img src=”http://photos1.blogger.com/img/43/5843/160/thinking.jpg”br /br /emfont color=”#0000FF”(P.S: Sorry for any disturbances the advertisements above may have caused you)/font/embr /I like reading the Review segment of the Straits Times because there are occasionally interesting analysis, and Andy Ho sometimes has interesting articles. This guy writes extensively on a variety of topics ranging from health to science to philosophy to political systems to economics … all of which interest me (save maybe for health). Saturday’s article in ST is the inspiration for this latest blog entry.br /br /I had read a book on chaos theory some years back, which is the subject of Andy Ho’s article, but had not really made the connection to stock market dynamics then, probably because my market experience then was not too extensive for me to make that connection. But some years later, on reading this article, it struck me how economic and stock market behaviour can be described by chaos theory’s derivative, complexity theory (if it can even be called a theory, since there’re no real quantitative equations).br /br /The story goes that classical mechanics can describe the interaction between two bodies, such that if given a set of initial conditions (eg. position, speed) for both bodies, the equations can predict their future trajectories for all eternity. This is known as a deterministic system (ie. its future is predictable). However, things get much more complicated for a system of emthree/em bodies. Where initial conditions vary by a little bit, the subsequent behaviour of the system can vary by a lot. This finding forms the basis for complexity theory, which attempts to describe complex systems.br /br /Imagine what happens when you have n bodies! The system will be too difficult to characterise, it appears at first glance. The first thought that comes to the stock market player’s mind will be the parallels to the stock market where you have n millions of players interacting with one another.br /br /However, one of the key findings of complexity theory is that, rather than throwing our hands up in despair, it is possible to find so-called “emergent” properties within such systems. Basically the idea is that although “their futures cannot be predicted, such systems exhibit patterns, so their stability is bounded” (I couldn’t have described it better than Andy Ho, hence the quote).br /br /The example of the graceful flocking behaviour of birds is given, where even though there is no preconceived coordination the herd movement appears elegant when viewed as a whole. In fact, the only rule that has to be followed individually is for each bird to keep constant the distance between itself and the one in front while flying in the same general direction. Extending it to anthropology, the way that people congregate in urban areas and interact gives their cities emergent personalities ie. small-scale interactions among many individual parts can lead to large-scale order.br /br /You can see where all this is going. If we characterise technical analysis as an attempt to identify such emergent patterns within the interaction of millions of bodies ie. a complex system, it may not be too far off the mark. Note the word —attempt — that suggests discretionary judgment and interpretation and hence practitioners should always remember to exercise prudence in implementation.br /br /I used to be more sceptical of technical analysis, primarily because of the lack of true scientific logic and its lack of quantitative rigour. My doubts were expressed in an earlier writeup in 2006 on the subject — a href=”http://mystockthoughts.blogspot.com/2006/07/technical-analysis.html”Technical Analysis/a — where I attempt it describe it as a technique for the short-term while fundamental analysis is more effective for capturing market-beating returns over the longer term. I also expressed my views that since the market often priced in breaking news swiftly, it tended to be efficient over the short-term and hence it was more advisable to assess companies fundamentally and position for the long-term.br /br /Today I’m not so sure. First reason: market volatility has increased since mid-2008, as described by the VIX index which shot up from its traditional 20s to as high as 80-90 (it is currently in the 40s). In the past reversal signals were triggered whenever the VIX breaks 30 …. so this current situation is out of the ordinary. A volatile market becomes a short-term oriented trading market, so anyone hoping to make money may have to pay more attention to TA. Secondly, one cannot fail to notice how simple TA rules would have got an alert investor out of the carnage in late 2008, or even early 2008. First example — the double top pattern, which was exhibited in most stocks and all major country indices that I know of, would have signalled an exit in late 2007 right at the top. On retrospect, it stares at us right in the face. Second example — moving averages — once all the short-term averages for the SP started going below longer-term averages one after the other in Sep 08, what followed was a rapid collapse that spread across the globe. Just two very simple TA indicators …. and the rules for interpreting the signs they carried were established long beforehand, and not on hindsight.br /br /So how do they work? I used to think the self-fulfiling prophecy provides the major mechanics that describes how TA drives the market; that may well be true as its acquires a critical mass following who act according to its signals. But this is not the sole mechanism. If we go back to the example about flocking behaviour, simple trading rules observed by market players can result in patterns that repeat themselves time after time as graceful emergent behaviour —- which is then lumped together under TA as chart patterns. For example, practice of simple cut-loss rules individually, eg. cut at 10% loss, can drive its own momentum that is naturally reflected in dips of near-term prices below their moving averages and provides a TA sell signal. Likewise, the likely behavioural pattern that culminates in a classic double-top signal can be characterised by many individuals thinking to themselves “damn, failed to sell it at the high the first time, next time it goes back up I’ll make sure to sell” ….. hence resulting in strong selling resistance the second time round leading to a double top that never recovers. Thus, individual behaviour that follows simple rules, just like the flocking example, can lead to a herd behaviour that seems coordinated when viewed from the outside, as described by complexity theory. There is this saying “history never repeats itself, but it rhymes” which further illustrates how a study of the past and of TA patterns can foretell likely herd behaviour.br /br /And of course, coming back to the example of the 2008 collapse, there is a third reason why short-term price behaviour is extraordinarily important: reflexivity theory is extremely relevant when the critical issue is banking, whose assets are marked to market and hence solvency is market-dependent. For further reading, here is my article “a href=”http://mystockthoughts.blogspot.com/2008/11/reflexivity-revisited.html”Reflexivity Revisited/a”.

Last 5 posts by DanielXX

• How To Make Money in Stocks Part 7: Pick Low-Hanging Fruit - May 15th, 2009
• Thanks Thailand, you screwed up the show again - April 12th, 2009
• On Temasek Holdings - February 8th, 2009
• Developing An Investment Philosophy Part 5 - January 4th, 2009
• Reflexivity revisited - November 29th, 2008