Angel Investing By The Numbers

With high-tech companies needing less capital due to advancements in technology, startup development methodology and online marketing, we have seen a Renaissance in angel investing. While angel investors participate in part for the excitement of engaging with entrepreneurs and placing bets on the future, they also do it for the expectation of significant financial returns. Various studies of angel investing published in the last decade estimate aggregate returns to angels on the order of 18-37% per year, well above market. The catch is that 50-70% of angels make less than what they invest. Returns are very unevenly distributed and this begs the question to what extent is portfolio theory fundamental to angel returns.

The best data set with detailed investment & exit information comes from the Angel Investor Performance Project by the Kauffman Foundation. The data was collected by surveying angels who belong to angel groups. Cleaning the data and restricting to the domain I was interested in—first round investments in early-stage high-tech companies—yielded a data set about the returns of 56 angels with exits from 112 companies. The data show the type of skewed distribution one would expect from early stage investing:

  • 75% of exits happened between 2001 and 2006. There is some reason to believe that the data may have a slight bias towards negative returns as 50% of investments happened between 1995 and 2000. Angels may have been buying high and selling low.
  • 3.2x cash-on-cash return for all investments put together (total dollars out divided by total dollars in). However, returns are extremely sensitive to big hits. A lucky angel put $600K in a software company in three rounds from 1988 to 1994. In 1996 the company went public and the person got a nice 55x return. Removing this one company from the sample drops the aggregate cash-on-cash return for all angels nearly in half to 1.8x.
  • Of the companies angels invested in, 63% were complete write-offs for the angels involved.
  • 66% of angels made less than what they invested. 45% generated no return. The remaining 21% of angels received only 4% of the total returns (7% if you exclude the 55xer).
  • 6% of the angels generated returns >10x that accounted for 68% of the total return (42% w/o the 55xer). The cash-on-cash return for that group was 36x with and 21x without the one big hit, in both cases more than ten times the average for all angels put together.
  • The data includes only one super angel who had 29 exits generating 2x return. Most other angels had one or two exits and only a handful had three or four.
  • Due to missing or overly granular investment and exit dates, it is practically impossible to calculate meaningful IRR numbers or to calculate returns in excess of financial markets.

The analysis suggests that angel investing as a whole can be quite profitable but, when dabbled into a deal or two at a time, it is more akin to gambling.

Without accurate data about angel investment portfolios, the next best option is to do Monte Carlo simulations of synthetic portfolios where thousands of hypothetical angels invest in thousands of hypothetical companies. The hardest part in setting up Monte Carlo studies is making good assumptions as they can pre-determine outcomes. Some have approached the problem by guessing probabilities of certain outcomes much in the same way VCs do basic portfolio presentations for LPs but with a bit more math in the mix. Rather than guessing, I chose to reverse-engineer a distribution of returns based on the data from the 112 companies. For the math-inclined amongst you, this involved piecing together a cumulative density function from three separate pieces: 60% chance of zero return, a logarithmic non-linear model for 0-10x returns and a combination power/exponential non-linear model for the long-tail of exits greater than 10x where not much data was available.

I ran a very simple Monte Carlo simulation evaluating the portfolios—ranging from 5 to more than 100 companies—of hypothetical angels. The average cash-on-cash return was right around 3.2x, exactly as with the Kauffman data, which is a good sanity check. Average returns don’t vary with portfolio size, which is to be expected.

Median returns vary substantially with portfolio size. Going from 5 investments to 10 investments increases median return by 68%, from right around 1x to nearly 1.7x. There are diminishing returns to growing portfolio size. Going from 10 to 15 increases median returns by another 40%. Doubling portfolio size from 15 to 30 adds another 50% but then in takes going all the way to a whopping 125 company portfolio to triple median returns compared to the 5 company portfolio. Similar conclusions apply with respect to other metrics. The probability of getting a return that’s greater than 2x doubles (from 34% to 69%) as one moves from a five company portfolio to a 50 company portfolio.

The data unequivocally suggest that playing like a super angel or an active seed fund as opposed to dabbling with the occasional angel investment is a key strategy to consider if financial returns are important. The data also call into question the behavior of some angel groups that do just a few investments per year.

This is not to say that volume investing—like throwing darts to pick stocks—should replace doing due diligence and the thoughtful development of investment theses. In fact, every Monte Carlo simulation of angel or venture investing I’ve seen, including mine, doesn’t take into account the various types of signaling that go on between entrepreneurs and investors and between investors themselves. For example, great entrepreneurs usually have over-subscribed investment rounds. A pure volume-oriented investor would find it difficult to compete for and win these hot deals, especially in a world where seed funds keep popping out like mushrooms after rain.

If you want to know more, let me know. I’m always curious to hear your thoughts. You can find me at @simeons or at FastIgnite.

Simeon Simeonov is founder and CEO of FastIgnite where he invests and helps entrepreneurs build great companies. Sim is also executive-in-residence at General Catalyst Partners and co-founder of Better Advertising and Thing Labs. Prior to that, he was a VC at Polaris Venture Partners and chief architect at Allaire/Macromedia (now Adobe). Sim blogs at blog.simeonov.com, tweets as @simeons and lives in the Greater Boston area with his wife, son and an adopted dog named Tye.

Related Posts

17 Comments

  • Nice model. Glad to see you’re using continuous distributions.

    Thought you might like to know that we’re actually trying out this approach of diversified angel investing. See http://www.rightsidecapital.com. We’ve got an internal Monte Carlo model that we’ve calibrated with a variety of data sources, including the AIPP dataset.

    I walked through a toy version on my blog at http://emergentfool.com/2010/05/11/simulating-angel-investment-kevins-remix/

    Would love to compare notes on this.

  • [...] Culling data from the Kauffman Foundation’s Angel Investor Performance Project, peHUB sees grim returns for angel investors. With a focus on first round investments in early-stage high tech companies the post reviews 56 [...]

  • [...] Culling data from the Kauffman Foundation’s Angel Investor Performance Project, peHUB sees grim returns for angel investors. With a focus on first round investments in early-stage high tech companies the post reviews 56 [...]

  • [...] Culling data from the Kauffman Foundation’s Angel Investor Performance Project, peHUB sees grim returns for angel investors. With a focus on first round investments in early-stage high tech companies the post reviews 56 [...]

  • [...] Culling data from the Kauffman Foundation’s Angel Investor Performance Project, peHUB sees grim returns for angel investors. With a focus on first round investments in early-stage high tech companies the post reviews 56 [...]

  • Kevin, I’d be happy to talk. Shoot me an email or tweet me at @simeons.

  • Honestly, one of most cogent commentaries on angel investing that I’ve seen over the last few years. And also one of the few that’s attempted to infer something from some of the sparse hard data that is available.

    My sense at the level of practice from the vantage point of the adjacent field of mid-stage venture investing is that too much of angel investing in the post-Dotcom era has really been little more than a glorified kind of speculation, frankly more gambling than investing. But it doesn’t have to be that way. Building a rational basis for seed stage venture capital, where failure rates among individual investments can be so high, clearly involves exploring some of the portfolio theory implications based on historical data. Which is precisely why this post’s take is so refreshing, and I think valuable. A heatlthy innovation eco-

    An enjoyable and thought-provokign post!

    -Jim

  • Honestly, one of most cogent commentaries on angel investing that I’ve seen over the last few years. And also one of the few that’s attempted to infer something from some of the sparse hard data that is available.

    My sense at the level of practice from the vantage point of the adjacent field of mid-stage venture investing is that too much of angel investing in the post-Dotcom era has really been little more than a glorified kind of speculation, frankly more gambling than investing. But it doesn’t have to be that way. Building a rational basis for seed stage venture capital, where failure rates among individual investments can be so high, clearly involves exploring some of the portfolio theory implications based on historical data. Which is precisely why this post’s take is so refreshing, and I think valuable.

    An enjoyable and thought-provokign post!

    -Jim

  • You have presented a great scenario

  • very good \o/

  • [...] in conducting due diligence, coaching their companies, building dealflow, etc.  However, the best research to date shows that we have no strong evidence [...]

  • Good information as per usual, thanks. I certainly hope this sort of thing gets more attention.

  • [...] that an uncomfortable amount of return would be up to chance. Like Brad Feld here and Sim Simeonov here and here say, it pays to be promiscuous by making lots of bets. (It should be noted that over [...]

  • [...] topic.  Sim Simeonov did a rigorous Monte Carlo simulation using the best data he could find, and his results showed [...]

  • [...] The latter is the biggest style item, as it goes to the heart of a manager’s philosophy. There is a strong case to be made for index-style investing, which in startup land is often derided as “spray and pray”. While normally index investing is meant to give rock-bottom costs on efficient markets such as large cap  domestic equities where it is really difficult to justify active management, in inefficient markets like startups, the argument is two-fold: when one is investing so early, especially when before product-market fit is found, it’s a numbers game: it can be hard to judge between the 1/3rd of companies you see, and the hope of landing that 100x or even 1000x return company justifies a portfolio with literally hundreds of companies. (Revisit Sim Simeonov’s analysis on diversification.) [...]

  • [...] the typical early-stage portfolio. As a result, various analyses have shown annualized returns of 18-37%, 27%, and [...]

  • [...] As a result, various analyses have shown annualized median returns for the industry of 18-37%, 27%, [...]

Leave a Reply

PEHUB Community

Join the 12494 members of peHUB to make connections, share your opinion, and follow your favorite authors.

Join the Community

Psst! Got any hot tips?

  • This field is for validation purposes and should be left unchanged.

Look Who’s Tweeting

Reuters VC and PE feed

RSS Feed Widget

Groups