The relevance of an investments vs. a maths/quant background for quant strategies?

A client asked us an interesting question. When it comes to quant strategies what is more important for the core team that is building the quant algorithm – an investments or a maths/stats background?

In our view, while the investment background & pedigree of the investment team is one of the most important criteria we look at when evaluating active fund manager managed equity portfolios, when it comes to quant strategies our focus shifts to the mathematical/quantitative/data-scientistic/programming (or geek) credentials of the core team that is developing the algorithm.

The core factors that help drive value creation are well known even amongst non-investment professionals. Accordingly, a core data-scientist background of an investment team (such as Accuracap) does have an understanding of the core quantitative factors that need to be part of the algorithm that they are building out.

What is important is the ability to take various forms of disparate data, clean it up, figure what is relevant or not, study co-variances, predictability etc. in order to build on an investment algorithm that is meant to (at least in theory) do as well, if not better, than a human fund manager.

This deep-dive into data requires a much deeper level of deep mathematical skills, than what typical fund managers typically have. This is why, globally pure quant strategies are typically run more by math geeks, as compared to investment professionals.

The criteria we use at IME Capital, to judge an investment teams pedigree accordingly varies when it comes to pure quant strategies.