Dave McClure recently sent out this tweetstorm, arguing in favor of 2x preferences on notes for early stage investments. Dave’s passion made me wonder if it was an emotional concern or if it actually moved the needle. I asked him, and he responded stating that “if I have a few unicorns, it prob doesn’t matter. if I don’t, then it matters more.” That was in line with my intuition, but I was interested in a more precise answer. Unfortunately, I don’t have access to a broad database of venture investments and exits, so I sought out base rate information on VC investment results. I decided to use the following data from Seth Levine’s article on venture outcomes.
Distribution of US Venture Returns
Given that data, and the knowledge that the actual results likely follow a power law distribution I sought a mathematically valid way to simulate underlying results. After discussing with, Austin Rochford I decided to use a basic probability integral transformation approach. This involves selecting buckets using base rate probabilities, then treating outcomes within buckets as though they're distributed uniformly. I simulated two scenarios with this approach, one with 2x prefs, and one without.
Net result? They help the VC a tiny little bit. The following violin plot shows the distribution of gross realized multiples for funds in both the base case and the case with 2x preferences for early exits. The performance of the bottom decile of funds is improved by around 10%, but the fund's LPs aren’t going to be throwing any parades. The clause improves upper decile by around 3%. It's a visible difference, but it doesn't turn poor results into Shinola.
I would have preferred a dataset that included timing information, so I could make a realistic attempt at calculating then effect on other measures of portfolio performance, such as IRR, instead of only having only timing-free gross realized multiples. That said, it was still interesting, and a good excuse to learn how to use Seaborn.