In @Jesse_Livermore interview he mentions how exceedingly high valuations are increasingly dependent on liquidity or what he terms "networks of confidence".

He refers back to prior work that shows how you'd need a healthy discount to intrinsic to buy an asset you couldn't sell

The fact that you can sell your at an in line price lowers your risk threshold to buy expensive assets.

And we see assets with long durations now. I think of duration as how long it would take to recoup your initial investment. Stocks and bonds have long durations today.

If these long durations are acceptable because we trust liquidity, and the idea that the market will not wake up one day and just reset at much lower multiples, it feels like risk that should be priced in an implied distribution.

Distributional parameters like skew and kurtosis should relate to fundamentals.

This is with no data, just musing, but I would think that "value traps" just bleed off. So they have skew and path behavior that you would not expect in say a speculative new tech or biotech company

So for high duration assets that rely on 'networks of confidence' how should the parameters be priced?

In historical examples of high duration assets, would was the realized history that ensued? What parameter would have been best to own and what did it cost ex-ante?

None of this is saying anything non-obvious. Parameters aren't usually priced naively or without respect to the distribution of the underlying. But the 'networks of confidence' thing is putting words to something that I have thought of in context of stocks like TSLA...

If you woke up one morning and the price was 1/2 of what it was on no news would you be able to justify buying it on a fundamental argument or would you want to buy it simply on the memory that it traded 2x yesterday.

The number of longs who have a deep, fundamental conviction on the price based on serious work is likely a tiny fraction of all the longs. If you wiped out the price history, they might be the lone buyers.

Maybe there's some analyst whose work thinks TSLA is a buy between the original and halved price based on fundamentals and didn't buy it a few months ago when you know it actually was that low. This feels like a deeply hypothetical human and there's less of them than

People who would wake up, wiped of memory, see TSLA's price (which is unknowingly halved from yesterday from their POV) and decide to short TSLA.

So names like this feel like "poof" names to me. Things that could just collapse. You'd shrug & wonder how they ever got so high.

And the lower price wouldn't be any more attractive than the higher price was. The mental anchoring to the fact that it traded higher would probably have a tyrannical effect on your evaluation. And that's the point. Most of us are not actually evaluating.

Personal example:

I bot YHOO for $20 in the midst of the GFC on the basis that MSFT had just bid $31 for them recently and the deal came apart. I had no idea what YHOO business is worth. No concept of downside, intrinsic, whatever.

I was anchoring to the price presumably smart people at MSFT did. But I put myself in "poof" situation. (I should have structured the trade with a better plan admittedly). The stock could and did just fall apart and I don't have any conception of its value

(I actually later did get a better sense, as I had a friend who worked there who gave me a very balanced picture of what they were worth on some conservative sum of parts stuff)

Anyway, more of an example of putting myself in a bad spot, taking network of confidence for granted

So if the stock market is trading 30x or about 3% per year, it has a duration of nearly 30 years (assuming no growth and rounding up a bit).

If the market haircuts overnight by 1/3, you have lost 10 years of returns. If something trades 10x losing 1/3 only costs you 3 years.

If high duration comes from excess reliance on liquidity where should the risk of "poof" be priced?

(This is more musing -- it's pretty low res to talk about "the stock market" with so much sector dispersion in 2020)