How can we best describe financial markets? Perhaps in technical terms, as efficient exchanges of credit, assets and liabilities, etc. Or in more human terms as huge engines of emotion, herd behaviour and the ever-present drivers of fear and greed.
Some hold that financial markets are best described as simple but not easy – the implication being that they are relatively straightforward to explain but extremely hard to predict. Perhaps a better approach would be to adopt a different way of thinking and say that the markets are complex but not complicated. This may seem a pedantic difference but in fact these terms are chosen carefully and have a precise meaning.
To illustrate what I mean let’s take an opposite example – the analogue wristwatch. A watch is complicated but not complex. When we open it up there is a whole heap of complicated gears and springs, but its outcome or objective is simplicity itself – it just tells the time. Now when we look at markets the machinery is pretty simple (in basic terms the activities of buy/sell, borrow/lend, now/later pretty much cover 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 research from both physics and biology, complexity science is developing new ways of thinking about networks and the agents within them. Needless to say with even a cursory look at finance and markets, the idea of interlinked networks and a vast number of agents is instantly recognisable. So what are the complexity ideas that may help us better understand the machinery of finance?
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 had remained hidden, or the vital place of which 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 to the markets due to its vital role as a prime broker.
Did the financial regulators understand the super spreader status of Lehman and the possibility of the near catastrophic consequences that they unleashed by letting the firm go to the wall? Arguably traditional approaches to risk could not have uncovered these issues and the area of systemic risk looks to be a very rich field for complexity analysis. In fairness, post the Lehman debacle, central bankers are now looking at complexity science – with the Bank of England producing papers and research on its relevance to the overall financial system.
In the past ten years or so there has been a surge of interest in the field of behavioural economics, which seems to offer a way to explain the activity of seemingly irrational markets. Whilst this has opened many interesting lines of research it really only addresses part of the issue. The behavioural side may explain the motives of the agents in markets, but doesn’t adequately explain the network effects of their activities as a whole. This is where complexity analysis will help fill in the current blanks in our understanding.
To find out more about Complex Adaptive Systems a good way is a web search with these terms: Complex Adaptive Systems, Scale Free Networks, Emergence, Transfer Entropy and Super Spreaders. Interest will grow rapidly in this field and I expect to see many practical applications developed in the near future.
Gerald Ashley is a consultant, speaker and writer on Risk, Uncertainty, and Decision Making.