Humans are terrible at trading. Evolutionary instincts, hard wired into our brains, make us rush into making bad decisions. Our grey matter is loaded with emotional baggage which leaves us predisposed to repeatedly making the same mistakes. Nobel prize winner Daniel Kahneman and his colleague Amon Tversky call these items of baggage cognitive biases.
They made sense when we were hunting woolly mammoths; but are positively unhelpful when we hunt for elusive profits in today's complex financial markets. These biases form the basis of the theory of behavioural finance. This theory explains why investors and traders often behave in ways which classical financial theories (that assume perfectly rational behaviour) cannot predict.
We believe the best solution is to hand over your portfolio to a systemwhich decides what, and when, to buy or sell.
But the process of creating and using trading systems is fraught with dangers. The same biases that affect us when we trade can also result in serious mistakes being madewhen designing trading systems. The result is a strategy which is heavily exposed to large losses.
Here are nine mistakes you should try and avoid when building your trading system.
The biggest mistake you can make is to be overconfident. People consistently over estimate their own abilities, both in absolute terms and relative to others. In the jargon of behavioural finance relative overconfidence goes by the catchy title illusory superiority. Feeling a sense of illusory superiority is extremely dangerous.
Studies frequently show that more than 90% of drivers believe themselves to be above average. It’s likely that 90% of traders, and those designing systematic trading systems, also believe they are in the top tier. Clearly most of those people are kidding themselves.
Overconfidence manifests itself in nearly all the other mistakes I’ve listed below. If you think you are better than the rest of the market you are more likely to trade too often and take too much risk, or to design a system which makes those errors.
According to market lore the very best discretionary traders are those who are humble enough to admit they are wrong and cut their position when it moves against them. The same humble attitude is necessary for those creating trading systems.
2) LIVING IN AN IVORY TOWER
Many people who design trading systems don’t come from a trading background, but from a scientific discipline, such as physics, mathematics or engineering. This can be a good thing, for a couple of reasons. Firstly they are more likely to be able to design robust automated trading systems. Also if you have been trained in the dark statistical arts then you should do a better job of fitting your trading system than a novice who is blindly using a piece of back testing software they do not understand (something I’ll talk about below).
However those who are scientific black belts but neophytes at trading are prone to making serious errors. Some of the biggest blow ups in trading history have been caused by extremely clever and well qualified people making mistakes. The meltdown of Long Term Capital Management in 1998 happened despite the fund having two Nobel prize winners on their staff. Derivatives backed by subprime mortgages were radically overpriced before they crashed in value in 2008, thanks to traders using a clever model created by a very smart guy with a Phd. Other examples include the quant quake of summer 2007, and the losses suffered in the Swiss France devaluation of January 2015.
In all these cases the rocket scientists had created a model which was a good approximation to reality most of the time, but ignored the very different dynamics of a market crisis which were missing from their data history. Experienced traders, bloodied by numerous market crashes of the past, are more likely to design trading systems that can cope with these extreme situations.
Other common screw ups by those short on practical experience include underestimating the costs of executing an order, and ignoring a critical element of market structure such as stock splits or short selling constraints.
A successful systematic trader will have both a good grasp of theory and a big dollop of market savvy.
3) OVER COMPLICATING
Rocket scientists have another fatal flaw –the tendency to over complicate. If you’re very smart then it’s tempting to think that to beat other people in the market you have to exploit your intelligence –after all that is the ‘edge’ that you supposedly have. Also creating a simple, run of the mill, trading system is far too trivial a task for someone with a PhD in signal processing or nuclear physics. Using your scientific knowledge to produce a wonderfully elaborate strategy is much more fun.
Over complication can also happen when you start with a relatively simple trading rule. After testing this you discover that it doesn’t perform as well as you’d hoped. So you adapt it, fine tuning it to improve its performance by adding some bells and whistles. A few more iterations and you have something that is far too complicated (This is also a form of over fitting; another mistake which I’ll discuss in our free ebook below).
The bad news is that complex systems are generally outperformed by simpler alternatives. Complexity is also bad because it makes the system opaque. A good trading system is predictable. If the market moves in a particular way, you should be able to predict roughly what your strategy should do. If you understand your system you are more likely to trust it, and let it run unimpeded.
Ready to find out more?
I hope you enjoyed/learned a lot from the points above. There are, in fact, 6 more mistakes you Must AVOID when using Systematic Trading Systems, and they can be found in our Ebook, which you can download right now, for FREE.