.333, times 0.420 gives 0.140.
5. Sales to total assets: $96 million over $48 million, 2.0, times 0.998 gives 1.996.
The total is about 2.69. Scores above 2.90 fall in the safe zone and below 1.23 in the distress zone, so Sabal Creek sits in the gray zone, nearer its top.
Exposure at Default
If the dealer fails, it is likely to have drawn its whole limit first, since struggling firms stretch trade credit before they stop paying. Exposure at default is therefore the current balance of $3.1 million plus the $1.1 million still available, at a conversion factor of 100 percent: $4.2 million.
Probability and Loss Given Default
A gray-zone score near the top of the band corresponds roughly to a low speculative-grade borrower. S&P's long-run default studies put one-year default rates for issuers rated in the single-B-plus range at around 2 percent (S&P Global Ratings, 2024), and that figure is used as the probability of default.
Loss given default is set at 60 percent. Ledgerwood is an unsecured trade creditor with no claim on installed pools, and unsecured creditors typically recover well under half of what they are owed (Hull, 2023).
Expected Loss
Expected loss equals exposure times probability times severity: $4.2 million times 0.02 times 0.60, or about $50,400 a year. Against $3.5 million of annual gross margin, the base-case cost of carrying this dealer is small.
The base case says the dealer is a sound customer, but the same three numbers move together in a downturn, because a housing slowdown raises the dealer's chance of failing, pushes it to draw its whole limit and lowers what creditors recover, so the stress case matters more than the base case here.
Stress Case
Suppose the dealer's sales fall 25 percent, to $72 million, as pool orders did in earlier rate cycles. Operating profit would drop to about $1.3 million because branch costs are largely fixed, and working capital would shrink to about $4 million. The Z-score would fall to about 1.90: still gray, but closer to distress. Under those conditions a probability of default near 6 percent and a loss given default of 70 percent are reasonable, since recoveries fall when many builders fail at once. Expected loss rises to $4.2 million times 0.06 times 0.70, or about $176,000, three and a half times the base case.
Recommendation
Keep selling to Sabal Creek, but on tighter terms. First, reduce the unused limit from $1.1 million to $0.5 million, lowering exposure at default to $3.6 million. Second, take a purchase-money security interest in shells delivered but not yet installed, which would give Ledgerwood a claim on its own product and lower severity to about 45 percent in the base case and 55 percent under stress. Base-case expected loss would then fall to about $32,400 and stressed expected loss to about $119,000. Third, review the dealer's statements every quarter and cut the limit further if the Z-score drops below 2.0.
References
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z-score model. Journal of International Financial Management & Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053
Hull, J. C. (2023). Risk management and financial institutions (6th ed.). Wiley.
S&P Global Ratings. (2024). Default, transition, and recovery: 2023 annual global corporate default and rating transition study. S&P Global.
Reading the GF 510 Unit 5 instructions
The fifth GF510 unit asks for a credit exposure analysis of a named counterparty. Start by placing the exposure in context: what the counterparty buys, on what terms, and what the relationship is worth to the firm. Read the counterparty's statements over several years for debt, coverage, liquidity and payment behavior, and say what the pattern means. Apply a recognized model, such as the private-firm version of Altman's Z-score, showing every ratio and weight. Then estimate exposure at default, including undrawn credit, probability of default mapped from the model or a rating study, and loss given default suited to your position as creditor. Multiply to get expected loss. Because those inputs move together in a downturn, add a stress case. End with credit terms tied to the numbers. Cite the model and default data in APA 7.
How the GF 510 Unit 5 example is put together
Sabal Creek's fourteen branches, $96 million of sales and $4.2 million credit limit open the sample, along with Ledgerwood's $3.5 million yearly margin on the account. Four short paragraphs read the statements: debt up from 2.9 to 4.1 times, coverage sliding, a current ratio of 1.3 and payment days past terms. The Z-score section cites Altman's 1968 model and the 2017 international review, then lists five weighted ratios that sum to about 2.69. Exposure at default assumes the whole limit is drawn, the default probability is mapped to S&P's long-run single-B-plus rate and severity cites Hull. The highlighted sentence explains why probability, exposure and severity worsen together. A stress case drops the dealer's sales 25 percent and the score to about 1.90. The recommendation trims the unused limit, takes a security interest in uninstalled shells and sets a quarterly review trigger.
GF510 Unit 5 rubric: what earns full points
Marks for a GF510 credit analysis go mostly to correct, transparent calculation. Graders verify each ratio, each Z-score weight and the expected loss arithmetic, so every step should be visible. The next share rewards reasoning about inputs: why exposure at default includes undrawn credit, why a particular default rate was chosen and why severity fits an unsecured trade creditor. Instructors in this MS in Finance course value recognition that the three inputs are correlated, and a stress case is the usual evidence of that understanding. Reading the statements as a story, rather than listing ratios, earns credit for interpretation. A recommendation that changes terms in measurable ways, with recalculated expected loss, lifts a paper into the top band. Sources for the model and the default data, in APA 7, finish the grade.
GF510 Unit 5 help: mistakes that cost points
A frequent GF510 Unit 5 error is computing the original public-company Z-score for a private firm, using a market value that does not exist. Students also set exposure at default equal to today's balance, forgetting the undrawn limit a failing customer would use. Default probabilities are sometimes invented rather than mapped to published studies. Loss given default is assumed at zero because the customer "has assets," ignoring that a trade creditor ranks behind secured lenders. Ratios are listed without interpretation. Many analyses stop at expected loss with no stress case and no recommendation. If your counterparty is a supplier, a borrower or a derivatives client instead of a dealer, follow the same chain from statements to model to expected loss, then stress it and propose terms.
Get a GF510 Unit 5 example written to your instructions
The counterparty's financial statements, or the case figures, are the starting point; add the GF510 Unit 5 prompt and your rubric. Ratios come traced to line items, each component of expected loss sourced, with a stress case on top. You pay nothing for the first sample, which generally reaches you within 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More GF510 papers and MS in Finance samples
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- GF510 Unit 4: Pool Maker Risk Register
- GF510 Unit 6: Seminar Reflection
- GF510 Unit 7: Stress Scenario Write-up
- GF510 Unit 8: Operational Risk Case Study
- GF510 Unit 9: Treatment and Cost Plan
- GF510 Unit 10: Enterprise Risk Report
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GF510 Unit 5 questions, answered
Where do default probabilities come from if the counterparty is unrated?
Published default studies by the major rating agencies report historical default rates by grade, and a model score such as Altman's can be mapped to an equivalent grade. The sample does that and states the mapping openly. Where your instructions supply a probability instead, the custom version uses it and notes the source beside the figure.
What loss given default should an unsecured trade creditor assume?
Recovery studies generally show unsecured creditors recovering less than secured lenders, and many coursework cases use an assumption between 50 and 70 percent loss. Sixty percent is the sample's figure, stated as an assumption with a range. A sensitivity line showing expected loss at both ends of that range tends to strengthen the paper.
Is expected loss enough, or is unexpected loss required too?
Many sections ask only for expected loss, the average cost of carrying the exposure. Some ask for unexpected loss, the volatility around that average, which drives how much capital a lender holds. The sample adds a stress case as a practical stand-in; if your instructions require a formal unexpected loss calculation, the custom version includes it.
Where can I find a free GF510 Unit 5 sample paper?
A full GF510 Unit 5 credit exposure analysis of a composite pool dealer is on this page, with statement ratios, a private-firm Z-score, expected loss, a stress case, new terms and APA 7 references.
Why assume the dealer draws its whole credit limit before failing?
Firms in trouble typically stretch trade credit as far as they can before they stop paying. Counting the undrawn $1.1 million gives an exposure at default of $4.2 million, which is the honest figure for planning.