Portfolio construction is the process of creating your portfolio strategy, check sizes, follow on reserves, and expectations around valuations, ownership, and dilution over time. There is no single right way to approach portfolio construction. The “best” way to forecast a portfolio depends on your specific needs. More detail may sound appealing, but requires more assumptions and may not be necessary for your needs. Ultimately the question to ask is how you plan on using your model, what decisions you will make with it, and your ability to create, use, and manipulate the important decisions in your model.
Portfolio construction results from a series of trade offs
Portfolio construction at its core is making choices between trade offs:
- Should the fund concentrate or diversify? (e.g. invest or more companies or less companies?)
- What check size makes sense for your investment strategy and position as an investor?
- Reserve for follow-ons reduces number of companies to invest in, increases concentration (hopefully into winners)
- How large does an investment need to be for a large exit to return the fund size?
- Should the fund invest the same check sizes or vary check sizes based on the conviction behind their investment?
- What do you need to do to get access and invest in the best possible deals, and does that fit your check size?
The result of these trade-offs should be an investment strategy that allows you to build a portfolio that will give you a chance to invest in winners. It's important to invest in enough companies (30-100) to have the opportunity to invest in big winners, the primary source of returns in the power law world of venture capital.
Why? The highest performing funds are the ones with (1) the largest volume of large exits, and (2) the largest exits, on a multiple basis. Portfolios concentrated in a few companies may not provide enough opportunities for one or more of them to be a large exit, whereas portfolios that are diversified across a number of investments increase the probability that there will be enough exits for the fund to have one or more "fund return" type exits.
This highlights a few common issues with portfolio construction.
Mismatch between fund size, check size, and number of investments
Often a company will create a strategy for initial check sizes and reserves for follow-ons that does not align with the realities of the amount of capital the fund can investment. Outside of the simple mistakes in capital budgeting like overestimating the amount of capital that can be invested, often times funds will assume a check size that results in too few investments for proper portfolio diversification. A $10 mm fund that can invest $8 mm, as an example, will likely not want to invest $1 mm first checks into investments. A $20 mm fund that reserves 70% of their invested capital for follow-ons might struggle to invest enough companies and meaningfully deploy their reserves into the proratas from the check sizes. There is a math to budgeting for follow-ons that is based on being able to forecast (a) a cap table for a company based on the round sizes they would need to do if they grow (which will dictate your prorata strategy), and (b) the expected graduation rates for your investments (which will dictate the amount of potential follow-ons you can do based on the number of deals you want to do and the number of rounds you want to reserve capital for.) A misalignment with investment strategy and mathematical realities is the most common issue I see in portfolio construction.
Mismatch between check size and fund strategy
Charles Hudson's note that "your check size dictates your investment strategy" points that not only does your check size have to align with the math of one's fund size and portfolio construction, it also dictates how you invest. For example, a fund that targets $50k initial investments may struggle to get into Series B deals, and may need to write a large check size to be able to get into deals at that stage, but it may not make sense for the fund size. A fund targeting to be a lead investor writing $250k checks may struggle to win investment opportunities. A fund targeting $1.5mm checks in early-stage companies will have to have team, experience, processes, and ability to handle the responsibilities of a lead investor. The examples are numerous, but it's important for the check size to match the fund strategy.
Mismatch between target check size and target ownership
Eniac details this at Seed Fund Portfolio Construction for Dummies:
The classic mistake here is new managers pick an amount like 250K or 750K when they should be pegging initial checks to a target ownership. At the end of the day, the percentage you own of a portfolio company when it exits is what’s important, not the amount you put in. ... Part of the rationale for [our target] ownership range [of 10-15%] is that if we maintain ownership in the 10% range when the company is worth $1B, an exit at that time can return the whole fund. Every successful VC fund I’ve heard of has at least one company return at least the entire fund (remember the power law) so you want to make sure you’re well aware of what that will take.
This is related to the point about fund size, check size, and number of investments detailed above, but focuses on what the potential exits would be from an average investment. Once you account for dilution from additional rounds or the necessary reserves for proratas to maintain one's ownership percentage, what would the ownership percentage have to be at a potential exit for an exit to return the fund? The Venture Capital Method and my Venture Valution Tool can help you think through the math for this, and it is important to think through how a target check size and ownership percentage can impact the potential returns from exits.
How to model portfolio construction
Portfolio construction drives the logic used to budget for the deployment of capital (e.g. new, follow) and forecast proceeds from investments, both in terms of timing and how much. There are many different ways to approach modeling portfolio construction, and to explain them, I'll use examples from Foresight's venture capital models, ranging from simple methods to more detailed and complicated approaches:
- The Venture Capital Model, Overall Forecast takes the simplest approach, as it only forecasts the overall fund performance, without detailing the cash flows per time period, and by assuming an average gross multiple on invested capital.
- The Venture Capital Model, Annual Forecast is the base for the annual forecast free models. It creates a forecast of investments, proceeds, and distributions per year up to a twenty year period, and uses an average investment approach for modeling portfolio construction, and an average gross exit multiple to forecast proceeds. This model is also available in Causal.
- The Venture Studio Model uses the same core structure as the Venture Capital Model, Annual Forecast, but adds on a venture studio using the dual-entity fund plus studio structure. In this, you forecast the operations of the studio (operating expenses and revenues from management fees), the investments by the fund into the companies incubated by the studio, and the returns from the fund investments and the studio's ownership of the companies.
- The Venture Capital Model, Manual Portfolio offers a different way to build a portfolio by allowing you to input each specific investment and forecast the result of each specifically. In this, you input each check size, date, follow-on, and result (proceeds and date), and it aggregates each investment to create the total returns.
- The Venture Capital Model, Average Cap Table layers onto the average investment approach a more detailed portfolio construction approach that models follow-ons, increases in valuations, graduation rates, dilution, and proceeds per type of exit. This creates the rationale for how a fund returns a gross exit multiple, demonstrating the underlying logic behind stage entry points, ownership, and dilution.
- The Venture Capital Model, Quarterly Forecast expands on the average investment approach with a more detailed approach to defining the initial investment and follow-on strategy per type of exit outcome, calculating the overall capital allocation and investment performance for up to three different scenarios of portfolio construction or investment performance.
- The Venture Investor Model expands the timescale to create a quarterly forecast, and expands on the portfolio construction method to model expected graduation rates, follow-ons, proratas, increases in valuations, ownership and dilution, and proceeds per exit and per stage. This creates the detailed logic behind how an investment strategy generates returns, and analyzes the expected value per investment stage based on entry and exit valuations and exit rates per stage.
- The Venture Investor Model with Actuals Tracking is the same as the Venture Investor Model, and adds on the ability to track a portfolio using the same tracking structure as the Angel and Venture Fund Portfolio Tracking. This allows investors to update their forecast with actual results and analyze their investment pacing by comparing their budgeted capital deployment to their actual.
More detail is not always better
The ”right” level of detail varies by investor and their situation.
Are you building your first model to project fund returns, for your first prospective LP conversations for your first fund? A complicated forecast of how the average company creates a 4x gross is likely not the most important thing for those conversations, as that likely won’t answer an LPs biggest question about you as a GP.
But the deeper you go in the fund process, or the more information you need to create to convince LPs that they should invest in you, or raising Fund II or III, then it becomes more valuable to create detailed forecasts, and you’re likely in a better position to understand the complexity.
Scenarios are powerful ways to describe the variability in returns
Due to variability of returns in venture capital investments, creating scenarios and range-based inputs are very valuable for portfolio construction. Scenarios can be built to show variability of returns from key assumptions (primarily exit values of large exits and the portion of companies that achieve large exits).
Scenarios can be used to create discrete scenarios to reflect different point of views (e.g. best/worse/medium case), or could leverage simulation modeling to use range-based inputs to create outputs using probabilities and distribution curves.
Questions, contact me anytime.