Bubbles. I’m no expert in behavioral economics, but bubbles seem to be well understood (after they occur) although they seem hard to detect (at least in the eyes of outsiders and late bubble participants). This post won’t tell you how to avoid bubbles, but might give you some insight.
I came across Minsky’s explanation of bubbles (vulgarized), hidden in a comment from this post from The Big Picture, and the part about market invasion by outsiders stuck to my head (see right quote).
Increasing prices are not enough for a bubble. Every financial crisis needs rocket fuel and there is only one thing that this rocket burns – cheap credit. Without it, there can be no speculation. Without it, the consequences of the displacement peter out and the sector returns to normal.When a bubble starts, the market is invaded by outsiders. Without cheap credit, the outsiders can’t join in.
Question: How do we detect if outsiders has invaded the market (from a measurement point of view)?
Obviously, this depends on the context and the way transactions are performed. Let’s look at two recent bubbles: the real estate bubble, and the most recent Netflix bubble.
For the first case, the real estate case, we’ll be looking at “Loans Secured by Real Estate“, available by the Federal Reserve Bank of St. Louis. By visual inspection, it looks like it behaved exponentially after 1995. One can convinced himself of this with the following half-baked code:
o <- read.csv('data/loans.csv')
o <- ts(cumsum(o[,2]), start=1985, frequency=4)
plot(log(o)) |
It looks like the cumulated amount of secured loans started exponentially a bit before 1995 up to just before 2010. Again, nothing is really precise in this post. One could use nonlinear fitting to see where things really started and went bust. If it really did expand exponentially, the bubble wasn’t anywhere near sustainable. Note that this doesn’t argue whether we can have a “soft-landing” or a bubble bust.
For the second case, the Netflix 2011 bubble, let’s look at the cumulated volume in NFLX, and we can see the linear growth changed drastically in April 2010, a period we can attribute to the inclusion of outsiders (due to media over-coverage or some other factor).
library(quantmod)
getSymbols('NFLX')
plot((cumsum(Vo(NFLX)))) |
The second increased in linear growth might be attributed to the recent dump of the stock. To continue this analysis, one could use R for piecewise linear fitting.
So far as the real estate bubble: some of us saw it happening in 2003, although the (self interested) mainstream pundits didn’t or wouldn’t. The measure is simple: the historic ratio of
median house / median income
is a (near) constant over short to medium term in any one place. Aggregated over the USofA, it’s a bit flaky, given regional differences in price/wage levels.
Here’s a plot: http://drcoddwasright.blogspot.com/2011/08/viagra-at-home.html
The point of bubbles is quite simple, they occur when mo money chases less stuff. Credit isn’t really needed, although that certainly happened with the Subprime Mess. A bubble can happen with just a shift of funds out of proportion to some sector. The first dot com bubble was just that; a massive shift of existing funds to a narrow sector.
The Subprime Mess was motivated, in the patient zero sense, by Greenspan’s decree that interest rates would be held down. The bread crumbs can be traced. The point is that it was not the result of independent homo economicus decisions, but deliberate political decisions by a few political appointees. Pre-20th century, not so much.
The financial services coup de etat of our economy is a bubble, from the point of view of historic proportion of national income. This was a co-ordinated shift from the many to the few orchestrated by Right Wingnuts in Congress. Nothing to do with excess credit. One could go on for days.
Specific (or narrow sector) stock bubbles are generally driven by ignorant retail fools. We saw that with vaccine stocks two years ago. Nothing to do with excess credit. Netflix is quite the same.
In other words: Minsky’s only half (b)right. Inflation requires mo money, but narrow (in time or space) inflation need not require global increases in cash/credit. If you look at median income from 1980 to 2008, you’ll see that it moved virtually not at all. The Subprime Mess was motivated as much by existing funds shifting to “higher yield, lower risk” housing instruments due to Greenspan’s enforced low interest rates. Up to then, yes, housing instruments were low risk, *just because* they were restricted to conservative price/income ratio. Blow out that ratio, and you blow out the risk. Some noticed, but the mainstream pundits turned a blind eye. It was in their self interest to do so.
Analytics don’t help much when the propellent is vicious politicians in league with Banksters.
Posted by Robert Young | October 26, 2011, 11:58 pmA few russians did a study of bubbles using LPPL to identify when something is bubbling. If you google LPPL and gold I am sure you can find them, or I can send them to you.
They were quite interesting even for a non mathematician, they predicted the collapse of gold last spring…
Posted by chris | November 30, 2011, 10:31 pmYes, that little application of catastrophe theory to gold was rather interesting. Unfortunately they predicted gold would crash in mid-June, arguably that happened in September. Easy to by off by a couple of months in a bubble, but not a great advertisement for the theory.
Posted by Keiran | December 3, 2011, 5:59 pm