--- name: modeling-credit-fund-portfolios-c34132d3402e language: en description: Builds credit fund portfolio models with yield attribution, default/recovery scenarios, and portfolio-level return analysis. Use when modeling credit funds, projecting portfolio returns, or analyzing yield components. tags: - modeling - credit-and-institutional-lending - portfolio - credit metadata: author: casemark practice_areas: - Credit Markets - Leveraged Lending - Direct Lending document_types: - Financial Model skill_modes: - Modeling - Forecasting --- # Modeling Credit Fund Portfolios Builds credit fund portfolio models with yield attribution, default/recovery scenarios, and portfolio-level return analysis for direct lending, broadly syndicated loan, and private credit strategies. ## When To Use - Projecting net returns for a credit fund across base, stress, and downside scenarios - Decomposing portfolio yield into coupon, OID, fee income, and PIK components - Modeling default waterfalls with recovery timing and severity assumptions - Evaluating the impact of leverage (subscription lines, asset-level facilities) on equity returns - Preparing LP reporting models or IC memoranda that require portfolio-level return attribution ## Inputs To Gather - **Portfolio composition**: loan tape or representative pool (borrower, commitment size, drawn %, spread, floor, OID, maturity, asset type) - **Fund terms**: management fee rate, incentive fee / carried interest structure, hurdle rate, preferred return, catch-up, GP commitment % - **Leverage assumptions**: advance rate, cost of borrowing on credit facility, commitment fee on undrawn, covenant headroom - **Credit assumptions**: annual default rate, loss given default (LGD) or recovery rate, recovery lag (months), prepayment rate (CPR or voluntary) - **Deployment schedule**: ramp period, reinvestment period end, harvest / wind-down timeline - **Fee income**: upfront origination fees, amendment/waiver fees, prepayment penalties, LIBOR/SOFR floor benefit [VERIFY: confirm current reference rate and transition status] ## Workflow 1. **Build the loan tape model** - Populate each position with par amount, spread (S + margin), SOFR floor, OID amortization schedule, maturity, and PIK toggle if applicable - Calculate weighted-average spread, weighted-average life (WAL), and cash vs. PIK yield split - Flag any floating-rate mismatches between assets and liabilities 2. **Construct yield attribution** - Separate gross portfolio yield into: (a) cash coupon, (b) OID accretion, (c) origination/amendment fee amortization, (d) PIK accrual, (e) SOFR floor benefit - Sum to gross asset yield; subtract cost of fund-level leverage to arrive at net asset yield - Layer in management fees and fund expenses to compute net investment income (NII) 3. **Model default and recovery scenarios** - Define scenarios — e.g., base (1–2% annual default, 60–70% recovery), stress (4–5% default, 40–50% recovery), severe (8%+ default, 25–35% recovery) [VERIFY: adjust ranges to match fund vintage and asset class norms] - Apply defaults as random or front-loaded timing vectors across the portfolio life - Model recovery cash flows with a lag (typically 12–24 months post-default) and haircut to par - Calculate net credit losses per period and cumulative loss rate 4. **Layer leverage and compute equity returns** - Model subscription-line draws during ramp, converting to term asset-level leverage post-ramp - Calculate interest expense on drawn leverage, undrawn commitment fees, and facility amortization - Compute levered vs. unlevered returns: gross ROA → levered gross return → net-of-fee return to LPs - Derive gross and net IRR, MOIC, and DPI across the fund life for each scenario 5. **Build the waterfall and carried interest schedule** - Map cash flows through the distribution waterfall: return of capital → preferred return → GP catch-up → carried interest split - Compute GP economics (management fees + carry) and LP net returns separately - Sensitivity-test the waterfall on deployment pace, default timing, and prepayment speed 6. **Run sensitivity and scenario tables** - Two-way tables: default rate vs. recovery rate → net IRR to LPs - Two-way tables: spread compression vs. prepayment speed → gross yield - Toggle leverage on/off to isolate leverage contribution to returns - Stress-test SOFR path scenarios (parallel shift, inversion) on floating-rate NIM ## Output - **Portfolio summary**: position count, total commitments, drawn balance, WAL, WA spread, WA OID, cash/PIK mix - **Yield attribution table**: line-item decomposition from gross asset yield to LP net return - **Scenario matrix**: base / stress / severe cases showing gross IRR, net IRR, MOIC, DPI, cumulative loss rate - **Leverage impact summary**: unlevered vs. levered returns with advance rate and borrowing cost shown - **Waterfall schedule**: period-by-period cash flows to LP and GP, with carry crystallization timing - **Sensitivity tables**: two-way grids on key drivers (default/recovery, spread/prepay, SOFR path) ## Quality Checks - Confirm WAL and WA spread match the loan tape; reconcile any differences from PIK or OID treatment - Verify that gross-to-net bridge is fully traceable (no unexplained leakage between gross yield and LP net return) - Ensure default timing vectors sum to the stated cumulative default rate over fund life - Check that recovery cash flows are lagged correctly and do not exceed par - Validate waterfall math: LP preferred return accrues correctly; catch-up and carry split match fund LPA terms [VERIFY: confirm specific waterfall mechanics against the fund's LPA] - Cross-check levered return math — leverage should amplify both upside and downside symmetrically relative to the advance rate and spread-over-borrow differential - Confirm SOFR floor benefit is calculated only when reference rate falls below the contractual floor - Test edge cases: 100% prepayment in year 1, zero defaults, and full portfolio wipeout to ensure model stability --- Source attribution: CaseMark Original project: https://github.com/CaseMark/skills/blob/449269ec49b6a8f1b4614cea52aa44b2a76e4e94/skills/capital/modeling-credit-fund-portfolios/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: CaseMark Original project: https://github.com/CaseMark/skills/blob/449269ec49b6a8f1b4614cea52aa44b2a76e4e94/skills/capital/modeling-credit-fund-portfolios/SKILL.md Attribution basis: Source repository record Fields of work: Finance & Accounting, Marketing, Sales Roles: Accountant, Business Development, CFO / FP&A, Financial Analyst, Growth Marketer Specialties: API Tools: Financial Modeling Software Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 488547fb54a712137411bbd022ef7ef70466eb704c476689849e1ecf5b528b9f