Cantab Capital – DEBUNKING THE MYTHS, original article here 


Dr Matthew Killeya dispels a few of the myths surrounding systematic investment.

Out on the road and back home in Cambridge we meet a lot of people and speak to them about systematic trading. The conversations are often interesting and overall it has been a joy to witness people respond positively to the systematic space over the years. Yet, in spite of our best efforts, we have seen a few myths spring up around our industry. So here goes my attempt to deal with some of the more persistent ones.


The first thing to say is that it’s certainly true that the systematic or CTA space and computer-based trading has expanded significantly in recent times. Perhaps this is not surprising given the similar expansion in computing power in that time. The amount of data and available computing is increasing all the time yet the fraction of the world’s capital invested in systematic firms from large institutions(1) is still small.
Systematic firms generally trade the largest, deepest markets. These are liquidly-traded futures and forwards markets, large cap stocks(2), and vanilla options markets. The key is not how large positions are, but how large you are as a fraction of the market volume. Systematic firms are very focused on transaction costs and liquidity in the assets they trade and allocations to these assets are scaled accordingly. Their focus on data and technology allows them to accurately estimate trading costs on any asset for their own trading and estimate — and control — costs of liquidating their own portfolios.(3)
There’s a lot more we could say about this one, but instead we direct you to our research paper on this topic. We’ll move on to our second myth to be debunked which is often conflated with the first.


The genealogy of our industry has seen several large funds spawn children.(4) Whilst this might give rise to concerns about similarities, splits often emerge because the founders of the new firms have different visions to those of their previous employers and these again are a force for differentiation over time.(5)
The best systematic firms are extremely active in research and development. They employ teams of scientists who research new strategies and analyse data constantly. Thus our products are constantly evolving and there is reason to expect further divergence over time. For example, satellite imaging data has only recently become generally available. This offers the potential to measure the amount of sunshine or ‘good growing days’ for various crops around the world. Augmented with crop production figures per geographic location, researchers can build models to predict crop yields and ultimately to profit on price moves.
Systematic firms are focused on the similarity issue themselves. They can estimate their similarity to other funds and quantify this risk. By building factors such as ‘momentum’ and ‘value’ firms can regress the returns of their peers against these factors to understand to first order what they are doing.(6) Neutralising or orthogonalising your own returns to limit your exposure to these factors is also natural. Statistics offers a large number of sophisticated and subtle tools to do this.(7)


In fact, systematic firms start from the same point as discretionary or fundamental trading: with a hypothesis. The hypothesis could be that interest rates of two countries predict the exchange rate between the two. It could be that the slow release of news or herding behaviour of markets — which after all are simply collections of people — leads to trends or momentum in markets. The key difference is that, given a hypothesis, systematic funds take the next natural step as scientists: they use the wealth of available historical data and available statistical techniques to learn about, test, validate and refine the hypothesis. And once this process is complete — and only then — they use it to invest.
One limitation is that hypotheses are only useful if they can be legitimately tested by data. If one cannot hope to test the validity of a hypothesis, then it is discarded. This is no bad thing.


People are comfortable flying in airplanes, relying on satellite navigation and using smart phones. All these use complex mathematics and technology that most of us cannot hope to fully understand. However, most of us are prepared to risk our lives in an airplane that we cannot fully understand.(8) Paradoxically, people can baulk at systematic investment that has a degree of complexity.(9)
Einstein (10) said “everything should be as simple as possible and not simpler”. This sentiment holds true with systematic investment. We apply a principled hypothesis-based and disciplined approach and carefully construct a process to manage billions of dollars.
That said, we do not over-indulge in complexity. We follow the statistical doctrine of searching for the simplest explanations and models to explain the data. The reason is simple: simpler models have a lower likelihood of falling foul of ‘over-fitting’ and thus a higher likelihood of realising profit in the future.

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