The fund size is only kind of the strategy

Comparing expected fund returns across different deployment strategies in venture capital

A common phrase amongst venture capitalists is that, “the fund size is the strategy”. This is both true and overly reductive. A more accurate statement would be that, “there are a set of deployment strategies that are unrealistic or extremely high risk at each fund size.”

There are a few key elements of any venture capital strategy from a pure capital deployment standpoint, most of which are obvious. They define what type(s) of investments the fund will make. For early stage venture:

Key Primary Decisions:

• Stage: for an early stage fund, they may consider pre-seed, seed, and Series A

• Ownership: “lead” investments with 7.5-15% ownership, or “follow” with 2-7.5% ownership

• Follow-on reserves: funds commonly reserve between 0% and 55% of the fund size to maintain their ownership in portfolio companies through pro rata investments in future rounds that limit (or eliminate) dilution

Downstream impact:

• Average entry check size: Expensive market conditions require more capital in each investment to hit target ownership (and vice versa)

• Size of portfolio (# of investments): Depending on the entry check size and follow-on reserves, the fund will have capital to make a set number of primary investments in net new companies

We can model a variety of key primary decisions as inputs to a Monte Carlo simulation and compare the performance of different strategies in similar market conditions.

As part of Gradient’s Fund 5 fundraise last year, I built out a set of simulation logic that I’ve made available in this web app here (website). Affectionately referred to as “The Monaco GP” because of my love for F1 - ha, get it? The app runs monte carlo simulations that model the probability of different fund returns by running thousands of random, iterative fund lifecycles.

Fund strategy for a $100M fund

The basic tradeoffs of different fund strategies are easiest to see when we consider a larger fund, so let’s look at a $100M fund (click here for the interactive web app version of the chart below)

These four strategies are ordered from most diverse (#1) to most concentrated (#4). Each step to the right raises both entry ownership (2.5% → 5% → 10% → 15%) and reserves, shrinking the portfolio from 160 companies down to 22. Right out of the gate, the strategies at both ends look less practical to execute or generate less compelling returns:

• Too many deals: Strategy #1 requires 160 primary investments. Over a 30-month deployment window, that’s more than five new deals every month. That’s likely not realistic for a $100M fund. And at 2.5% ownership, you own so little of each company that even top quartile funds with a big winner only generate 2.5x MOIC.

• Too much concentration: Strategy #4 is so concentrated that half of funds running that strategy will actually lose money (median returns are 0.9x). Even a top-quartile fund rarely lands a meaningful winner which is why you see returns sink to 1.8x for that strategy. The concentration doesn’t even buy you the top decile: #4’s P90 (5.5x) is actually beaten by the less-concentrated #3 (6.1x). Only the extreme tail (a 9.2x P95) rewards it, and most funds will never get there.

GPs running a $100M fund likely want to pursue some permutation of strategies #2 and #3 (highlighted in green). Both can work depending on the skillset of the GP:

• Strategy #2 has the best top-quartile return in the set (2.8x) and the sturdier floor. If you have access to a large amount of deal flow from Tier 1 funds where you can follow-on for meaningful (~5%) ownership, this is the more reliable path.

• Strategy #3 trades that top-quartile reliability for the better top-decile returns (P90 of 6.1x). If you can win and lead deals on your own and believe in your picking ability, this is the bigger swing.

Fund strategy for a $30M fund

(Click here for the interactive web app version of the chart below)

The same four strategies for a $30M fund, and the set of reasonable strategies shifts.

Too concentrated to justify the risk: Strategies #3 and #4 shrink the portfolio to 9-13 companies, and the math doesn’t reward it. Their top quartile (1.4-1.5x) and top decile (3.5x) are actually lower than the more diversified strategies above them and not to mention, the median outcome loses half of your LPs money.

Some permutation of strategy #1 and #2 that balances deal volume with ownership is likely the best route for most GPs.

So what does this all mean?

Simulating thousands of fund lifecycles is not the same as forecasting the returns of an individual fund. Ultimately, for a single venture fund to be successful, they have to:

1. Pick winners AND

2. Own a meaningful amount of those winners

No amount of fund strategy can save you if you’re a bad picker or you own a minuscule amount in your winners. However, some fund strategies have structurally higher floors and upside than others. Others offer even better upside at higher risk profiles.

It’s useful to play with the inputs to your fund strategy (e.g., how much you reserve for follow-on, how many investments you make, what entry ownership you target, etc.) to understand how the funds might perform in different market scenarios with expected outcomes. LPs certainly consider

How can I compare fund strategies?

I built the aforementioned Fund Strategy Comparison tool so GPs and LPs could compare the outcomes of similar fund strategies and understand the tradeoffs. Two notes:

1. The best way to use this tool is to compare strategies, not to evaluate actual expected MOIC / TVPI. The return quartiles are a direct function of the historical market inputs which can be seen here. Most acutely, at what rate companies are graduating from one stage to the next [e.g., seed to Series A]. We don’t know what market graduation rates and valuation rates, so exact performance is speculative.

2. This analysis and the visuals focus more heavily on the returns between top quartile to top decile. This is deliberate. No LP is investing in a VC firm for median returns. They could get those return profiles with lower risk assets in a variety of other markets.

3. The fund strategy comparison tool is really best used for pre-seed, seed, and Series A funds (or a mixture thereof).

Happy investing & if you ever want to talk fund strategy, shoot me a note. I’m easy to reach.

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A huge thank you to Peter Walker from Carta who helped us pull market data to inform the simulations.