with Marc Penkala, General Partner at āltitude
EUVC Academy · 1h · Fund Modelling
Fund modelling in venture capital translates an investment strategy into a structured model of returns. It matters because assumptions around portfolio construction, fees and follow-on strategy directly determine fund outcomes.
This session focuses on building a fund model from first principles, linking core assumptions to portfolio construction, portfolio decomposition and the distribution waterfall.
It covers alpha and beta, power law dynamics, portfolio size, fund size, follow-on strategy, fees, deployment ratios and LPA constraints to model realistic TVPI, DPI and MOIC outcomes.
Key Learning Points
Alpha, beta and power law dynamics
Fund outcomes are driven by a small number of outliers with large magnitude
Increasing alpha typically requires accepting higher beta and more losses
Unrealistic outlier assumptions are unlikely to reflect real portfolio distributions
Portfolio construction and strategy trade-offs
Portfolio size drives dispersion: smaller portfolios have wider outcome ranges, larger ones narrower
Follow-on strategy depends on having a sufficiently large picking pool to back winners
Alpha can be increased through volume (numbers game) or through strategy (sourcing and picking)
Fund size, fees and deployment effects
Larger funds require higher MOIC on a single asset to return the fund
Fees and deployment ratio materially affect the relationship between MOIC and TVPI
Recycling and higher deployment can reduce required per-asset performance
Assumption-driven modelling and fund mechanics
The assumption sheet is the core driver linking all parts of the fund model
Portfolio construction feeds into decomposition, which then determines the distribution waterfall
LPA constraints define the limitations within which the fund model is built
