Fund Returns / Power-Law Portfolio Modeler
Forward-looking fund construction. Build a venture or growth portfolio from a power-law outcome distribution, deploy your follow-on reserves into the winners, and see gross & net fund returns — MOIC, TVPI / DPI, net IRR after management fees and carry — plus how heavily the single home-run bucket drives total proceeds. Pure client-side math; your model never leaves your device.
Fund Economics
Portfolio Construction
Outcome Distribution (Power Law)
Fund Returns
Outcome Distribution
Fund Summary
Home-Run Multiple Sensitivity
Sweeps the home-run bucket's gross multiple, holding every other input constant — the clearest view of how one assumption drives the whole fund (the power law at work).
Methodology — math used in this model
Power-law buckets — your portfolio is split across 5 outcome buckets (Loss 0× → Home run 30×), each carrying a % of companies and a gross MOIC. Company counts are expected-value (fractional), not integer-rounded. If the bucket percentages don't sum to 100%, they're normalized internally to 100% (and the model notes it). Most of the fund's proceeds typically come from a single bucket — the "% of total proceeds" on the home-run row is bolded to make that concentration obvious.
Fees — total lifetime management fees = feePct × fundSize × life (a deliberately simple flat-fee model; real funds step fees down off invested capital and onto a reduced base over the harvest period — out of scope here). Investable capital = fundSize − totalFees, floored at 0.
Deployment & reserves — initial deployment = min(#investments × avgCheck, investable × (1 − reserveRatio)). Reserves = investable − initialDeployment. Each bucket's initial invested capital is its company-weight × initial deployment.
Follow-on into winners — reserves are deployed ONLY into the buckets that make up the top followOnConcTopPct% of companies by multiple. Buckets are sorted descending by multiple; we take them until their cumulative company-% reaches the top-% threshold, then split reserves pro-rata by each qualifying bucket's company-weight. Follow-on dollars earn that bucket's same gross multiple — i.e. you double down on companies at their winning outcome. This is a simplification: real follow-on requires graduation-rate modeling and pro-rata rights, which are out of scope.
Gross — total invested = initial deployment + reserves deployed. Gross proceeds = initial proceeds + follow-on proceeds. Gross MOIC = grossProceeds / totalInvested. Gross TVPI = grossProceeds / fundSize (LP perspective vs. committed).
Net of fees & carry — fees are already removed from investable capital (they reduce what gets deployed). Distributions to LPs = gross proceeds; carry = carryPct × max(0, grossProceeds − fundSize) (carry on whole-fund profit above committed — a simplification; real waterfalls use a preferred return / hurdle and a GP catch-up). Net to LPs = grossProceeds − carry. Net MOIC = netToLP / totalInvested; Net TVPI / DPI = netToLP / fundSize.
Net IRR — single-cashflow approximation: committed capital called at t=0, net distributions returned as one lump at t=life. netIRR = (netToLP / fundSize)^(1/life) − 1. This ignores J-curve timing, capital-call pacing, and staggered distributions — it's an annualized equivalent, not a true cash-flow IRR.
Concentration stats — top-bucket share of total proceeds, whether the home-run bucket alone returns the fund (top-bucket proceeds vs. committed size), and the loss ratio (% of companies in the 0× bucket).
What's intentionally omitted — J-curve / cash-flow timing, recycling of management fees and early distributions, cross-fund effects, preferred-return hurdles and GP catch-up, and graduation-rate modeling of follow-on rounds. For realized-cohort, vintage-level analysis see the free VC Vintage Explorer.
Educational financial-modeling tool — not investment advice and not a substitute for institutional-grade fund modeling. All math runs in your browser; no inputs are sent to a server.