Building a Venture Capital Fund of Funds Portfolio

Peter Khayat
5 min read4 days ago


This post is about reverse engineering a Fund of Funds Business Plan. How? TLDR: fitting a diversified set of top funds into a modeled cash J-Curve.

Before we start

The FoF expected return: referring to that famous Techcrunch chart below, only about 15% of VC funds return at least 2 times the money invested by Limited Partners. By clustering top VC funds into a Fund of Funds, which like any fund, has its fees — say 1.3% annual management fees over 10 years and 15% carried interest— , a 2.5x DPI (cash multiple return) in 10 to 12 years, or 15%-20% IRR (yearly return), is quite fair to expect from the investors of a FoF.

The FoF diversification purpose: LPs that join a VC Fund of Funds, as well as many Family Offices that run a VC FoF strategy, do so to get a broad exposure to the Venture Capital sector, and hence indirectly to the fast-growing tech ecosystem companies, so why not embrace it by amplifying portfolio diversification?

Collage of AI-generated images of how a FoF portfolio could look like

Now let’s build the portfolio!

Step 1 — Focus on your goal: the targeted returns to your Limited Partners.

The strategy is to build a cash J-curve (not TVPI J-curve) to reach such target return (2.5x, 15–20%). Let’s consider the JC to be the base of the Fund of Funds business plan. Generally speaking:

  • Seed funds have a slow or elongated J-Curve but a high expected return.
  • Growth funds have a faster J-Curve but a lower expected return, as growth investments are expected to be less risky and closer to exit.

Net of double fees and carried, you could fairly expect a 3x to 5x cash return for seed funds and a 2x to 3x return for (early) growth or secondaries funds. Some seed funds did historically have J-curves faster than those of early growth funds (early exits, quick 1x DPI approach…), and entering the last closing of a seed fund could implicitly have the same effect if its first investments matured astonishingly. There are many Financially Attractive Venture Capital Funds to Invest in.

You could use the fund cash flow model (capital calls and distributions) provided by its managers, but I would advise remodeling if the fund expected return exceeds the above suggested.

In the example below, the JC of the growth fund in the portfolio is more aligned with that of the business plan compared to JC of the seed fund that was committed to a year earlier. To compensate for lagging cash returns of seed funds, or to “accelerate” the JC, incorporating secondaries funds or near-exit direct investments to the portfolio does a good job to boost DPI.

Cash J-Curve of 3 portfolio funds compared to FoF business plan with a target net return of 2.5x, all taking into account double fees and double carried.

Step 2— Maximize diversification within the thesis.

Having checked the J-curve fit, I would split FoF diversification into two layers.

The first layer consists of the four essential components that characterize a VC fund. Feel free to define the categories and allocation % for each. My pick:

  • Stage focus: Seed, Series A, Series B+. I would pick multi-stage funds or secondaries funds over Series C and beyond ones.
  • Sector focus: Enterprise Software, Web3/Gaming, Marketplaces, Energytech, Biotech/health…
  • Portfolio geography where the fund will invest, not the fund jurisdiction of incorporation: US (East, West, Mid…), Europe (UK, DACH, FR, Iberia, Baltics…), Latam, MENA…
  • Fund currency: USD, EUR, GBP…
Example: first layer diversification of a Europe-focused VC Fund of Funds

The second diversification layer consists of “nice to have” components which would be good metrics for a next fundraising deck. A single fund can fall into several categories. Examples:

  • Managers: established or emerging — say 70:30
  • “Quick DPI” (like secondary or pre-IPO funds) — say 20%
  • Approach: Generalist (deep tech agnostic, B2B software…) or thematic (AI, Web3, Femtech, Drug Discovery…)
  • Prestigious VCs — say 20% of Sequoia-like funds
  • ESG boosters (minorities-focused, climatetech…) — say 15%

And there goes your market research on funds.

  • If you need a Seed fund that is focused on Iberia, find a set (let’s say 10 to 15), filter out those that cannot fit in your J-curve (for example if they will fundraise at the end of your investment period), then those that focus on sectors you have too much exposure too.
  • If you need a “women-focused fund”, find a set, filter out those that cannot fit in your J-curve, then those that invest in sectors, geographies, stages and currencies that you have too much exposure to.

In any case, pick the best among remaining ones according to step 3. Tip: do not feel constrained to fill the spot if you could not find any appropriate fund, nor hesitate to spread the ticket allocation into 2–3 good and non-competing funds.

Step 3— Assess opportunities using a scorecard.

My suggested method is to use a two-stage process.

  • First, a knock-out stage validates steps 1 and 2 above and incorporates some tweaks like “flags” (example: recent news) and extraordinary positive facts (example: Midas List…). A minimum grade of 6/10 is required to pass to the next stage.
  • The second stage consists of raw-evaluating the fund to commit and its asset-manager irrespective of the FoF portfolio fit. Evaluation criteria differ between established and emerging managers. Grading example: an established manager with two of the last 3 funds at 5x+ DPI and newest one at 1x DPI would receive a perfect 10/10 grade on “track record — multiples”, while one with 4x 2x 0.5x DPIs would receive a 7/10 “pass” grade.

Finally, a grade over 10 is obtained from the two stages, with a minimum of 7/10 needed to approve the investment.

BONUS: here are scorecard examples