analyzing-credit-fund-performance

Evaluates credit fund returns with yield attribution, mark-to-market dynamics, and performance comparison across credit strategies. Use when analyzing credit fund performance, decomposing returns, or benchmarking credit strategies.

11 stars

Best use case

analyzing-credit-fund-performance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Evaluates credit fund returns with yield attribution, mark-to-market dynamics, and performance comparison across credit strategies. Use when analyzing credit fund performance, decomposing returns, or benchmarking credit strategies.

Teams using analyzing-credit-fund-performance should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/analyzing-credit-fund-performance/SKILL.md --create-dirs "https://raw.githubusercontent.com/CaseMark/skills/main/skills/capital/analyzing-credit-fund-performance/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/analyzing-credit-fund-performance/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How analyzing-credit-fund-performance Compares

Feature / Agentanalyzing-credit-fund-performanceStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Evaluates credit fund returns with yield attribution, mark-to-market dynamics, and performance comparison across credit strategies. Use when analyzing credit fund performance, decomposing returns, or benchmarking credit strategies.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Analyzing Credit Fund Performance

## When To Use

- Decomposing credit fund total returns into yield income, spread movement, and credit migration components
- Benchmarking a credit strategy (direct lending, broadly syndicated loans, CLO equity, distressed) against peers or indices
- Evaluating manager performance across vintage years or market cycles
- Preparing IC memos, quarterly LP letters, or due diligence reports that require return attribution
- Assessing whether reported returns reflect genuine alpha vs. leverage, illiquidity premium, or mark-to-market lag

## Inputs To Gather

- **Fund-level data**: NAV history, IRR (gross/net), MOIC, DPI, RVPI, cash yield, total return time series
- **Portfolio composition**: sector/industry weights, rating distribution (BB/B/CCC split), fixed vs. floating mix, first lien vs. second lien vs. mezzanine breakdown
- **Benchmark indices**: LSTA Leveraged Loan Index, ICE BofA HY indices, Cliffwater Direct Lending Index, or relevant CLO tranche benchmarks
- **Fee structure**: management fee, incentive fee / carried interest, hurdle rate, catch-up, fee offsets
- **Leverage profile**: fund-level leverage (subscription lines, NAV facilities), asset-level leverage (CLO issuance, repo)
- **Mark-to-market policy**: frequency of third-party valuations, use of dealer marks vs. model marks, lag conventions
- **Vintage and deployment timing**: capital call schedule, deployment pace, reinvestment period status

## Workflow

1. **Normalize return data**
   - Convert reported returns to a common basis (time-weighted vs. money-weighted) — use IRR for drawdown funds, TWR for open-ended vehicles
   - Strip out subscription-line effects by computing IRR from actual LP cash flows vs. fund-level cash flows
   - Confirm currency exposure and whether returns are hedged or unhedged

2. **Decompose total return into components**
   - **Carry/yield income**: coupon income, OID accretion, fee income (origination, amendment, prepayment fees)
   - **Spread/mark-to-market**: unrealized gains/losses from spread tightening or widening; isolate rate moves from credit spread moves for floating-rate portfolios
   - **Credit losses**: realized defaults, recoveries, and write-downs; compute cumulative default rate and loss-given-default
   - **Leverage contribution**: estimate the return uplift attributable to fund-level or structural leverage

3. **Benchmark and compare**
   - Match the fund's strategy to the appropriate benchmark (e.g., direct lending vs. Cliffwater DLI, BSL vs. LSTA LLI)
   - Compare on gross and net basis; adjust for fee drag and leverage differences
   - Rank within peer universe by vintage year if available
   - Evaluate risk-adjusted metrics: Sharpe ratio (for liquid strategies), loss ratio, Sortino ratio, max drawdown

4. **Assess valuation and reporting quality**
   - Check NAV volatility relative to underlying market moves — suspiciously low vol may signal stale marks
   - Review frequency and methodology of independent valuations [VERIFY — valuation policies differ by fund structure and jurisdiction]
   - Flag any positions held at cost or above par for extended periods without supporting market data
   - Compare reported EBITDA adjustments on underlying credits to industry norms

5. **Identify risks and red flags**
   - PIK concentration: high PIK income can inflate reported returns while deferring cash realization
   - Concentration risk: single-name, sector, or sponsor concentration exceeding stated limits
   - Credit migration: drift toward lower-rated credits or covenant-lite structures vs. fund mandate
   - Denominator effects: unfunded commitments reducing reported leverage ratios without reducing actual risk

6. **Synthesize findings**
   - Summarize alpha sources vs. beta/structural exposures
   - Provide forward-looking assessment: how the portfolio is positioned for rate changes, spread widening, or default cycle acceleration
   - State confidence level on marks and data completeness

## Output

- **Return Attribution Table**: period-by-period breakdown of yield income, spread P&L, realized credit losses, leverage effect, and fees
- **Benchmark Comparison**: side-by-side metrics (IRR, MOIC, yield, loss rate) vs. index and peer quartiles
- **Portfolio Risk Dashboard**: rating migration matrix, sector concentration, top exposures, PIK percentage, covenant quality summary
- **Narrative Assessment**: 1–2 page summary of key performance drivers, valuation concerns, and forward outlook
- Flag items requiring [VERIFY] for data the analyst must confirm (third-party marks, borrower financials, fee calculations)

## Quality Checks

- Gross-to-net reconciliation: confirm that gross-to-net spread is explainable by stated fee terms and leverage costs
- Cash yield vs. total return gap: if total return materially exceeds cash yield, verify the source of unrealized gains
- Benchmark alignment: ensure the selected benchmark matches the fund's risk profile (do not compare a first-lien direct lending fund to a high-yield bond index)
- Time period consistency: all comparisons use the same measurement periods; do not mix inception-to-date with trailing periods
- Double-count check: PIK income and OID accretion should not appear in both yield and mark-to-market components
- [VERIFY] Regulatory and reporting standards applicable to the fund's domicile (e.g., ILPA reporting guidelines, AIFMD Annex IV requirements, SEC Form PF)

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