It is a well-worn cliché that financial markets are simple but not easy – the implication being that they are relatively straightforward to explain but devilishly difficult to predict. Perhaps a more subtle approach would be to say that the markets are complex but not complicated. This isn’t playing with words these terms are chosen carefully and have precise meaning.
By way of explanation let’s look at the opposite case. A watch is complicated but not complex. When we open it up there is a whole heap of complicated gears and springs etc. but its outcome or objective is simplicity itself – it just tells the time. Now when we look at markets the machinery is pretty simple (buy/sell, borrow/lend, now/later pretty much covers everything) but the outcomes are complex because they are not readily predictable and certainly not stable or certain. This is the world of Complex Adaptive Systems and network theory. Drawing on ideas from both physics and biology, complexity science is developing new ways of thinking about networks and the agents within them. Needless to say, work is now being done on looking at the markets as networks (pretty obvious really) and the nature of the agents operating in the networks (anything but rational I hear you say).
One example is the concept of “Super Spreaders”, these are highly connected agents or nodes in a system (the term comes from research into the spread of viruses) and how we can analyse their impact on the network. The clever part of complexity is the analysis can point out connections that hitherto remained hidden, or their vital place in a network was overlooked. A good example of this may have been Lehman Brothers, a middling size firm in terms of capital, but with a disproportionate relationship in the markets due to its vital role as a prime broker. Did the regulators understand the super spreader status of Lehmans and the possibility of near catastrophic consequences that they unleashed by letting the firm go to the wall? In the post Lehman’s world – Central Bankers are now looking at Complexity Science – with the Bank of England for example producing papers and research on its relevance to systemic risk.
In the past ten years we have seen the growth of behavioural finance as a way of trying to explain away seemingly irrational market antics. But it looks increasingly as if that is only part of the story – the behavioural side may explain the motives of the agents but to truly understand markets we need to uncover the complexity within the networks.

So it’s time to start researching the implications of complexity in the markets. Some keywords to start with would include: Complex Adaptive Systems, Scale Free Networks, Emergence and Super Spreaders. If you don’t, you can be certain the competition will be soon.