Posts tagged with startups
The Manhattan-based firm will net $253 million from the deal, mostly from a $400,000 seed investment it made in Tumblr in 2007
Many thanks to Sameer Al-Sakran for compiling the numbers.
Category Started Funded %F ∑Raised TC Posts Advertising 3972 631 16 $8B 996 Biotech 2787 1770 64 $42B 43 Cleantech 1302 719 55 $33B 192 Consulting 3330 176 5 $2B 444 Ecommerce 5383 868 16 $10B 1587 Education 522 60 11 $½B 90 Enterprise 2389 652 27 $10B 993 Games video 3992 910 23 $16B 3625 Hardware 1726 613 36 $12B 3274 Legal 306 23 8 $0.1B 16 Mobile 4101 1099 27 $20B 4263 Network hosting 1782 340 19 $8B 1375 Other 33068 2022 6 $24B 2501 Public relations 2531 468 7 $7B 765 Search 1437 226 16 $2½B 4625 Security 710 218 31 $4B 135 Semiconductor 620 381 61 $9B 27 Software 12405 3039 25 $33B 3733 Web 12830 2401 19 $29B 14356 Category Acqu %A IPOs %I % on TC Avg Funding Advertising 221 5½ 15 ½ 6½ $2M Biotech 332 12 143 5 ½ $15M Cleantech 72 5½ 39 3 5½ $25M Consulting 107 3 15 ½ 1½ $½M Ecommerce 188 3½ 16 ⅓ 12½ $5M Education 4 ¾ 1 ¼ 3½ $1M Enterprise 257 11 41 1½ 9 $4M Games video 285 7 30 ¾ 11 $4M Hardware 139 8 81 4½ 6 $7M Legal 2 ½ 0 0 1 $⅓M Mobile 275 6½ 34 1 13 $5M Network hosting 154 8½ 22 1 8 $4½M Other 2325 7 101 ½ 2½ $½M Public relations 171 18½ 32 1½ 19 $3M Search 57 4 4 ¼ 10 $2M Security 96 13½ 12 2 7 $6M Semiconductor 119 19 51 8 3 $15M Software 1101 9 110 1 5 $2½M Web 827 6½ 57 ½ 14 $2½M
My favourite number here is the number of companies started.
12,830 Web companies started up and got a Crunchbase profile. Forget about the Facebooks and Instagrams’ buyout package to the founder, that’s the
max of the sample. If you’re looking at the
lower-50% CVaR, it may be $0 or less.
My second favourite number is that, even among the crème-de-la-crème who play these games, they have less than one-in-five chance of either acquisition or IPO.
As you might expect, stuff that’s harder to do and takes more technical expertise (semiconductors, hardware, biotech/cleantech) has a higher rate of success than stuff that can be learned in a year or two by >1% of the population (build a Rails app!). Software seems to be at a disadvantage except enterprise has a one-in-ten acquisition rate, which is quite a gamble with your life but counts as good odds in this low-probability game.
On the other hand, the software companies are much cheaper to start than cleantech/biotech (cleantech has highest avg funding). Web companies are 1 order of magnitude cheaper to start.
P.R. is also a standout, I’m guessing the 18% acquisition rate is acquihires (Sameer Al-Sakhran alluded to this). But still, this reveals that public relations must be an important part of the SF business ecosystem, or else the market is mispricing PR. But I have enough stereotypes about geeks who can’t negotiate that I can explain away the high valuation of smiley PR folks filling the niche none of the cool hackers want to talk about.
Of course, these are “running tallies” not “final fail/success rates”. It would be good to know
- for the subset that exited, what’s the year of founding and the year of exit?
- for the subset that didn’t exit, what’s the year of founding?
That might help us guess at what companies have been abandoned. (Did a lot of Web companies—maybe unfunded ones—make Crunchbase profiles for themselves to put themselves on display and then quit after a few months?) It would also give a more precise idea of the number of years it takes to develop a company to IPO-ability. (“Eating Ramen” is expected for a few months, but what about if it’s half a decade?)
If I get around to doing my own scrape, I’ll add those things—as well as some ggplots of distributions for some parameters. I’d also like to compare some Crunch-based estimates of success rates with YCombinator and TechStars, etc. That would be hard because of selection effects but still nice to see a side-by-side.
In the meantime, big thanks to Sameer for doing it first.
One misconception I got from the academic theory of finance is that risk and reward go together. You take on more risk, you get more reward. This is formalised in CAPM theory as a higher expected return associated with a higher standard deviation of investment returns.
In reality, ∃ many stupid risks—mistakes, bad ideas, not doing your homework, believing people you shouldn’t believe, taking on a job without negotiating a floor for your own compensation first, or investing in a company that was bound to tank.
Recently, academics have undercut the premise that risk goes hand-in-hand with reward. Perhaps this pill is easier to swallow after seeing “dumb money in Düsseldorf” vacuum up synthetic CDO pyrite (AAA mortgage bonds) spun from BBB bonds—and then find out, publicly, along with the rest of investment Narnia, that the rewards were nowhere near commensurate with the risks.
I’ve seen this play out a little more in private equity, where models of price paths are less influential than common sense, gut reactions, and balance-sheet research.
I don’t know as much about trading. But I’ve read between the lines on the EliteTrader forum and its cousins, and got the sense that, as academic papers that study the matter report: most day-traders lose money on expectation. Their trading capital approaches $0 faster than would be expected merely by the drag of trading fees on a statistical mean of zero profit.
Warren Buffett, the world’s best living investor, is in a business where risk and reward are inverted from the CAPM model. (He’s written about it plenty so I won’t repeat him.)
Steve Schwarzman, another of today’s most successful investors, says in this lecture that he focusses on figuring out every possible angle beforehand, not making any mistakes, controlling every risk and making sure he wins. I’ve read similar things in interviews where Mark Zuckerberg or Peter Thiel talk about “making their own luck”. A lot of questions and decisions go into running a business, and I find it entirely credible that getting that right increases the chances of success—that if an omniscient Arjuna were starting a company today, he would have a very high chance of success (again, what does “chance” mean? Where do the “possible worlds” come from?)
Insurance and reinsurance companies, though they may serve a social function, aren’t actually concerned with actuarially converting risk into reward. They’re interested in collecting as many large premia as possible for risks that will never harm their balance sheet. Why do you think they have three times as many claims adjusters as actuaries? Si guarda al fine.
Michael Price, one of the stars of The Vulture Investors, bought a loan to a bankrupt company for 47¢ on the dollar, covered 15¢ immediately with cash, plus 45¢ in bonds plus 23% of the post-bankruptcy company. He needed the bargaining skills and the capital to buy out other bondholders and negotiate a good rate for
One last classic example: McDonald’s. Ray Kroc saw a huge return on investment but only took smart risks, doing less of the hard work and spending more time being successful. Mr. Kroc didn’t finish college with a bright-eyed hope to be the world’s greatest entrepreneur (cf. YCombinator). He sold Dixie cups for 17 years before he saw an opportunity—in a B2B space—with high returns and low costs. (Selling malt mixing machines back when malts were the profit centre for burger joints—a malt might cost as much as sandwich + fries, or even as much as sandwich+fries+coffee.) The malt mixer business was a classic play; it would earn 100% checkmarks from a Business 101 textbook. Only after Ray Kroc saw another opportunity related to the business he was in, did he buy up the MacDonald Brothers’ restaurant and multiply it out. Again, this is a textbook private-equity move: find a proven business where somebody has completely figured out how to make money hand over fist, such that the only other thing they need is more money. (Obviously this is very different from an entrepreneur with an idea who just wants some money or thinks their failing idea would be saved if only they had more money.) You provide the money and collect the multiplied profits, i.e. you take on the easy part of the problem, negotiate the terms so you get a huge return on solving it, and then you’ve done little work for great reward. That’s a “smart risk”, not a correlation of risk and reward.
We could probably go back and forth with examples of titanic companies. (Sure, Ted Turner threw massive sums into a money pit for over a decade before seeing TNT and its siblings become profitable.)
But still I think the overall message of
risk~reward is wrong. There are smart risks, and there are dumb risks. Don’t expect that just because you did something risky, that the return will be good. Work smart, not hard. Cover your *rse and check yourself before you wreck yourself.
Gauging the frothiness of the webby/techy/san-fran VC market.
Based on the NVCA statistics on the venture capital industry, there are [approximately] 1,000 early stage financings every year….
And somewhere around 50 - 100 of them exit for more than $100mm every year. So 5-10% of the companies financed by VCs end up exiting for more than $100mm.
Mathematical PS: These are value-at-risk numbers, just upside-down.
With the the increasing availability of complicated alternative investment strategies to both retail and institutional investors, and the broad availability of financial data, an engaging debate about performance analysis and evaluation is as important as ever. There won’t be one right answer delivered in these metrics and charts. What there will be is an accretion of evidence, organized to assist a decision maker in answering a specific question that is pertinent to the decision at hand.
(by Brian G. Peterson & Peter Carl)
People have been saying that ads are going to be more targeted on the Web since the 1990’s. The more data we give to corporations, the better they can “serve us” ads which “meet our needs”.
I read that again on Scott Adams’ blog a little while ago. I’m doubtful that more data → more targeted ads, because Gmail and Facebook each know about a jillion things about me, and I still see irrelevant ads frequently.
Here are my made-up guesses of why ads are still irrelevant:
- it’s cheap to serve irrelevant ads (true in email)
- no matter how much data, people don’t currently know what to do with it — their models aren’t good enough to weed out false positives (non-customers)
I don’t bring this up to complain, I bring it up because I wonder if a team of mathematicians, psychologists, dataists / statisticians, and marketers, couldn’t do better ad targeting and make a business of it. Maybe Mathematical Capital would invest in a business like this.
The wealth required by nature is limited and is easy to procure; but the wealth required by vain ideals extends to infinity.