Mysterious and possibly nefarious trading algorithms are operating every minute of every day in the nation’s stock exchanges.
. . .
The trading bots visualized in the stock charts in this story aren’t doing anything that could be construed to help the market. Unknown entities for unknown reasons are sending thousands of orders a second through the electronic stock exchanges with no intent to actually trade. Often, the buy or sell prices that they are offering are so far from the market price that there’s no way they’d ever be part of a trade. The bots sketch out odd patterns with their orders, leaving patterns in the data that are largely invisible to market participants.
In fact, it’s hard to figure out exactly what they’re up to or gauge their impact. Are they doing something illicit? If so, what? Or do the patterns emerge spontaneously, a kind of mechanical accident? If so, why? No matter what the answers to these questions turn out to be, we’re witnessing a market phenomenon that is not easily explained. And it’s really bizarre.
It’s thanks to Nanex, the data services firm, that we know what their handiwork looks like at all. In the aftermath of the May 6 “flash crash,” which saw the Dow plunge nearly 1,000 points in just a few minutes, the company spent weeks digging into their market recordings, replaying the day’s trades and trying to understand what happened. Most stock charts show, at best, detail down to the one-minute scale, but Nanex’s data shows much finer slices of time. The company’s software engineer Jeffrey Donovan stared and stared at the data. He began to think that he could see odd patterns emerge from the numbers. He had a hunch that if he plotted the action around a stock sequentially at the millisecond range, he’d find something. When he tried it, he was blown away by the pattern. He called it “The Knife.” This is what he saw:
The smarter we are, the more complex the economy becomes, and the dumber we become.
Dynamic Stochastic General Equilibrium models assume we’re all playing a game where the goal is equilibrium. Unfortunately (or fortunately?), though we are all playing games, they’re not the same ones.
At least, that’s how I read this piece from The Economist, which is, frankly, a little beyond me, but contains the kind of phrasing that sets off pleasure nodes in my brain:
Agent-based modelling does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends.
Crucially, agents’ behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This feature of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms. These things are very hard, sometimes impossible, to build into conventional models. But in an agent-based model you simply run a computer simulation to see what emerges, free from any top-down assumptions.
Although DSGE models are also based on microeconomic foundations, they accept the traditional view that there exists some ideal equilibrium towards which all prices are drawn. That this is often approximately true is why DSGE models perform well enough in a business-as-usual economy. They do badly in a crisis, however, because their “dynamic stochastic” element only amounts to minor fluctuations around a state of equilibrium, and there is no equilibrium during crashes.
. . . ABMs contain feedback mechanisms that can amplify small effects, such as the herding and panic that generate bubbles and crashes. In mathematical terms the models are “non-linear”, meaning that effects need not be proportional to their causes.
These non-linearities were clearly on show in the credit crunch . . . These “network-based vulnerabilities” are just the kind of thing that ABMs are good at capturing.
Of course, ABMs need stacks and stacks of data. Of the real-time real-life variety, if possible. Which means the modellers are keen to plug in your phone, your Facebook, your Twitters and all that. Which I’m all for, to be honest. But we’ll see how it goes down with the tin-foil hatters.
More recursion (or is it embedding?) from the Buttonwood column in The Economist:
THE best bet for the long term is to buy shares and hold on to them. That was the lesson hammered into the heads of investors in the 1990s when the “cult of the equity” was at its peak. Unfortunately, they absorbed the message at precisely the wrong time.
The past decade has been disastrous for equities. Over the ten years to June 18th 2010 investors in developed-market equities earned a cumulative total return of minus 7.9%. By contrast medium-dated Treasury bonds returned 95.3% and high-yield American bonds 102.2%. Richard Cookson, the chief investment officer at Citi Private Bank (and a former journalist at The Economist), points out that the cumulative outperformance of high-yield bonds over equities dates back to 1995.
At first glance this seems rather odd. Bondholders have first claim on the corporate sector’s cashflow and shareholders take what is left. In theory, shareholders should earn the best returns over the long term provided profits keep growing. After slumping in 2008 profits have recently rebounded and, in the case of America, are close to a post-war high as a proportion of GDP. Even if profits had been terrible, owners of high-yield bonds would have suffered too because of a likely jump in corporate defaults.
The answer to the conundrum is valuation. As the cult of equity gained more and more adherents in the 1990s share prices were bid to stratospheric levels. On the best long-term measure, Robert Shiller’s cyclically adjusted price-earnings ratio (which averages profits over ten years), valuations in 1999 were more than a third higher than their previous peak, just before the great crash of 1929. It was a nice irony. Investors bought shares because they desired high returns but their enthusiasm pushed prices to a level from which high returns became impossible.
I’ve never really thought of following a Flickr user’s RSS feed until now. This one from Endless Forms Most Beautiful is ace – some of the recent highlights (originally via Kottke, natch):
Grab the RSS feed. Here’s an embed of the most recent additions:
Interesting talk from Johanna Blakley on how fashion survives with no copyright. Or, more accurately, how fashion survives because they have no copyright – trademark is enough.
The argument is simple: fashion is too utilitarian to copyright. It would be wrong to allow something so utilitarian to fall into the hands of a few companies and designers.
More information about the Ready to Share project and a PDF of the slides from the talk here.
There are two really interesting slides used in the talk. This one, which shows the ‘two main binary oppositions within copyright law’ (ie utilitarian vs artistic objects / idea vs expression of idea – as shes says, these are unstable oppostions) and demonstrates how little consistency there is in the current copyright framework:
And this one, which will probably be all over the place and is self-explanatory:
