AIPP Data Modeling (Angel Investing, Sidebar 2)

More from the Angel Investor Performance Project data.

Much of the financial activity at early stage companies happens around investment rounds. Acquisitions, failures, and fire-sales tend to happen when it's time for the company to raise more money. So it doesn't make sense to look at rates of failure or exits divorced from the fund-raising process.

Here's a look at the AIPP data from the last post. Company exits and the sale multiples by years held. A zero multiple means the company failed.

	Sale Multiple
Year	0	0-1	1-2	2-4	4-8	8-16	16-32	32-64	64-128	128-256	256-512	>512
0	5	3	0	1	0	0	0	0	0	1	0	0
1	22	8	3	35	1	2	1	0	0	0	0	0
2	36	24	6	5	3	1	1	1	0	0	0	0
3	25	6	6	8	21	1	1	1	1	0	0	0
4	29	8	3	39	1	1	1	1	0	0	0	0
5	14	23	7	6	1	0	0	0	0	0	1	0
6	2	2	1	1	1	1	1	2	0	0	0	1
7	1	5	6	0	0	1	0	0	0	0	0	0
8	1	2	1	1	2	1	0	2	0	0	0	0
9	0	0	0	0	0	2	0	0	0	0	0	1
10	1	0	1	0	0	0	0	0	0	0	0	0
>10	11	0	0	0	2	1	1	1	0	0	0	1

Note the oddly large numbers in the 0-1 multiple range in years 2 and 5. My hypothesis is that these were companies that could not raise their next round (the A and the B, I assume) and went through a fire-sale, resulting in some money to the investors but not a gain.

What would Markov do?

This, I think, more closely corresponds to reality than the continuous assumptions most analyses take. Note that this roughly agrees with the common wisdom that 1/3 of venture-backed companies fail (here, 34% overall), 1/3 return capital (here, 19% overall are fire-sales so return >0x and <1x, but some bleed-over into slightly more than 1x returns could be attributed to the 1/3 "return capital") and the rest make money for the fund.

The AIPP dataset is not large enough to be able to make good predictions about sale multiples at each round. But if we assume that

• Venture investments as a whole make 25% p.a.;
• Venture funds end up returning 2x cash on cash on average;
• Seed is in year 0, any A would be in year 1, any B would be in year 3; and
• A sale after the B would be in year 6.

Then we can model the multiples at each point. The mean cash-on-cash sale multiple would be*

Full follow-on:

• 1.6 for pre-A sales, 
• 4.4 for pre-B sales, and 
• 4.7 for post-B sales;

No follow-on**:

• 1.6 for pre-A sales, 
• 6.4 for pre-B sales, and 
• 10.2 for post-B sales.

Actual multiples would follow a power law probability distribution, as noted in the first sidebar, with these as the means.

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* I doubt this is a unique result. In configuring the model behind these this seemed most reasonable to me given my experience.
** If you follow on, then you invest money in the A and B at a higher valuation, so your eventual multiple is lower, albeit on a larger investment.