This site is intended for people in the early-stage venture capital industry to build simple portfolio models to better understand and talk about "venture math."
You can build models of the markets you are investing into by stating your assumptions around what valuations you will be be investing at, what your expectations of follow-on pricing will, how big the exits will be, and how common or rare certain kinds of exits will be.
Once you have decided what the market looks like, you can test different portfolio strategies. Will a bigger portfolio perform better? Will more or less reserve capital give you better returns on average? How will different strategies affect your chances of a 5x fund, or a 10x fund?
Of course, this is just a mathematical model. Good returns in venture will always come down to investing in great companies. But understanding "venture math" can help emerging managers avoid certain kinds of mistakes in allocating capital. They can also help GPs and LPs have an explicit discussion around the fund strategy.
This tool simplifies your choices when constructing a portolio. For example, all initial investments will be of the same amount. All follow-on investments will also be of the same amount, your reserve divided by the number of follow-on investments. You cannot have multiple strategies in the same fund, e.g. 25% of a fund dedicated to pre-seed and 75% dedicated to seed.
This tool also ignores things like management fees, and focuses on TVPI vs IRR to keep it simple.
The tool currently only supports an initial investment and one follow-on inestment. It does not allow you to invest more than twice in the same company.
There is no "righ answer" to what the best fund strategy is, so you will see a lot of data when you run a model. For example, limited partners may be interested in funds that have the highest probability of being 10x+, which will generally involve higher downside risk, and other limited partners may be more interested in the highest probability of achiving a 3x return, which will generally result in less upside potential.
The tool errs on the side of giving you as many data points as possible to look at the strategy and assumes you can figure out what's important to you.
This site is intended to help you visualize a venture portfolio model probabilistically.
You should start by choosing or creating a set of market assumptions. This is the outcome distribution of investments within the “market.” You could choose to model an overall market, based for example on public data, or you could choose to pick a different set of assumptions.
Because no two investors/teams will see the same deals and have the same judgement—which is implied by an outcome distribution of “their market” there is not per se a correct set of data here. The right way to think about it is that each investor is making their assumptions about the opportunity set explicit so that they can test different strategies (models) against it, and so that they can see the tradeoffs in different strategies.
Once you have identified a market, you can create a model. This simulator is relatively simplistic and lets you control a few variables.
Fund Size - Pretty self explanatory.
Portfolio Size - same.
Reserve - The amount of the fund reserved for follow-on. Initial investments will be split evenly across each company. Reserve will be split evenly amongst companies in which the fund makes a second investment. The model only currently supports writing two checks.
Follow-on Rate - The percentage of companies the fund writes a second check into. The smaller this number the more larger the follow-on checks will be.
Follow-on Ability - This is the single way of attempting to model investor skill. It can be set to random (you are no better at follow-on than initial picking), good (you are better, but not perfect), and Perfect (your follow-ons are in the top X companies).
You can “fork” a model, meaning copy it and modify variables to see if you can improve it. Before you do, explore the charts below to see how changing variables like portfolio size affect results.
Once a model simulation has been run, you can see a number of interesting pieces of data about it.
Monte Carlo - A standard visualization of portfolio strategies, this shows you the distribution of outcomes you might expect if you were to deploy 1000 funds with this strategy with the market assumptions you chose.
TVPI - A standard measure of returns (total value to paid-in) is the cash-on-cash returns expressed as a multiple of the fund size. For early-stage venture, 3 is typically considered to be a very good TVPI.
Company Ownership - You cannot set ownership targets directly, but you can infer them from market conditions and portfolio size. This app will allow you to write checks of any size. In practice, there will be a lower and upper bound in the market. Do reasonable things.
Median and Top Quartile Funds - These are example portfolios selected from the set of simulations. They are intended to provide concrete examples of what a good and ok portfolio might look like given the strategy.
There is a lot this app doesn't do that it could. In the future it will add the ability to do things like set minimum invesment thresholds (some large portfolios might be unrealistic), maximum ownership available per round, pro rata thresholds, invesment and exit timing to calculae IRRs, the concept of "winability" (implicitly represented by markets today), and moar data.
Feel free to send request