Guest article written for AllAboutAlpha.com – the official publication of the Chartered Alternative Investment Analyst (CAIA) Association
originally posted at: http://allaboutalpha.com/blog/2011/08/04/alpha-hunter-the-dna-of-financial-markets/
The discovery of DNA’s structure revolutionized our understanding of how the human body works, revealing the source of our biological successes alongside the true sources of previously ‘unpredictable’ diseases. Our current ‘mainstream’ understanding of financial markets is rooted in thinking akin to pre-genetic biology- and many researchers around the world are now putting together theories and models to show the true structure of ‘how’ financial markets really work.
Professor Neil Johnson (Head of the inter-disciplinary research group on Complexity at the University of Miami, Physics Dept) is leading the change in our understanding. In this interview, we talk about his insights into financial markets:
Vikas Shah: Can you explain the concept of financial markets as being a ‘complex system’ ?
Professor Johnson: Let’s imagine that we’re all looking at a market that was literally the result of a coin being tossed.. Regardless of the ‘kind’ of bets we place, we’re not actually affecting the outcome of the coin. The opposite, of course, is true. The actual actions of the people in the market are the things that determine what happens next within the market.
In scientific terms, a ‘complex system’ has some key characteristics. Firstly you would find lots of interacting objects (which is absolutely true of a financial market), with lots of interacting agents doing different things (they don’t have to be homogenous, they can be very different in terms of what they want, and what they are doing)…. Secondly there should be some kind of feedback (again, absolutely true in financial markets) insofar as people respond to what they, others and ‘the price’ has done in the past… and thirdly, there should be some kind of adaptation (again, absolutely true of financial markets) insofar as people tend to continue doing what is successful and change what isn’t…
Those ingredients also exist in the market, and one would therefore expect the market would exhibit a diverse range of behaviors- many of which you wouldn’t expect or foresee from just thinking about what one rational or irrational agent was doing.
Vikas Shah: How do collective behaviors and emergent properties fit in our understanding of financial markets?
Professor Johnson: The ‘standard’ financial markets model is, effectively, based on a coin toss. That is how derivatives are priced and how every exotic financial instrument (at a deep level) is seen- they [these instruments] are very fancy coins, where flipping creates price changes. If you think about that- it seems very far from capturing the collective effect with the feedback and heterogeneous behaviors which actually exist…
Vikas Shah: How can this change in our understanding affect strategies?
Professor Johnson: In my own research, we build models of how a collection of heterogeneous agents might, indeed, produce price dynamics once we consider their diversity in terms of strategies. We then compare this to the actual market. What we found is quite interesting. It’s absolutely true that a lot of the time the market is indistinguishable from a coin toss- and we all know that… It has ups and downs… and for all intents and purposes, a coin-toss model is OK. However, when the system is under some stress, when the market is undergoing changes, the coin toss model just doesn’t work. The ‘black swan’ doesn’t appear very often in coin-toss land but in agent based complex systems land, it appears quite a lot. It doesn’t just appear out of the blue and disappear- there can be signature associated with these large changes, almost like a taxonomy of large changes in the way that price relates to volume… the way we see calm before storms… and so forth. In the multi-agent model, if you feed in the real price of the market, they show that before the large dives in market prices…. looking at the strategies played within the market… the crowding that was beginning to happen…. you could see it behind the scenes, even though you couldn’t see it in the price. As you feed the price into the model… you then look for a moment when the model gives a prediction of the movement of the market over the next timeframe.
Although it’s quite hard to get any unique ecology to identify with the price- you can create a class of behaviors consistent with the price, and give some kind of definite direction.
Vikas Shah: Assuming we now adopt this understanding of markets as a complex system, does it change our perception of risk and the ‘elephants in the room’?
Professor Johnson: This is not just about the elephant- it’s about there being different types of elephants. For example, if we think about ‘the black swan’ as an outlier in a distribution. All those distributions you see of price changes are taken for a fixed time interval- over a day, for instance, which gives you a bell-curve. When we have large changes in a market, they might last an hour… a few seconds… a day… a month… there’s no fixed time over which they happen! Looking at the market in simplistic distribution perspectives misses the true effect, threat and risk of large movements and upcoming changes. If you use a complex system approach which doesn’t have a fixed period of time in the model- it enables you to start exploring in the computer, and mathematics, what types of animals you will see! Is it a black swan? a black swan with two heads? it’s classifying the elephants in the room if you like… it may not tell you which one will come, but it will give you a better idea of what is out there in terms of risk and what should be mitigated against.
So what does this mean for investors?
Moving toward a ‘complex systems’ understanding of financial markets provides astonishing opportunities for both risk-managers and investors. Risk managers can use this thinking to appreciate the sources and significance of left-tail risk contained in their portfolio- while investors can use complex systems models to look for the (typically higher return) opportunities created by trading large ‘outlier’ market events.