I’ve been listening to NPR’s Planet Money
podcasts recently, and a couple of days ago I listened to this one about economic bubbles. Bubbles are what some people call
it when the price of something goes up and up and then crashes, and often you could kind of see that a crash was coming sooner or later. Sometimes
people say that people were silly to spend all the money buying the thing when
it was expensive. The podcasters spoke to Nobel Prizewinner Robert Schiller,
who thinks the people driving up the price were being silly, at least
sometimes. They also spoke to Eugene Fama, who shared the prize, and thinks
that people aren’t being silly; you just can’t tell when something’s a bubble
and when it isn’t. It was all pretty good-natured. Schiller and Fama shared
their prize with another guy called Lars Peter Hansen, if you’re interested.
Now, regular readers will probably have noticed
that one of my hobbies is duplicating the intellectual efforts of other people,
and today is no exception. I had a bit of a think about bubbles. Silliness is
hard to model, so I tried to think of a kind of situation where the price of
something might go up and up and then crash, even though the investors are
going into it pretty much with their eyes open.
Here’s what I came up with. You have some
kind of commodity which is generating a pretty good income at the moment, but
you don’t know how long it’s going to carry on doing it. Maybe it’s a tulip
farm and at the moment people are paying top dollar for tulips, but you don’t
know how long the craze is going to last. Maybe it’s a share in Justin Bieber’s
record company. How do you value something like that?
Well, you need to quantify how long you think
it’s going to last. Maybe you think it’ll last something between one and ten
years, and your credences are distributed equally over one year, two years and so
on up to ten years. That lets you put an expected value on it. Now, if you’re a
Bayesian and it’s still popular after a year, you’ll think it has between one and nine years
left, and your credences will be equally distributed over one year left, two
years left, and so on up to nine years. This lets you put a new expected value on
it, and it’ll be lower than it was a year ago. So the expected
value of Bieber's tulip farm goes gradually down, until sometime during this decade
people lose interest in his tulips and the value of the farm crashes. That’s not a
bubble. In a bubble the price is meant to go up until the crash.
Suppose you model your uncertainty about how
long the craze will last differently. Instead of thinking how long it’ll last,
you think about what the craze’s half-life is. Maybe you think it’s got a
half-life of between one and five years, and you distribute your credences
equally over half-lives of one year, two years, and so on up to five. (The
craze’s half-life is the length of time in which it has a 50% chance of ending.
In general, if the half-life is h years, the chance of the craze surviving the next h*n years is 0.5n.)
Now what happens if the craze is still going
after a year? Well, that was more likely to happen if the half-life was long,
so you end up redistributing your credences to make a long half-life more
likely and a short half-life less likely. This means that after a year the
value will go up. And it’ll keep going up until the craze ends. That’s got the rise-rise-crash character of a bubble, but nobody has had to do anything silly. This is true even though it’s
predictable that the price will rise until the crash, and even if the
investors are all sure the crash is coming sooner or later. I guess that if
prices going up and up and then crashing was always this kind of phenomenon,
that would mean Fama was right and Schiller was wrong. But I don’t really know; I
just listened to one Planet Money podcast. (Well, actually I’ve listened to
about a hundred Planet Money podcasts, but only one was about bubbles.)
Is this a reasonable way of dealing with
uncertainty about how long a craze will last? Sometimes, it probably is. People
have been into Barbie dolls for longer than they’ve been into Loom bands, and
this inspires confidence that Barbie will still be around after Loom bands are
gone. The longer Loom bands stick around, the longer they might seem to have
left. When something’s been around as long as Barbie and Coke, it’s hard to imagine
it ever going out of fashion.
So here’s another question: do real
commodities exhibiting the price-rise-then-crash phenomenon fit this model?
Well, no. Not exactly. The dotcom bubble was based on a load of companies which
often weren’t bringing in much income at all at the time (right?), with
investors betting on future income. But I think that can still fit into the
model. Even if you only projected that the income would come, say, five years
into the craze, the expected value still goes up as the probable half-life goes
up, and the probable half-life carries on going up until the craze ends (or
until a bunch of similar crazes end). So maybe the dotcom bubble was like that
too. And the tulip bubble. Anyway, I recommend the podcasts.