The Data That Built Our Fund’s Investment Thesis
3 key pillars we used to build our early-stage VC investment strategy
At Connetic, we use data for everything. We use data to evaluate companies, build our asset allocation model, and create our fund investment thesis. In this post, I wanted to elaborate on the specific data points that built our investment thesis for our current fund, Connetic Ventures Fund.
The three main pillars of our investment thesis are:
Diversification — As the number of investments grow, the likelihood of generating positive returns increases
Value — In underserved markets, you can get 1.5 to 2x more share for your money without sacrificing the potential for outsized returns
Follow-On — Follow-on investments typically outperform initial investments
Diversification is probably one of the most discussed themes across every financial sector. It has been proven time and time again that diversification is the best way to reduce risk in a portfolio while still maintaining high levels of upside, so why would Venture Capital be any different.
Correlation Ventures did a very famous Monte Carlo simulation of expected portfolio returns that has been replicated throughout the years. We ran our own across 10k+ expected outcomes and came up with the following data.
As with the original work, we discovered that as the number of investments increase, the odds of making money also increases. We only modeled up to 100 investments, but the numbers continue to climb, albeit at a much slower rate. The largest percent differences occur between funds with 5 to 100 investments. Also, it seems that ~200–250 is the maximum number of investments before there is no additional benefit of diversification.
Before diving into the data further, let us set a baseline for what “good” looks like for VC returns. Historically, the stock market has delivered between 6–8% returns annually. Given venture is a slightly riskier asset class, we will consider 12% “good”. Generally, the lifecycle of a fund is 10 years, so to generate 12% annualized returns over 10 years you need to have a 3.1X fund return (we’ll just call this 3x and also ignore net/gross returns for simplicity).
Now that we’ve defined what “good” is, we can get back to the diversification data. To make this simple, let’s compare funds with 10 investments vs. funds with 100 investments. The likelihood of a good return (3x) with 100 investments is 52%. This figure is in contrast with a 32% probability of good returns when a fund only has 10 investments. You have nearly double the chance of a 3x return when you invest in a more diversified portfolio (100) vs a concentrated portfolio (10).
By investing in 100 companies, not only do you have a much higher likelihood of a good return to investors, but you also prevent nearly all downside risk. You have a 1% chance of losing money, while a portfolio of 10 companies has a 32% chance of losing money.
If you were an investor would you rather have?
- 52% chance of a good return and a 1% chance of losing money
- 32% chance of a good return and a 32% chance of losing money
That is obviously a rhetorical question, nearly every investor would choose a fund invested in 100 portfolio companies. The only downside of a diversified portfolio is the slightly decreased probability of a 5x or greater return. However, the odds of generating this return is very low to begin with, so we do not view that as a significant sacrifice at Connetic. There are investors that would like to roll the dice and invest in VC for the sole purpose of trying to generate a significantly outsized return, so if this is your primary goal then a concentrated portfolio is likely the better choice.
So, as a VC if you are building a model to generate outsized returns over the long-run diversification is the only way to go.
Pros: Significantly minimize risk while still maintaining upside potential. Much higher likelihood of predictable returns.
Cons: Lower potential of very large outsized returns. VC is generally labor-intensive and not scalable, it may be hard for most funds to find and evaluate this many opportunities.
At Connetic, we view the term value as getting the same quality of investment opportunity at a lower price. In VC, value can generally be found in underserved markets. Our definition of underserved markets are markets or regions where there is a much lower concentration of VC capital. As a result, the lack of VC funding in these markets creates lower demand which translates into lower startup valuations.
But looking through our database of more than 1,500 startups that have applied for funding, we found that the average pre-seed valuation for San Francisco, Boston, and New York are all well above the pre-seed valuation for the rest of the US. We are classifying pre-seed companies as companies that are generally building their MVP and currently have between $0–6k in Monthly Recurring Revenue (MRR).
As you can see, if you invest anywhere outside of the 3 main startups hubs, you are paying somewhere between 1/3 and ½ the price and getting between 1.5x and 2x the number of shares in the company for the same price. More shares = more value.
An argument we often hear is that you are getting higher quality teams and companies in larger cities and thus the higher valuations are justified. To test this hypothesis, we conducted an analysis using Pitchbook that analyzed the cumulative returns of all top-performing exits in the last 5 years.
We defined a top-performing exit as a company that has exited for more than $500M and returned a minimum of 5x return on invested capital. So, for a company that raised $200M they needed at least a $1B exit to hit that 5x threshold. Over the last 5 years, there were 112 companies in the US that fit these criteria.
*Pitchbook — Location: United States; Search HQ only; Exit Types: Public Investments; Acquisitions; Include PE-Backed Exit; Include VC-Backed Exit; Exit Date: Last 1825 Days; Exit Amount: Min: 500M; , Capital Raised to Date: $100M
In terms of total cash invested in these 112 companies, more than 2/3 of the capital was deployed in California, New York, or Massachusetts but they only saw roughly 1/3 of the top-performing exits. 2/3 of the top-performing exits occurred in states other than the “big 3”.
This data would suggest that startups in CA, NY, and MA take on much more capital than those in other states and have a harder time delivering comparative returns to investors. Based on my experience, startups on the coast raise significant capital with the intention of growing revenue at all costs, generally ignoring the idea of profitability altogether. Contrastingly, middle America takes on less capital and tries to grow at a moderate pace while trying to achieve profitability. The difference is night and day, you might as well be investing in two different worlds.
So, not only are you paying more for startups in CA, NY, and MA, but you are seeing much less return on the companies that end up making it big. After all, home runs are the name of the game in VC.
One thing to note is that valuations do ebb and flow with the larger macro climate, so valuation metrics are a bit of a moving target at times. For instance, since Covid-19 hit, we have seen valuations have dropped roughly 25–30% from where they were previously. These macro events tend to impact prices the same across all markets, so it does not really impact our thesis of finding better value in startups.
Pros: Get nearly 2x the number of shares for your money while seeing higher return potential.
Cons: Hard to source companies and execute at scale due to dispersion of companies. Either need technology or a lot of human capital.
Our last key pillar to our investment thesis is follow-on funding which is also frequently described as overweighting investment in your winners. Follow-on is generally the second investment that you make into a company. Follow-on investments for Connetic typically happened within 8–12 months after our initial investment (8–12 months is the average time between funding rounds).
Unlike the other topics, follow-on funding doesn’t get as much press and isn’t discussed as much in publications or journals. VC funds vary greatly on follow-on strategy and the amount of follow-on investing they do, so it is hard to get empirical data on returns of follow-on funding. VC investment data (outside of AUM and # of investments) is also sparse and rarely accurate which makes analyzing this topic even more difficult.
For Connetic, we are only 1.5 years into our first institutional fund, so we don’t have great data on the follow-on subject either, but I will discuss some of the data that we’ve come across while building our follow-on thesis. It’s worth noting that we have made roughly 10–12 follow-on investments dating back to our angel investments and those companies have had a higher exit rate than those we chose not to write a second check.
The first is a study that was done by an early-stage investment marketplace named Fundify. In their analysis they looked at company returns by length of time spent analyzing the company (due diligence) and they discovered that the longer time you spend in due diligence, the better the return of investment.
Personally, I find the return figures of 40+ hours (7.1x) to be a bit unbelievable, but I do agree with the general trend. The more you dive into a company, their financials, and the team, the better the odds of you making a return on your investment. For Connetic, if we spend 8–12 months with a company prior to writing a larger, more significant check — we believe that this will be just as good as spending 40+ hours of initial due diligence.
The other data we have explored around the subject of follow-on is looking at the performance of other funds that tout follow-on as an important part of their investing strategy. One fund that highlights follow-on predominately in their investing thesis is Union Square Ventures, a very successful and prominent figure in VC investing.
When looking at performance data in Pitchbook, Union Square Ventures (USV) has one of the top-performing funds of all time, posting a return nearly 4x better than the next closest fund (USV a projected IRR of 61%). It is worth noting, funds had to report data to Pitchbook to be included in this, which is very hit or miss in the venture space.
Pitchbook: Top performing Venture Funds that have reported data
Moreover, I additionally reviewed research done by Toptal on USV. They analyzed follow-on trends through CB Insights which found that the majority of USV investments were classified as follow-ons.
Without even looking at data, follow-on investments just make sense. You get to see behind the scenes for a year or more and collect large amounts of quantitative and qualitative data before deciding to write an additional check. The best analogy I have heard is follow-on is like doubling down in blackjack when you have an 11. If you look at the actual probabilities from Blackjack, regardless of what the dealer has you have a 49–64% chance of winning, higher than the average odds of winning a blackjack hand (48%).
If follow-on checks can move the needle slightly higher on probably of generating a positive return (VC average is 35%) then we consider this a win.
Pros: Increase likelhood of higher returns as you get to know the company and collect more data prior to writing a more substantial check.
Cons: Takes longer to deploy all fund capital and may be tough to invest desired capital in a “hot” deal.
So, there you have it. We built the investment thesis of our early-stage venture fund on 3 key principles: diversification, value, and follow-on.
Do you agree? Is there data we’re missing? We’d love to hear from you in comments or directly at email@example.com