# Angel Investing Returns

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:

1. Select only those records that correspond to an exited investment.
2. Delete all records that do not have both dates and amounts for the investment and the exit.
3. 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:

1. 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.
2. 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.]

1. Todd White says:

Looking at the dataset, I see about a 2/3 probability of a non-zero exit, and a 48% probability of a positive undiscounted return. Don’t those numbers seem high?

1. Kevin says:

Not really. Remember, these investments are across all stages. They’re not just ideas on paper. A lot of them were probably pretty established ongoing businesses.

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4. Matt says:

This is better methodology, but it’s wasted on suspect data. The data set comes from self-reporting, and only 13% of the surveyed angels responded. That’s two huge biasing factors. I suspect, based on the number of startups I see with seed rounds that never raise professional money, that the actual return is an order of magnitude lower.

1. Kevin says:

Thanks for stopping by Matt.

I refer you to the Dwyer and DeGenarro paper–where Fed economists analyze this data set for the bias you’re referring too and don’t find any overt signs. Though it’s difficult to prove a negative, of course:

http://www.frbatlanta.org/pubs/wp/working_paper_2010-14.cfm

As far as personal experience vs data, I’ve provided my philosophy on this topic in this blog post where I relate my a discussion with Brad Feld: