本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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)

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Evaluates activist investor campaigns with thesis assessment, proposed changes, and likely outcome analysis. Use when analyzing activist situations, evaluating campaign theses, or assessing activist track records.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

Evaluates corporate vulnerability to shareholder activism with governance assessment, valuation gaps, and operational improvement opportunities. Use when assessing activist risk, identifying vulnerabilities, or preparing defensive analyses.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

Evaluates bolt-on acquisitions for existing platforms with strategic fit, synergy quantification, and return contribution analysis. Use when screening add-on targets, building platform acquisition strategies, or modeling tuck-in economics.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

Evaluates signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

Evaluates competition law exposure with market definition, HHI analysis, and remedy estimation for proposed transactions. Use when assessing merger clearance risk, analyzing market concentration, or preparing HSR filings.

日本語の概要は準備中です。原文の説明を表示しています。

CaseMark/skills442026年9月9日 更新

CaseMark のスキルをすべて見る

このスキルの問題を報告する