Angel Investing Strategies, Part I: Portfolio Construction

Introduction

Conventional venture capital wisdom suggests unicorns are rare, and in order to invest in these unicorns, one would mathematically need to invest in a large number of companies (500 Startups anyone?) in order to consistently find these unicorns and deliver superior investing returns.

If it were so simple, then there should not be such a wide divergence of performance among venture capital firms:

Truth be told, I’ve never felt very comfortable with the “large portfolio theory” of venture capital.

On the surface, it seems to suggest that the simple act of investing into a large number of early stage companies is the path to great investing returns.

In reality, we find that the truth is more nuanced than that.

So why do we think angel investors should carefully reconsider the conventional advice around portfolio construction?

Non-Ergodicity in Venture Capital

If you’re not familiar with this term, which I certainly wasn’t when I was researching for this article, here’s how Wikipedia defines Ergodicity:

Here's an explanation and example in simple terms from Chatgpt:

In short, non-ergodicity is basically “past performance is not indicative of future results” taken to the extreme, since the performances of those that came before us have no bearing on how we’ll perform as a new investor even if we follow their same strategy. This in part explains the large spread in performance for venture capital firms.

Fundamentally, venture capital, and really investing in general, is non-ergodic, and blindly following advice in investing in many companies, and expecting the law of large numbers to protect you might not always work.

Risk of Ruin:

Did you notice a particular sentence in the explanation for non-ergodicity above?

“If the game has a high risk of losing everything at some point, playing it many times could mean you go bankrupt, even if the average outcome for the group looks profitable.”

This sentence not only is about non-ergodicity, but also relates heavily to concept of risk management and avoiding getting washed out of the game.

For a visual understanding of what we mean, we recently found a Monte Carlo simulation tool geared towards public market investing, but I found quite useful for modeling angel investing on AngelList Syndicate.

For those interested in playing around by yourself, follow the link here and download the "trading simulator” spreadsheet. Two notes and screenshots from exploring the spreadsheet:

• If we’re on the hunt for deals that return 100X (winning trade = $100,000, losing trade = -$1000) at an estimated 1% rate, returns over time look extremely choppy due to the low amount of winners, and any portfolio has to endure long periods of losses.

  

• If we adjust the expected outcomes to be a more modest 10X (winning trade = $10,000, losing trade = -$1000, 10% win rate), the profit/loss curve smooths out a bit more, but we still have to contend with the fact that most equity low points are a negative number, unless you managed to find a winner early on in your investment journey like the example below when you hit a 10X on your 2nd investment:

  

In particular, Look at the “loss streak” column in both images. If you attempted to “large portfolio” your way out of the wide performance variance in venture capital, you might lose 30, 50, or even 100 hands in a row before you hit one winner!

Remember, you can’t play the game of venture investing if you run out of money before you hit the winner.

Extreme outliers muddy VC returns analysis

Venture Capital, especially at the preseed/seed stage where we tend to invest in, is oft described as a game of finding outliers. However, the winners are so few and so great (ex: 5000X return for Uber’s earliest investors) that the outlier itself completely skews the way a large portfolio can be practically implemented.

In fact, you’ll need a substantially larger portfolio to improve your performance in a way that is practicality impossible to achieve (See this article for a more in-depth analysis). To understand how impossibly skewed the data ends up being, here’s a summary from the article of the portfolio size it takes to improve your odds of achieving or exceeding a certain X return:

Power Law indeed from a portfolio construction perspective!

Conclusion

To be fair, the advice to invest in many early stage companies is well intentioned. After all, more at bats equals more chances at success, which is why baseball teams always put their strongest hitters at the top of the lineup for that extra at bat towards the end of the game. There is also an aspect of diversification that we’ll partially explore in a later part of the series.

However, it’s important to remember that because of the power law nature of venture capital returns, the terms “in aggregate” or “on average” makes sense theoretically, but actually isn’t particularly useful for the angel investor looking to plan his or her investing journey. Investors would do well to consider the implications of some of the concepts explored and develop their own interpretation of the ideal portfolio construction.

What are some other misconceptions in venture investing angel investors should be aware of? Join us next week as we continue exploring strategies and concepts to help investors enhance their early stage investing experience.

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