My past few posts have been driven by an underlying question that was pointedly raised by someone in a discussion group I follow on linkedin (if you’re curious, this is a Quant Finance group that I follow due to my interest in autonomous agent design and the question was posed by a hedge fund person with a Caltech PhD and a Wharton MBA):
I read Ernest Chan’s book on quantitative trading. He said that he tried a lot of complicated advanced quantitative tools, it turns out that he kept on losing money. He eventually found that the simplest things often generated best returns. From your experiences, what do not think about the value of advanced econometric or statistical tools in developing quantitative strategies. Are these advanced tools (say wavelet analysis, frequent domain analysis, state space model, stochastic volatility, GMM, GARCH and its variations, advanced time series modeling and so on) more like alchemy in the scientific camouflage, or they really have some value. Stochastic differential equation might have some value in trading vol. But I am talking about quantitative trading of futures, equities and currencies here. No, technical indicators, Kalman filter, cointegration, regression, PCA or factor analysis have been proven to be valuable in quantitative trading. I am not so sure about anything beyond these simple techniques.
This is not just a question about trading. The exact same question comes up over and over in the domain of robotics and I have tried to address it in my published work.
My take on this issue is that before one invokes a sophisticated inference algorithm, one has to have a sensible way to describe the essence of the problem – you can only learn what you can succinctly describe and represent! All too often, when advanced methods do not work, it is because they’re being used with very little understanding of what makes the problem hard. Often, there is a fundamental disconnect in that the only people who truly understand the sophisticated tools are tools developers who are more interested in applying their favourite tool(s) to any given problem than in really understanding a problem and asking what is the simplest tool for it. Moreover, how many people out there have a genuine feel for Hilbert spaces and infinite-dimensional estimation while also having the practical skills to solve problems in constrained ‘real world’ settings? Anyone who has this rare combination would be ideally placed to solve the complex problems we are all interested in, whether using simple methods or more sophisticated ones (i.e., it is not just about tools but about knowing when to use what and why). But, such people are rare indeed.