Sexual Trading

It’s often remarked that stockmarket manias and panics are contagious, as though there’s some virus spreading through the markets, infecting the participants and causing their irrational behaviour. Of course, this is usually meant as a metaphor rather than something to be taken literally – it’s a nice conceit that there’s a disease “out there” causing us to all do stupid things all together.

However, humans are social animals, we live in social networks and networks are prone to attack at their weakest points. Models of how real and virtual diseases spread – the study of epidemiology – can give us clues as to what’s really happening when everything goes screwy. And it turns out that the more connected we are the more danger we’re in.

Rod Steiger’s Network

Most people are aware of the Kevin Bacon game which measures actors by how close they’ve come to acting with the prolific star of Flashdance. Someone who’s acted in the same film with Kevin has a Bacon number of 1, someone who’s acted with someone who’s acted in the same film as him has a number of 2, and so on. By tracing out this network researchers have been able to piece together the network of social connectedness amongst Hollywood actors. In fact it turns out that Mr Bacon isn’t anywhere near the most connected actor, and the thesps should actually be linked by the Rod Steiger number.

What’s really interesting, however, is what happened when the researchers looked at how the actors were connected. It turned out that the average actor has an average number of connections but that a few, key, thespians held the network together. These critical “nodes” had far, far more connections than most of their fellow luvvies. If you think of this as a network, then if you could somehow remove the key players then suddenly the whole thing would fall apart.

Scale Free Networks

It turns out that this is a decent model for the way most social networks link and, in particular, for the way diseases spread. Epidemiologists have a real interest in understanding this because they’re interested in figuring out how to prevent the spread of diseases like AIDS. When a bunch of researchers from Stockholm and Boston studied the Web of Human Sexual Contacts they discovered a network that looks an awful lot like that of the actors. Preventing the spread of sexually transmitted diseases turns out to be difficult because not all of the people have the same importance within the network: the especially promiscuous individuals – the critical nodes – were the key. Targeting these people successfully could stop the disease transmission, but a failure to do so would see re-infection occur again and again.

Networks like these are known as scale free, because there’s no pattern to how connected each node can be – to be precise, the distribution of connections follows a power law. It seems that the worldwide web has the same pattern. A few, very popular websites, are linked to with a frequency that’s way in advance of the average site. These sites are our critical points for disease transmission when some problem sweeps the information highway. And herein lies the next clue to the epidemiological model of stockmarket madness.

Markets Are Social Networks

Markets are inherently comprised of traders, agents communicating with each other, doing deals. Some of these agents are our supernodes, the critical people holding the vast majority of the network together. If these nodes get infected then we can surmise that all of their unimmunised contacts also get the virus. Which is why we have nothing to fear but fear itself.

Interestingly this research suggests an amendment to efficient markets theory rather than an alternative to it – a reminder that econophysics research like this comes from a background of rational theory rather than empirical observation. The development of scale-free networks from models of interacting traders has been observed by Tseng, Chen, Wang and Li, who posit a concept of zero intelligence traders in their models. This, of course, may be rather closer to the truth than they imagine, but the argument is that the scale-free nature of the network emerges from market structure, not trader interaction – aka, the Efficient Market Hypothesis.

On the other hand a paper from Rossitsa Yalamova suggests that extreme market events are a result of the coordination of trader activities due to synchronisation of trading rules through a relatively small number of highly connected individuals. As she states:

"I argue that stock price correlations actually reflect coordination of traders activity that might be initiated by different factors related to news or noise. At the end what determines price changes are trading orders. Therefore, I propose that price changes actually measure the interaction strength among traders especially in extreme events situation."