--- name: conducting-peer-benchmarking-analysis-cf6aba90f977 language: en description: Evaluates fund performance against peer universes with vintage year comparison, quartile ranking, and strategy-specific benchmarking. Use when benchmarking fund performance, analyzing vintage comparisons, or assessing relative positioning. tags: - process - investor-relations-and-lp-reporting metadata: author: casemark practice_areas: - Investor Relations - LP Reporting - Fund Administration document_types: - Process Documentation skill_modes: - Process Management --- # Conducting Peer Benchmarking Analysis ## When To Use - Preparing quarterly or annual LP reports that require relative performance context - Responding to LP due diligence requests for peer comparison data - Evaluating fund positioning ahead of fundraising or investor meetings - Assessing whether a fund's return profile warrants strategy adjustments - Building track record presentations for new fund marketing materials ## Inputs To Gather - **Fund performance data**: Net IRR, gross IRR, TVPI, DPI, RVPI, and PME ratios for each fund vehicle - **Vintage year**: The year of first capital call (not final close) for accurate cohort matching - **Strategy classification**: Buyout, growth equity, venture (early/late), real estate (value-add/opportunistic/core), credit, infrastructure, secondaries, or fund-of-funds - **Geography focus**: North America, Europe, Asia-Pacific, global, or emerging markets - **Fund size band**: Confirm AUM range to select the correct peer slice (e.g., small-cap buyout vs. mega-cap) - **Benchmark source(s)**: Cambridge Associates, Preqin, Burgiss, PitchBook, Hamilton Lane, or proprietary LP datasets — note each source's methodology and universe construction [VERIFY] - **Reporting date**: As-of date for all metrics; confirm alignment between fund data and benchmark data timestamps - **Currency**: Base currency for the fund and whether benchmark data is hedged or unhedged ## Workflow 1. **Define the peer universe** - Match vintage year exactly; avoid blending adjacent vintages unless the universe is too small (< 15 funds) - Filter by strategy, geography, and fund size band - Document universe size (number of funds) and any exclusions applied - If using multiple benchmark providers, note universe overlap and methodology differences (e.g., Cambridge uses pooled IRR; Burgiss uses fund-level median) 2. **Normalize performance metrics** - Align as-of dates — if the fund reports on 12/31 but the benchmark updates on 9/30, flag the gap - Confirm net-to-LP vs. gross-of-fees basis; never compare net IRR to gross benchmarks - Convert to common currency if the fund and benchmark use different bases - For younger funds (vintage < 3 years), emphasize DPI and called capital % over IRR, which is volatile early in fund life 3. **Calculate quartile rankings** - Rank the fund within the peer universe for each metric: net IRR, TVPI, DPI - Assign quartile (Q1 = top 25%, Q2 = 25-50%, Q3 = 50-75%, Q4 = bottom 25%) - Report the exact percentile where available, not just the quartile band - Note the spread between quartile boundaries — a narrow Q1/Q2 boundary signals a compressed peer set 4. **Run PME analysis** (if applicable) - Select the appropriate public market index (e.g., S&P 500, MSCI World, Russell 2000) based on strategy and geography [VERIFY index selection against LP preferences] - Calculate Kaplan-Schoar PME, Long-Nickels PME, or Direct Alpha — document which method and why - A PME > 1.0 indicates outperformance vs. the public index on a cash-flow-weighted basis 5. **Contextualize the results** - Compare current quartile ranking to prior reporting periods — is the fund trending up or down? - Identify J-curve effects for younger funds that suppress early IRR - Note any survivorship bias in the benchmark dataset (liquidated underperformers may be excluded) - For funds nearing end of life, weight DPI and realized multiples more heavily than IRR 6. **Compile the benchmarking output** - Summary table: Fund metric | Peer median | Peer upper quartile | Fund quartile rank - Vintage year scatter plot or bar chart positioning the fund within the distribution - Narrative commentary explaining relative positioning, trends, and any caveats - Source attribution for all benchmark data with as-of dates ## Output - **Peer benchmarking summary table** with fund metrics alongside peer median, upper quartile, and lower quartile for each KPI - **Quartile ranking card** showing net IRR, TVPI, and DPI rankings with percentile positions - **PME comparison** (where applicable) with index selection rationale - **Narrative commentary** (2-4 paragraphs) contextualizing performance relative to peers, noting trends, J-curve effects, and data limitations - **Data source disclosures** listing benchmark provider, universe size, vintage year, as-of date, and any filters applied ## Quality Checks - Confirm vintage year assignment uses first capital call date, not final close date - Verify net vs. gross alignment — never mix fee bases in a single comparison - Check that peer universe size is disclosed; flag universes with fewer than 15 funds as potentially unreliable - Ensure as-of dates match within one quarter between fund data and benchmark data - Validate that quartile boundaries are calculated from the correct universe (not a broader or narrower slice) - Confirm PME index selection is appropriate for the fund's strategy and geography - Mark any stale benchmark data (> 6 months old) with [VERIFY] for updated figures - Review for survivorship bias disclosure — note whether the benchmark includes liquidated or written-off funds --- Source attribution: CaseMark Original project: https://github.com/CaseMark/skills/blob/449269ec49b6a8f1b4614cea52aa44b2a76e4e94/skills/capital/conducting-peer-benchmarking-analysis/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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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/conducting-peer-benchmarking-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Finance & Accounting, Marketing, Product Management, Sales Roles: Business Development, Financial Analyst, Marketing Analyst, Product Manager Specialties: Specialty not specified Tools: Burgiss, Cambridge Associates, Hamilton Lane, Pitchbook, Preqin Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5cf012e085d419dfccf743bc5273532333ead96482e488731e9550afa6b1be4a