

Paul Graham is right (using AVC’s data) - 3pt14159
http://zachaysan.tumblr.com/post/1431828646/paul-graham-is-right-using-avcs-data

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nadam
I don't really understand what the debate is about. Even if the market is
totally 'bimodal' valuations matter the same way as in a non bimodal market.
Expected return is orthogonal to expected Risk.

Let's say the market is so bimodal that a startup fails or becomes google.
Then the expected return is:

p * G

where 'p' is the chance of becoming Google, 'G' is Google's value.

Basically investors' bid for valuation is their bid for what p is.

The story is only that if they say that the company's value is X while the
startup says it is 2 * X that means that they say p = p1 while the startup
thinks p = 2 * p1

Of course valuations matter. It matters linearly. In any market. Even in the
most risky markets. I can decrease my risk by diversifying my investments into
lots of different high risk assets and my expected return remain the same, but
I can only increase my expected return by buying in at low valuation. This is
exactly what YCombinator does. This is just very basic math.

TL;DR: The fact that investors care about valuations does not mean they don't
recognize that the success distribution is bimodal.

~~~
soundsop
If p is bimodal, then p = 2*p1 is not possible. Either p = 0 or p = 1. The
values in between do not occur.

Then the only thing to quibble over is G. But if you believe p = 1 for a
company, what's the point of quibbling over G?

~~~
india
No. The event being bimodal only implies that the probability is centered
around two values, not that p =1 or p = 0 for those values. For example, take
a biased coin that turns up head once in a million. This is bimodal with
p(head) = 10^-6.

PG's argument appears to be that the error in your estimate of p is almost
always larger than the value of p and one should invest as long as 2*p is also
within the estimation error.

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jessriedel
> PG's argument appears to be that the error in your estimate of p is almost
> always larger than the value of p and one should invest as long as 2*p is
> also within the estimation error.

I don't think this is correct under canonical Bayesian reasoning. All
uncertainties (whether because of ignorance or "objective uncertainties") can
be bundled into your probability assessment. There is no "error" on your
probability.

I'm not expert though. I'm only....80% sure.

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andrewparker
Interesting chart, thanks for presenting. A few issues with your methodology:

1\. I'm sure compounded returns would be nice to do, but you don't know the
start date for any of those investments. A 2004 vintage fund does not mean the
investments are from 2004. Instead, it means the fund made it's first
investment in 2004 and will continue deploying money over ~7-8 years.

2\. In the VC industry, a wipeout is a wipeout. Call it Zero, not 0.5x.

3\. LPs care more about cash-on-cash multiples, not compounded returns (aka
IRR). There's an expression that will help explain this point: "You can't eat
IRR." VC is a 10-year horizon business and GPs call do special moves to
improve the IRR (calling down capital as needed, and then returned capital
upon exit even if it's going to be called down again and reinvested). But, at
the end of the day, the best measure of the success of any fund is the gross
cash-on-cash multiple of the entire fund.

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bluedevil2k
These types of returns and their distribution are surprisingly seen across
every industry, no matter the scale you're looking at. For example, if you had
invested in all the top car companies that existed in the early 1900's, you'd
have lost money on most and only gained money in Ford, GM, Dodge, and overall
you would only make a reasonable about of money. Warren Buffett points out
that this type of behavior can be seen across every revolutionary business in
the past 100 years - automobiles, airlines, radio, television, computers, and
now we're seeing another example of this.

On a larger macro-scale, this type of return distribution can be seen in the
S&P 500. Over the past 20 years (1990 - 2009), the S&P 500 returned 8.2%.
However, if you had pulled your money out of the market on the 10 best single
days of those 20 years, your return would drop to 4.5%. If you took out the
best 30 days from the past 20 years, you'd have a _0% return!_ Imagine that,
30 days out of 20 years, and it would cost you all your gains. The same
pattern can be seen over any time period for the S&P.

The lesson in all of this is that diversification is the key to consistent
returns.

~~~
anamax
> However, if you had pulled your money out of the market on the 10 best
> single days of those 20 years, your return would drop to 4.5%. If you took
> out the best 30 days from the past 20 years, you'd have a 0% return! Imagine
> that, 30 days out of 20 years, and it would cost you all your gains.

I suspect that the 10 and 30 worst days had comparable effect, albeit in the
other direction.

You can't win if you don't play, but neither that nor the dominance of "big
win days" implies that you should play every day.

~~~
bluedevil2k
Exactly right. You remove the 10 worst or 30 worst days, your returns would be
way above the 8.2% average. The point is, no one knows ahead of time which
days will be one of the 10 best, and which will one of the 10 worst. Same
thing with angel investors - you don't know which startups will be home runs
and which ones will be strike outs. The data suggests you should play every
day.

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joshu
If you double the valuations, the # of bets halve. This affects your
likelyhood of getting ANY wins.

My own angel portfolio: since 2006: 46 deals, three possible winners, four
dead, one weak exit.

~~~
mmt
_If you double the valuations, the # of bets halve. This affects your
likelyhood of getting ANY wins._

I would expect this is only true if one "must" have a certain percentage of a
startup. It makes some amount of sense for traditional VCs who insist on
taking a board seat, but I though this was a differentiating characteristic of
angels.

~~~
joshu
you are typically limited by the % the company is willing to sell.

higher valuation deals tend to raise more.

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lisper
> you don’t let the really good ones get away because they are asking twice as
> much as you were expecting

The problem is that to actually apply this strategy you have to overpay for
everything, because you don't know which ones are the really good ones until
after the fact.

~~~
emmett
Ah, but you don't overpay for the failures, because they don't take more of
your money. Instead, you invest the same $500,000 and get a smaller percentage
of ownership. But in all of the 0x and 1x return cases, it doesn't matter what
percentage of the company you own.

~~~
lisper
For the outright losses this is true. But overpaying can transform the
moderate wins into losses. Buy in at 1M sell for 10 you get a 10x return. Buy
in at 20, sell for 10, you get a 50% loss. So if you're playing the middle
(which is where the _reliable_ returns are to be found) valuation matters. A
lot.

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ithayer
Of course this data is presented in hindsight. If expected value of a return
on a company is the probability it's the next G times the percent you own:

E[Return] ~= E[Company] X %Owned

And

E[Company] = P(Company=NextGoogle) X Value(NextGoogle)

If you invest in a portfolio of companies, then you'd try to control the
things you can:

1) P(NextGoogle): Impossible to estimate, look for good founders

2) Value(NextGoogle): maximize this (look for big markets)

3) %Owned: maximize this

Note that YC does (1) and (3). I would argue they don't do (2) at all. Noone
thought AirBnb would be as big as it is, but they're crushing it.

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pge
I disagree with the conclusion that the return distribution is bimodal. My own
analysis* is that it is instead lognormal. This shape makes sense to me - a
lognormal distribution is the result of the product of random variables
(whereas a normal dist is the sum of randomly distributed variables). The
success factors in a startup tend to be multiplicative, not additive (e.g.
great market, great product, customer adoption, great strategic partners,
etc).

* based on observed portfolio performance which is unfortunately not public information, but also on published data from mckinsey

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far33d
Beat me to it: <http://news.ycombinator.com/item?id=1847466>

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SemanticFog
Your graphs don't back up your headline.

Fred thinks his returns are trimodal, and that he plays mainly in the middle.
Looking at your chart, he seems to be 100% correct. He's got one exit at 70%
annual returns, but he makes the bulk of his money in the middle of the range.

~~~
fredwilson
that's right

i can't comment on that post, so i will do it here

if we toss out our two biggest winners, we will still have a terrific fund

of course, i am thrilled to have them

but they are not required to have a good fund

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acgourley
But what if the venture capital model creates a situation where outcomes must
be bi-modal? Consider the case where a CEO goes to a VC (either on the board
or in the fund raising process) with 2 plans

Plan A has a ~90% chance of slow growth Plan B has a ~10% chance of explosive
growth

The VC will point you towards plan B, because plan A isn't a win given their
investment structure.

If this is true, then you cannot conclude that investments are always bimodal,
just that in the past they have been due to structural reasons. And this is
relevant because investors could create a model where they foster and profit
from non bi-modal companies.

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shawndumas
Would the amount invested strengthen the chance of a good outcome?

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klochner
a compounded 20x return does not validate the following:

"The expected value of a startup is the percentage chance it’s Google"

