Note: this is the first of four articles on best practices in early-stage investing.
Links to the rest of the series (forthcoming): Part II, Part III, and Part IV.
Introduction
Early-stage venture capital is one of the highest-yielding asset classes in existence.
According to findings from numerous studies, angel investors who assemble long-term portfolios of early-stage investments in growth companies consistently enjoy IRRs in the 22% to 27% range.
And thanks to the 2012 JOBS Act, startups are now permitted to sell shares to “retail investors” – in other words, ordinary individuals. Passage of the JOBS Act led to an equity crowdfunding boom. Scores of equity crowdfunding platforms have been created and countless crowdfunded deals are launched each year.
With this volume and diversity of investment opportunities available to small-scale investors, it is more important than ever to adopt best practices for early-stage investing. The outsized IRRs mentioned above are only realizable if smaller-scale investors pick deals and build portfolios thoughtfully, drawing on insights and lessons learned from seasoned angel investors and venture capitalists.
This article is the first in a four-part series that identifies 16 of the most common mistakes made by investors who are new to the unique world of early-stage investing.
Each piece in the series addresses a different dimension of the early-stage game: Conceptual Approaches (this article), Deal Flow Sourcing, Execution of Investments, and Portfolio Management.
Armed with the tips presented across this series of articles, even investors with very modest investment budgets can participate in the immense wealth creation currently underway in the world of early-stage growth companies.
Mistake 1 – Not Understanding the Power Law
If I had to pick just a single concept to impart to early-stage investing novices, a great choice would be Power-Law investing.
Power Laws are simply a specific class of statistical relationships in which a tiny number of inputs are responsible for not merely a majority, but an overwhelming majority of total output.
The conceptually tricky thing about Power Law distributions is that they produce outcomes that diverge wildly from those of more familiar statistical distributions.
As an example: the Heights of Adults in the U.S. displays the familiar bell-curve shape of a so-called Normal Distribution:
Interpretation of this statistical distribution is quite intuitive:
- The average adult in the U.S. is about 67 inches tall (so, very roughly speaking, call it five and a half feet).
- Most adults are between 60 inches (five feet) and 72 inches (six feet).
- There are zero adult humans who deviate from the average (in either direction) by more than 3x.
Contrast this with the counter-intuitive way that Power Law distributions operate. Power Laws are asymmetrical, ultra-skewed distributions that look nothing like bell curves. Instead, they are L-shaped, featuring an extreme peak on one end, and an equally extreme long tail on the other:
To appreciate how counterintuitive the outcomes are when a Power-Law relationship is in effect, imagine a world in which adult human heights still averaged about five and a half feet – but the variation around that average height followed a Power Law, rather than a Normal distribution:
The blue (Power Law) and red (Normal) distributions depicted above have the same average: roughly five and a half feet.
But in a Power Law world, most humans would be under 30 centimeters – i.e. less than one foot tall!
And even more remarkably, several humans would be taller than Mount Everest (so tall that the rightmost long tail of the blue distribution, as pictured above, can’t even fit into the available width of the image).
Obviously, human height doesn’t follow a Power Law – since there are no humans taller than Mount Everest.
However, some other natural phenomena do follow a Power Law. For example, urban populations are Power-Law distributed: New York isn’t just double or triple the size of the typical American town. It is hundreds of times more populous.
The absolutely critical thing for investors to understand is the distribution of returns within the universe of early-stage investment deals:
There is widespread recognition among seasoned early-stage investors that Power-Law dynamics are in effect. Let’s dig into what this implies for investment returns:
The leftmost bar of this graphic highlights the stark reality of investing in early-stage ventures: in around two-thirds of cases, the ROI is under 1x. In other words, on most investments, you’ll lose some or all of your money!
The dynamics of the power law imply that most investments in a typical early-stage portfolio will ultimately be worth zero – with just a modest number returning 1x, 2x, or 5x the capital invested. But crucially, an even tinier handful might return dozens, hundreds, or even thousands of times their initial investments. This handful generates so much ROI that they ensure strong performance, not just for that minuscule set of outsized winners but for the entire portfolio. Their huge gains completely dwarf the remainder of the portfolio, which proved mostly worthless.
Here’s another way to visualize the same phenomenon:
Notice how the black slice of the leftmost bar (just 6% of the deals done) eventually becomes a much bigger black slice in the rightmost bar (60% of the eventual returns).
So, as an early-stage investor, prepare yourself for the reality of Power-Law Investing. Expect that most of your investments will end up worthless. And that almost ALL of your investment returns will come from a tiny handful of deals that are major successes.
Mistake 2 – Investing in Too Few Deals
Since the Power-Law nature of early-stage investing implies that almost all your gains will come from the enormously outsized winners in your portfolio, it stands to reason that you must do whatever it takes to ensure that you participate in those winning deals.
But in practice, how can an investor ensure that their portfolio includes at least one or two outsized winners?
There are a couple of ways that one could do this. One approach would be simply to “pick better” – i.e. improve one’s ability to spot the very best deals, so that one’s portfolio contains nothing but companies destined for billion-dollar outcomes.
Unfortunately, this is not realistic. Remember, the two bar charts shown directly above depicted returns attained by VC firms. So, even professional venture capitalists who invest in early-stage ventures for a living see the majority of their bets partially or completely fail. As Fred Wilson of Union Square Ventures describes, even a successful early-stage VC fund might have around twice as many “misses” (returning 1x or less of the original capital) as “hits”.
So, that suggests a tactic of sheer volume. Invest in a large number of companies (think: dozens). This maximizes the chances of hitting at least one deal that delivers such massive ROI that it makes up for all of one’s other losses.
Or as Brad Feld, VC and co-founder of Techstars, puts it: be promiscuous.
Mistake 3 – Inconsistent Check Sizes
Investors tend to be optimists. And this holds doubly true for angel investors, given their focus on very early-stage speculative deals.
Although this optimism is warranted, it can also prove counterproductive for inexperienced investors who have not yet established a standard check size for each of their investments.
Excessive optimism becomes a problem when a novice investor gets overly excited by one of the first few deals they encounter in their investing journey. They may decide to deploy several times the check size that they would normally commit to a deal.
This can backfire in a big way. An oversized check that’s, say, 4x bigger than one’s usual commitment actually implies missing out on three additional companies that one could have otherwise backed.
And as we learned from the discussion of Power Laws above: anything that might lead to the ultimate VC sin of omission (missing out on what could have been that one massive winner) should be avoided at all costs.
Just imagine deciding to pass on a new deal because you’ve already exhausted your investing budget, due writing some larger-than-expected checks… And then it turns out that the deal you passed on ended up being Uber, which made its earliest investors anywhere from 2000x to 4000x their initial investments. (If you have any doubts about how much it stings to miss this kind of opportunity: read the lamentations of investors who had the Uber deal slip through their fingers.)
To avoid that fate, follow the rule of thumb suggested by celebrity angel (and seed investor in Uber) Jason Calacanis. Take one’s total budget for early-stage investments, and divide it by the generally accepted target portfolio size of around 30 deals. The resulting figure is your standard check size.
For example, a newbie investor who feels that they can justify allocating $60,000 of their personal wealth (over a multi-year period) to early-stage investing should aim to write a standard check size of $2000 into each of 30 companies.
And then don’t deviate from that check size, no matter how excited you get about Deal #2 or #11 or #27. After all, the most important thing is to make an ample number of bets. Maximize the chance of hitting that one huge winner that more than compensates for all the other failures.
Mistake 4 – Focusing on Probability of Success
At first glance, it seems like it would be hard to fault an angel investor for picking deals based on their sense of which ventures appear most likely to succeed. After all, the whole point of venture capital is to invest in successful companies – right?
Strangely, it turns out that focusing on a company’s odds of succeeding tends to be a lousy framework for picking early-stage investments. To see why, let’s revisit a chart that we saw earlier:
The flaw in the strategy of focusing on a startup’s chance of success is that this framework is generally only effective at shifting one’s portfolio deals from the leftmost bar in the chart (Total Misses) to the bar on its immediate right (Slight Winners).
But as we know, the Power Law dictates that almost ALL of one’s returns will come from massive winners that return 10x, 50x, 100x or better. To find these outliers (the FAR right column in the graph), something besides assessment of a startup’s mere likelihood of success is needed.
A better approach involves re-wiring one’s brain. The key is to move away from traditional investment thinking (“must find decent, reasonably safe deals”) to favor Power-Law thinking (“must take risks on ambitious deals that COULD generate spectacularly large returns”).
Sam Altman, recently the president of prestigious accelerator Y Combinator, advises investors to avoid focusing on a given venture’s chance of working – and instead ask themselves, “How big could this venture become, IF it works?”
Peter Thiel, co-founder of PayPal, Palantir, and Founders Fund, has similar advice in his book Zero to One. Thiel explains that the Power Law implies two “very strange rules” for early-stage investing:
“First, only invest in companies that have the potential to return the value of the entire fund… This leads to Rule Number Two: because Rule Number One is so restrictive, there can’t be any other rules.”
Chris Dixon, VC at Andreessen Horowitz, describes the Babe Ruth effect. This refers to legendary baseball player Babe Ruth’s all-or-nothing hitting style, which at one point landed him the moniker “King of Strikeouts,” but also earned him the record for most home runs (a record that has lasted almost a century). Dixon points out that the best-performing VC funds have a similar trait: their investments actually lose money more often than the investments of other funds – but when their investments succeed, they do so in a big way.
All three of these very successful investors have arrived at the same conclusion. What matters in early-stage investing is not a venture’s chance of success. Rather, it is the potential magnitude of the outcome, if it does happen to succeed.
Conclusion
Armed with just the insights from the above discussion of conceptual approaches to early-stage investing (namely: the importance of understanding Power Law dynamics, going for plenty of deals, adopting standard check sizes, and focusing on outcome magnitudes), even a new investor will have a fighting chance of building a portfolio that can generate superior ROI.
Stay tuned for Part II where we’ll review another set of common mistakes. This time, drawn from the subsequent dimension of the early-stage investment process: Sourcing Deals.
Note: this is the first of four articles on common mistakes in early-stage investing.
Links to the rest of the series: Part II, Part III, and Part IV.
