In angel investing, it’s the extreme distribution of payoffs that keeps things interesting.
If anything, it resembles buying a deep out-of-the-money call option, but with nonlinearity. If you win big you might find yourself in on the ground floor of the next Google or Facebook. That’s incredibly unlikely, but still possible.
More likely, you’ll end up with a solid 2x-5x return from a startup that grows into a viable long-term business. But most likely of all–by a long shot–you’ll lose your entire investment in another failed startup.
The most successful angels invest in a long series of deals over many years. They know that any one startup in isolation is a gamble, and to eventually hit a big return, an investor needs to draw repeatedly from the payoff distribution.
How many deals?
A discussion with Gabriel Weinberg on this topic piqued my curiosity about the relationship between the number of deals an angel invests in, and the shape of the payoff he or she can expect from that specific number of deals.
It’s clear that a single investment would have a terrible expectation and huge variance, but how about five deals? 20? 100?
How many angel investments are needed to make the combined payoff look attractive from an investment standpoint?
Monte Carlo simulation of angel investing
I coded the following simulation in Python.
1. Create a pool of 10,000 different investors, each investing in D deals, with>>> READ MORE at: