Abstract: Most VC funds are far too concentrated in a small number (<20–40) of companies. The industry would be better served by doubling or tripling the average # of investments in a portfolio, particularly for early-stage investors where startup attrition is even greater. If unicorns happen only 1–2% of the time, it logically follows that portfolio size should include a minimum of 50–100+ companies in order to have a reasonable shot at capturing these elusive and mythical creatures.
Like startups, most venture capital firms fail — at least in terms of returns.
Historically, 1/2 of all VC funds fail to return 1X initial capital. Another 1/4 fail to beat the (much more liquid) public market. Of the remaining “top-quartile” VCs that actually do perform, most can’t do it consistently across multiple funds. Yet we still view most VCs as pseudo-divine interpreters — powerful wizards who peer into their palantir to see the future, tell us what new companies or trends will disrupt existing incumbents, and write big checks to amazing founders who create the next Insanely Great startup.
Except most of the time this is just a big bunch of baloney, and they don’t.
For the few firms that by luck or skill get those predictions right, a strategy of very big bets on a very small # of companies can pay off handsomely. In fact, the more concentrated the portfolio, the better the returns will be for investors, assuming the portfolio still contains one or more big winners.
However in its most extreme form, this strategy devolves into betting all one’s money on a single turn of the roulette wheel, or buying a single ticket in a lottery. Surely winners of such games of chance should not be viewed as financial geniuses. Yet we still worship concentrated portfolio strategy as an industry best practice — when clearly, longitudinal performance of the venture capital asset class has yielded less than stellar results in the average case, and only consistent, frequent success for very few (~5–10%).
In the past five years of investing in over 1,000 companies at 500 Startups, we have found a few companies in our portfolios perform extremely well, but they occur very infrequently. Most of our investments (likely 50-80%) don’t ever get to any kind of exit, or return less than 1X invested capital. Perhaps 15–25% of portfolio companies succeed and result in a small exit of 2–5X. Another 5–10% might attain valuations of over $100M (which we call “centaurs”) and achieve exits of 10–20X. And if we’re lucky, 1–2% attain valuations of over $1B ( “unicorns”) and result in very large returns of 50X or more invested capital. In summary: most investments fail, a few work out ok, and a very tiny few succeed beyond our wildest dreams.
While these numbers might be unique to our own experience and process of investing at 500 Startups, most people in the industry would not disagree that large outcomes happen infrequently, or that a few big investments tend to dominate returns (re: Peter Thiel / power laws, etc). If this is true, then a more prudent VC investment strategy would be to construct portfolio size based on # of investments required to generate at least one big outcome (or ~3–5 large outcomes, to be on the statistical safe side).
Currently most larger VC funds ($200M+) doing Series A/B investments rarely invest in over 30–40 companies, and most micro-funds (<$100M) doing Seed & Series A investments rarely invest in over 50–75 companies.
We believe the current VC fund industry average portfolio construction is inherently & critically flawed, and undersized by a factor of 2–5X. We believe a more rational # of investments is ~50–100 companies for later-stage funds, and at least ~100–200 companies for early-stage funds.
Let’s presume that even for the average khaki-wearing VC — tall, smart, good-looking, went to all the right schools, and likely very white & male — that their portfolio distribution looks something like this
Looks pretty doable, right? With only 7% big wins, a VC fund could theoretically return almost 2.5X — hey, we should all become VCs!
But given the frequency of centaurs & unicorns (5% and 2% respectively in this model), let’s look at three portfolio sizes of 15, 30, and 100 companies.
