Major players in the systematic investment world are attempting to raise awareness of the blind reliance on “black box”, data-led models, which has led to previous fund collapses and other serious problems for quantitative investors.
The idea behind such strategies is that investment decisions are based on disciplined statistical treatment of various sources of information ranging from fundamental to behavioural and market data, thoroughly back-tested using historical data to measure performance, and executed systematically. This means taking away the subjectivity of discretionary managers.
But quant models need to be based on sound financial ideas to work well, and quality of data needs to be checked and assessed regularly, making the judgemental input of the quant fund manager a key factor.
“Quantitative techniques are a useful toolbox to test statements that people make about financial markets, but they usually hardly reveal any hidden reality,” says Nicolas Gaussel, CIO of the quantitative management team at Lyxor Asset Management.
“Letting the data talk,” as some claim, is dangerous. There may for instance be a correlation between variables such as the temperature in winter and stock price, without any real cause-effect relationship between them.
The value of this investment style lies much more in the team itself than the programme, believes Mr Gaussel, and it is paramount the model adapts to the changing market environment.
Quant managers can be naïve and fall in love with their models, believes Oscar Vermeulen, director at multi-management firm Altis Investment Management. This attitude played its part in the quant crash of 2007, where quant managers, deceived by the homogeneous and consistent period started in 2003, were betting on the same underlying momentum factors and did not understand the demand for market liquidity was closely correlated to the positive momentum in the market.
When some big players cut their positions, there was a knock-out effect on all the others. “Many big quant teams, made of tens of PhDs, had popped up at the most prestigious firms in a matter of a few years, but they had “never lived the markets”, he says.
However, he believes today’s market presents a good environment for investing in quant strategies, as a quant manager’s track record is made and tested under the “Goldilocks” period of 2003-2006, the credit crunch and crisis of 2007-8 and the recovery after dislocation of 2009-10, which gives a fund selector more scope to see how a model performs in different markets scenarios.
Also, today’s markets are much less “efficient” than before, with real dislocation in credit, and a huge dispersion in valuation in investment grade credit, which should prove a more rewarding hunting ground for quant modellers than the very homogenous, low spread market of 2006. Moreover, the incredible growth in passive and semi-passive investing over the past 15 years, up from almost nothing to 50 per cent of the cake, means index constituents are valued at least partly on momentum, not just fundamentals.
