Archive for the ‘Technology’ Category
Yes, it’s time for another edition of “Seed Bubble Watch”. The subtitle for this round is, “Still Waiting to Inflate.”
The first chart shows a slight dip back towards 2009 and 2010 levels. However, I think this is an artifact of seasonality in angel investing. Comparing 1H2012 to 1H2011 for the angel data, there’s actually an increase of 7% from a $6.9B annual rate to a $7.4B annual rate. It’s just that 2H2011 had a $12.1B annual rate. I know of nothing specific that happened in the second half of last year to account for a spike, so my guess is that angel activity is just generally higher July through December. My prediction is that when the full year 2012 numbers come out in April 2013, we’ll see 2012 angel investing at slightly over $10B.
Reported VC seed stage investing continues to drop (with my standard caveat that what VC’s call “seed” is probably different from angels). But it was made up for by increases in the size of super angel funds. The obvious explanation is that super angels are taking over part of the VC seed function. I think there’s more to it than that, but substitution and specialization are certainly part of the story.
Recall that I aggressively assume super angels deploy all their dollars in one year, to make my synthesized investment metric maximally sensitive to detecting bubbles. It looks like this assumption may not be too far out of line with reality. Aydin Senkut’s Felicis Ventures is raising a $71M fund after a $40M fund in 2010. Mike Maples’ Floodgate is raising $75M after $70M in 2010. Lerer Ventures is raising $30M after $25M in 2011. Ron Conway’s SV Angel is raising $40M after $28M in 2011. Chris Sacca’s Lowercase Capital is raising $65M after $28M in 2010. And finally, our big winner is Thrive Capital, raising $150M ($147M of which was already in the door as of 9/6/2012) after $40M in 2011. Given IA Ventures‘ big raise noted in our last installment, we’ve got two NYC new media funds blowing up. And it seems like a lot of the leading funds are on a 1-2 year cycle.
I think this data supports my previous assertions that there is no general seed bubble in the US. However, it certainly seems plausible that Silicon Valley and NYC could have local bubbles given the increasing dollars going into super angel funds in those areas and anecdotal reports of high valuations. But my ad hoc conversations with people at the super angels indicate they are investing in a wide variety of locations. It’s possible that, at the margin, angels and super angels are choosing to invest outside of Silicon Valley and NYC often enough that we’ve reached an equilibrium.
For the rest of 2012 and all of 2013, I think the big driver will be the overall economy. GDP growth was only 1.3% in the 2nd quarter and 2.0% in the 1st quarter. The 20-year average is 2.5%. So I don’t expect a big increase in seed funding until we at least get above that rate.
One of the questions I most frequently answer about RSCM is how we value seed stage startups. Apparently, being not only willing, but eager to set equity valuations sets us apart from the vast majority of investors. It’s also the aspect of our approach that I’m most proud of intellectually. Developing the rest of our process was mostly a matter of basic data analysis and applying existing research. But the core of our valuation system rests on a real (though modest) insight.
We’ve finally accumulated enough real-world experience with our valuation approach that I feel comfortable publicly discussing it. Now, I’m not going to give out the formula. Partly, this is to preserve some semblance of unique competitive advantage. But it’s also for practical reasons:
- Our precise formula is tuned for our specific investment theses, which are based on our larger analysis of exit markets, technology dynamics, and diversification requirements.
- The current version of the formula doesn’t communicate just how adaptable the fundamental concept is (and we do in fact adjust it as we learn).
- There’s a lot of truth in the wisdom about teaching a man to fish rather than giving him a fish.
Instead, I’m going to discuss how we constructed the formula. Then you can borrow whatever aspects of our approach you think are valid (if any) and build your own version if you like.
The first part of our modest insight was to face the fact that, at the seed stage, most of the value is option value not enterprise value. Any approach based on trying to work backwards from some hypothetical future enterprise value will be either incredibly expensive or little more than a guess. But how do you measure a startup’s option value from a practical standpoint?
The second part of our modest insight was to ask, “Is there anyone who has a big stake in accurately comparing the unknown option value to some other known dollar value?” The answer was obvious once we formulated the question: the founders. If the option value of their ownership stake were dramatically less, on a risk-adjusted basis, than what they could earn working for someone else, they probably wouldn’t be doing the startup. Essentially, we used the old economist’s trick of “revealed preference“.
We knew there could be all sorts of confounding factors. But there might be a robust relationship between founders’ fair market salaries and their valuation. So we tested the hypothesis. We looked at a bunch of then current seed-stage equity deals where we knew people on the founder or investor side, or the valuation was otherwise available. We then reviewed the founders’ LinkedIn profiles or bios to estimate their salaries.
What we found is that equity valuations for our chosen segment of the market tended to range from 2x to 4x the aggregate annual salary of the founders. The outliers seemed to be ones that either (a) had an unusual amount of “traction”, (b) came out of a premier incubator, or (c) were located in the Bay Area. Once we controlled for these factors, the 2x to 4x multiple was even more consistent.
Now, the concept of a valuation multiple is pretty common. In the public markets, equity analysts and fund managers often use the price-to-earnings ratio. For later stage startups, venture capitalists and investment bankers often use the revenue multiple. Using a multiple as a rule-of-thumb allows people to:
- Compare different sectors, e.g., the P/E ratios in technology are higher than in retail.
- Compare specific companies to a benchmark, e.g., company X appears undervalued.
- Set valuations, e.g., for IPOs or acquisitions.
Obviously, 2x to 4x is a big range. The next step was to figure out what drives the variance. Here, we relied on the research nicely summarized in Sections 3.2-3.6 of Hastie and Dawes’ Rational Choice in an Uncertain World. In high-complexity, high-uncertainty environments, experts are pretty bad at making overall judgements. But they are pretty good at identifying the key variables. So if all you do is poll experts on the important variables and create a consensus checklist, you will actually outperform the experts. The explanation for this apparent paradox is that the human brain has trouble consistently combining multiple factors and ignoring irrelevant information (such as whether the investor personally likes the founders) when making abstract judgements.
So that’s what we did. We asked highly experienced angels and VCs what founder characteristics are most important at the seed stage. (We focused on the founders because we had already determined that predicting the success of ideas this early was hopeless.) The most commonly mentioned factors fell into the general categories you’d expect: entrepreneurial experience, management experience, and technical expertise. Going to a good undergraduate or graduate program were also somewhat important. Our experts further pointed out that making initial progress on the product or the business was partly a reflection on the founders’ competence as well as the viability of the idea.
We created a checklist of points in these categories and simply scaled the valuation multiple from 2x to 4x based on the number of points. Then we tested our formula against deals that were actually in progress, predicting the valuation and comparing this prediction to the actual offer. This initial version performed pretty well. We made some enhancements to take into account location, incubator attendance, and the enterprise value of progress, then tested again. This updated version performed very well. Finally, we used our formula to actually make our own investments. The acceptance rate from founders was high and other investors seemed to think we got good deals.
Is our formula perfect? Far from it. Is it even good? Truthfully, I don’t know. I don’t even know what “good” would mean in the abstract. Our formula certainly seems far more consistent and much faster than what other investors do at the seed stage. Moreover, it allows us to quickly evaluate deal flow sources to identify opportunities for systematically investing in reasonably valued startups. These characteristics certainly make it very useful.
I’m pretty confident other investors could use the same general process to develop their own formulas, applicable to the particular categories of startups they focus on—as long as these categories are ones where the startups haven’t achieved a clear product-market fit. Past that point, enterprise value becomes much more relevant and amenable to analysis, so I’m not sure the price-to-salary multiple would be as useful.
According to our Web statistics, my post on Angel Investing Returns was pretty popular, so I thought I’d dive a little deeper into the process of extracting information from this data set. At the end of the last post, I hinted that there might be some value in, “…analyzing subsets of the AIPP data…” Why would you want to do this? To test hypotheses about angel investing.
Now, you must be careful here. You should always construct your hypotheses before looking at the data. Otherwise, it’s hard to know if this particular data is confirming your hypothesis or if you molded your hypothesis to fit this particular data. You already have the challenge of assuming that past results will predict future results. Don’t add to this burden by opening yourself to charges of “data mining”.
I can go ahead and play with this data all I want. I already used it to “backtest” RSCM‘s investment strategy. We developed it by reading research papers, analyzing other data sources, and running investment simulations. When we found the AIPP download page, it was like Christmas: a chance to test our model against new data. So I already took my shot. But if you’re thinking about using the AIPP data in a serious way, you might want to stop reading unless you’ve written your hypotheses down already. As they say, “Spoiler alert.”
But if you’re just curious, you might find my three example hypothesis tests interesting. They’re all based loosely on questions that arose while doing research for RSCM.
Hypothesis 1: Follow On Investments Don’t Improve Returns
It’s an article of faith in the angel and VC community that you should “double down on your winners” by making follow on investments in companies that are doing well. However, basic portfolio and game theory made me skeptical. If early stage companies are riskier, they should have higher returns. Investing in later stages just mixes higher returns with lower returns, reducing the average. Now, some people think they have inside information that allows them to make better follow-on decisions and outperform the later stage average. Of course, other investors know this too. So if you follow on in some companies but not others, they will take it as a signal that the others are losers. I don’t think an active angel investor could sustain much of an advantage for long.
But let’s see what the AIPP data says. I took the Excel file from my last post and simply blanked out all the records with any follow on investment entries. The resulting file with 330 records is here. The IRR was 62%, the payout multiple was 3.2x, and the hold time was 3.4 years. That’s a huge edge over 30% and 2.4x!
Now, let’s not get too excited here. There’s a difference between deals where there was no follow on and deals where an investor was using a no-follow-on strategy. We don’t know why an AIPP deal didn’t have any follow on. It could be that the company was so successful it didn’t need more money. Of course, the fact that this screen still yields 330 out of 452 records argues somewhat against a very specific sample bias, but there could easily be more subtle issues.
Given the magnitude of the difference, I do think we can safely say that the conventional wisdom doesn’t hold up. You don’t need to do follow on. However, without data on investor strategies, there’s still some room for interpretation on whether a no-follow-on strategy actually improves returns.
Hypothesis 2: Small Investments Have Better Returns than Large Ones
Another common VC mantra is that you should “put a lot of money to work” in each investment. To me, this strategy seems more like a way to reduce transaction costs than improve outcomes, which is fine, but the distinction is important. Smaller investments probably occur earlier so they should be higher risk and thus higher return. Also, if everyone is trying to get into the larger deals, smaller investments may be less competitive and thus offer greater returns.
I chose $300K as the dividing line between small and large investments, primarily because that was our original forecast of average investment for RSCM (BTW, we have revised this estimate downward based on recent trends in startup costs and valuations). The Excel file with 399 records of “small” investments is here. The IRR was 39% and the payout multiple was 4.0x. Again, a huge edge over the entire sample! Interestingly, less of an edge in IRR but more of an edge in multiple than the no-follow-on test. But smaller investments may take longer to pay out if they are also earlier. IRR really penalizes hold time.
Interesting side note. When I backtested the RSCM strategy, I keyed on investment “stage” as the indicator of risky early investments. Seeing as how this was the stated definition of “stage”, I thought I was safe. Unfortunately, it turned out that almost 60% of the records had no entry for “stage”. Also, many of the records that did have entries were strange. A set of 2002 “seed” investments in one software company for over $2.5M? A 2003 “late growth” investment in a software company of only $50K? My guess is that the definition wasn’t clear enough to investors filling out the survey. But I had committed to my hypothesis already and went ahead with the backtest as specified. Oh well, live and learn.
Hypothesis 3: Post-Crash Returns Are No Different than Pre-Crash Returns
As you probably remember, there was a bit of a bubble in technology startups that popped at the beginning of 2001. You might think this bubble would make angel investments from 2001 on worse. However, my guess was that returns wouldn’t break that cleanly. Sure, many 1998 and some 1999 investments might have done very well. But other 1999 and most 2000 investments probably got caught in the crash. Conversely, if you invested in 2001 and 2002 when everybody else was hunkered down, you could have picked up some real bargains.
The Excel file with 168 records of investments from 2001 and later is here. 23% IRR and 1.7x payout multiple. Ouch! Was I finally wrong? Maybe. Maybe not. The first problem is that there are only 168 records. The sample may be too small. But I think the real issue is that the dataset “cut off” many of the successful post-bubble investments because it ends in 2007.
To test this explanation, I examined the original AIPP data file. I filtered it to include only investment records that had an investment date and where time didn’t run backwards. That file is here. It contains 304 records of investments before 2001 and 344 records of investments in 2001 or later. My sample of exited investments contains 284 records from before 2001 and 168 records from 2001 or later. So 93% of the earlier investments have corresponding exit records and 49% of the later ones do. Note that the AIPP data includes bankruptcies as exits.
So I think we have an explanation. About half of the later investments hadn’t run their course yet. Because successes take longer than failures, this sample over-represents failures. I wish I had thought of that before I ran the test! But it would be disingenuous not to publish the results now.
So I think we’ve answered some interesting questions about angel investing. More important, the process demonstrates why we need to collect much more data in this area. According to the Center for Venture Research, there are about 50K angel investments per year in the US. The AIPP data set has under 500 exited investments covering a decades long span. We could do much more hypothesis testing, with several iterations of refinements, if we had a larger sample.
In my work for RSCM, one of the key questions is, “What is the return of angel investing?” There’s some general survey data and a couple of angel groups publish their returns, but the only fine-grained public dataset I’ve seen comes from Rob Wiltbank of Willamette University and the Kauffman Foundation’s Angel Investor Performance Project (AIPP).
In this paper, Wiltbank and Boeker calculate the internal rate of return (IRR) of AIPP investments as 27%, using the average payoff of 2.6x and the average hold time of 3.5 years. Now, the arithmetic is clearly wrong: 1.27^3.5 = 2.3. The correctly calculated IRR using this methodology is 31%. DeGenarro et al report (page 10) that this discrepancy is due to the fact that Wiltbank and Boeker did not weight investments appropriately.
In any case, the entire methodology of using average payoffs and hold times is somewhat iffy. When I read the paper, I immediately had flashbacks to my first engineering-economics class at Stanford. There was a mind-numbing problem set that beat into our skulls the fact that IRR calculations are extremely sensitive to the timing of cash outflows and inflows. I eventually got a Master’s degree in that department, so loyally adopted IRR sensitivity as a pet peeve.
To calculate the IRR for the AIPP dataset, what we really want is to account for the year of every outflow and inflow. The first step is to get a clean dataset. I started by downloading the public AIPP data. I then followed a three step cleansing process:
- Select only those records that correspond to an exited investment.
- Delete all records that do not have both dates and amounts for the investment and the exit.
- Delete all records where time runs backwards (e.g., payout before investment).
The result was 452 records. A good-sized sample. The next step was to normalize all investments so they started in the year 2000. While not strictly necessary, it greatly simplified the mechanics of collating outflows and inflows by year. Finally, I had to interpolate dates in two types of cases:
- While the dataset includes the years of the first and second follow on investment, it does not include the year for the “followxinvest”. For the affected 12 records, I interpolated by calculating the halfway point between the previous investment and the exit, rounding down. Note that this is a conservative assumption. Rounding down pushes the outflow associated with the investment earlier, which lowers the IRR.
- For 78 records, there are “midcash” entries where investors received some payout before the final exit. Unfortunately, there is no year associated with this payout. A conservative assumption pushes inflows later, so I assumed that the intermediate payout occurred either 0, 1, or 2 years before the final exit. I calculated the midpoint between the last investment and the final exit and rounded down. If it was more than 2 years before the final exit, I used 2 years.
With these steps completed, I simply added up outflows and inflows for every year and used the Excel IRR calculation.
The result was an IRR of 30% and a payoff multiple of 2.4x with an average hold time of 3.6 years.
Please note that this multiple is slightly lower than the 2.6x and the hold time is slightly higher than the 3.5 years Wiltbank and Boeker calculated for the entire dataset. Thus, my results do not depend on accidentally cherry-picking high-returning, quick-payout investments. If you want to double-check my work, you can download the Excel file here.
All in all, a satisfying result. Not too different from what’s other people have published, but I feel much more confident in the number. For anyone analyzing subsets of the AIPP data, I’ve found that my Excel file makes it pretty easy to calculate those returns. Just zero out all records you don’t care about by selecting the row and hitting the “Delete” key. The return results will update correctly. But don’t do a “Delete Row”. Then a bunch of the cell references will be broken. [Update 1/27/11: I’ve done a follow up post on using this method to test various hypotheses.]
Some of you may recall my post Organic Farming Harms the Environment. As I wrote, one of the things that bugs me about organic proponents is that they act as if there are no tradeoffs. I don’t understand much about farming, but I do understand something about how economic activity works. I presume that modern farming has responded to market pressure and evolved to optimize along many different dimensions. I’m pretty sure you can’t magically improve along one dimension without sacrificing along another dimension.
Thus, I was not surprised to read this article (hat tip to Tyler Cowen at Marginal Revolution) on modern farming by an honest to goodness family farmer. It is full of good examples of the tradeoffs I suspected were lurking. For instance, by using herbicides, farmers reduce the need to till, which is a major source of soil erosion. Hog crates and turkey cages may seem inhumane, but they prevent sows from killing piglets and turkeys dying from drowning. Crop rotations that decrease the need for synthetic fertilizer increase the amount of water needed to produce the desired crop.
Read the whole thing. It reinforced my confidence in the general rule of trying to avoid legislating solutions. Send pricing signals by allocating resource rights and taxing negative externalities. Then let the market do its optimization.
[EDITED 05/08/2009: see here] The majority of people I’ve talked to like the idea of revolutionizing angel funding. Among the skeptical minority, there are several common objections. Perhaps the weakest is that individual angels can pick winners at the seed stage.
Now, those who make this objection usually don’t state it that bluntly. They might say that investors need technical expertise to evaluate the feasibility of a technology, or industry expertise to evaluate the likelihood of demand materializing, or business expertise to evaluate the evaluate the plausibility of the revenue model. But whatever the detailed form of the assertion, it is predicated upon angels possessing specialized knowledge that allows them to reliably predict the future success of seed-stage companies in which they invest.
It should be no surprise to readers that I find this assertion hard to defend. Given the difficulty in principle of predicting the future state of a complex system given its initial state, one should produce very strong evidence to make such a claim and I haven’t seen any from proponents of angels’ abilities. Moreover, the general evidence of human’s ability to predict these sorts of outcomes makes it unlikely for a person to have a significant degree of forecasting skill in this area.
[EDITED 05/08/2009: see here] We are finally ready to go semi-public with our revolutionary new angel funding concept! For the last year, Dave Lambert (the Tiltboy also known as Diceboy) and I have been working on an alternative mechanism for delivering seed funding to technology companies. [REDACTED 05/08/2009: see here].
Here’s the summary. The market for seed capital is clearly broken. Most individual angels will only do about 1 deal per year, which means their portfolios lose money 40% of the time due to insufficient diversification. Even premier angel groups like the Band of Angels say they only do about 8 deals per year. Our math says you need to do 125 to achieve good diversification. On the other side of the table, only 14% of entrepreneurs who want angel funding will find it. Those that do will spend about 6 months looking for money instead of building their businesses.
This is a sorry state of affairs for a market where the overall annual return is 25%+. Here’s a straightforward application of portfolio theory that can fix it. Have a large enough pool of money so one entity can do 125-200 deals per year. Then use an online screening process to give founders a yes or no in two weeks. Obviously, there are a ton of details beyond this, but those are what we’ve spent the last year figuring out. If you’re curious, let me know in a comment here and I will contact you privately.[Links to files REDACTED 05/08/2009: see here].