Analyze alternative investments including hedge funds, private equity, and venture capital. Use when the user asks about hedge fund strategies (long/short, macro, event-driven), PE or VC performance metrics (IRR, TVPI, DPI), fee structures ('2-and-20', carry, hurdle rates), the J-curve effect, illiquidity premiums, lock-up periods, or hedge fund replication. Also trigger when users mention 'managed futures', 'CTA', 'fund of funds', 'vintage year', 'capital calls', 'distributions', 'carried interest', or ask how to evaluate an alternative investment manager.
npx skills add https://github.com/JoelLewis/finance_skills --skill alternatives
The standard hedge fund fee is "2-and-20" — 2% annual management fee on AUM plus 20% performance fee on profits.
Private equity funds typically show negative returns in the early years because management fees are charged on committed capital, initial investments are carried at cost or slightly written down, and returns have not yet materialized. As portfolio companies mature and are exited, returns improve. The characteristic shape — initial losses followed by gains — resembles the letter J.
PE fund performance is significantly influenced by the economic environment at the time of investment. Spreading commitments across multiple vintage years reduces the risk of investing all capital at unfavorable valuations.
The expected excess return demanded for accepting illiquidity — the inability to sell quickly at fair value. Private equity, venture capital, and certain hedge funds impose lock-up periods (1-10+ years). The illiquidity premium is theoretically 150-400bp for PE and private credit, though estimates vary and are debated.
Many hedge fund returns can be replicated with systematic factor exposure (equity market, size, value, momentum, credit, volatility selling). Research shows that a significant portion of hedge fund "alpha" is actually alternative beta — compensation for well-known risk factors. True alpha (manager skill net of factor exposure) is scarce and diminishing.
Key areas: operational risk (back-office, custody, valuation practices), strategy capacity (can the strategy scale?), manager skill vs factor exposure, transparency and reporting, alignment of interests, and regulatory compliance.
| Formula | Expression | Use Case |
|---------|-----------|----------|
| Management Fee | AUM × Management Fee Rate | Annual fee on assets |
| Performance Fee | max(0, Gains Above HWM) × Perf Fee Rate | Fee on profits |
| Net Return (2-and-20) | Gross Return - 2% - 20% × max(0, Gross - Hurdle) | After-fee return |
| TVPI | (Distributions + NAV) / Paid-In Capital | Total return multiple |
| DPI | Distributions / Paid-In Capital | Realized return multiple |
| RVPI | NAV / Paid-In Capital | Unrealized return multiple |
| IRR | Rate r: sum CF_t/(1+r)^t = 0 | Money-weighted return |
Given: $10M invested, gross return = 8%, 2% management fee, 20% performance fee, no hurdle rate
Calculate: Net return and fee drag
Solution:
Management fee = $10M × 2% = $200,000
Gross profit = $10M × 8% = $800,000
Performance fee = 20% × $800,000 = $160,000 (charged on gross profits; under this fee structure the management fee is calculated independently and is not deducted first — some funds instead charge the incentive fee net of the management fee, which would give 20% × $600,000 = $120,000; always check the fund documents)
Total fees = $200,000 + $160,000 = $360,000
Net return = ($800,000 - $360,000) / $10,000,000 = 4.4%
Fee drag = 8.0% - 4.4% = 3.6 percentage points
The investor keeps 4.4% of the 8.0% gross return. Fees consume 45% of gross returns in this example. At lower gross returns, the fee drag as a percentage becomes even more severe.
Given: A PE fund calls $2M/year for 5 years (total $10M). Distributions: Year 4 = $1M, Year 5 = $3M, Year 6 = $5M, Year 7 = $8M, Year 8 = $4M. No residual value after Year 8.
Calculate: DPI, TVPI, and approximate IRR
Solution:
Total distributions = $1M + $3M + $5M + $8M + $4M = $21M
Total paid-in = $2M × 5 = $10M
DPI = $21M / $10M = 2.1x
TVPI = (21M + 0) / $10M = 2.1x (no residual, so TVPI = DPI)
Cash flows for IRR: Year 1: -$2M, Year 2: -$2M, Year 3: -$2M, Year 4: -$2M + $1M = -$1M, Year 5: -$2M + $3M = +$1M, Year 6: +$5M, Year 7: +$8M, Year 8: +$4M
Solving for IRR numerically yields approximately 23%.
The J-curve is visible: negative net cash flows in years 1-4, turning positive in year 5, with the bulk of value returned in years 6-7.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take joellewis/alternatives from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.