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Quant funds make up lost ground after the crash, part 2

Quants | Quant funds make up lost ground after the crash, by Anthony Harrington, part 2 Anthony Harrington

With quant funds on the rise again – and this time looking a lot more resilient than they did before the crash of 2008 – what is different this time round? Actually, as Janet Campagna, CEO of QS Investors points out, before we get around to answering this question we should note that it is actually unfair in its formulation. Not all quant funds have had to return to and reinvent their models. Some did just fine through the crash because they were using rather different approaches to the statistical arbitrage techniques that dominated quant fund model making through the boom years to 2007. Some of QS’s funds, for example, performed exactly as they were designed to do through the worst of the market downturn in 2008, turning in performances that were between 2% and 4% over benchmark.

Of course, with the benchmark falling 33%, year on year, that would not have been much comfort to investors, but this is exactly what a relative return fund should do. You can’t blame the fund for doing what it sets out to do. Decisions about whether it is smart in a downturn to leave investments in a relative return fund clearly have to be made at a level outside the fund itself.

Opening the portfolio

With investors still skittish and requiring some coaxing and soothing to commit to quants again, a number of the quant shops are being much more forthcoming about what their models are doing. They won’t generally tell all, but they’ll give away enough to enable investors to get comfortable. It is almost impossible in a short blog to give a feel for how different quantitative techniques produce very different results. QS has an excellent brochure on its Portfolio Choice approach to portfolio optimisation, which is worth reading (just click here). INTECH, another mathematically-based fund, also provides a detailed account of its methodologies. Those interested can read INTECH founder Robert Fernholtz's original academic paper on stochastic portfolio theory (see Part 1) on the INTECH web site.

To provide something of a flavor though, QS, which has $13 billion in funds under management, defines the difference between itself and mainstream quant approaches by its ability to incorporate investor and market views into its model and to use these to shape portfolio selection. It can then generate multiple alternative portfolios from the same broad universe of assets and analyze those for their fit both to investor’s return goals and to investors’ tolerance for the probable shortfall associated with particular return goals on assets at various levels of risk.

As Campagna explains, this level of detail used to be completely intractable mathematically, but advances in computing power and in algorithms have enabled QS to make huge advances on stock picking through mean-variance analysis and then to add layers on top of that to reflect the views, again mathematically derived, that the firm is taking on where market conditions are at any particular point in time.

Volatility and the quant

Being able to blend a complex analysis of risk factors together with market views makes for a much more responsive model. Campagna cites as an example the fairly sustained period of low volatility that characterized the market a few years ago. Investor columns in leading magazines and newspapers were talking about “the death of volatility” (large up and down price movements). Mathematical models drawing on the huge data sets from this time would have taken low volatility as a defining feature. QS looked ahead to a point where there would be an inevitable return to high volatility and built a separate model that would be much more sensitive to conditions of high volatility. This was tested and “put on the shelf” until the company’s other analytical models told it that it was statistically probable that increasing volatility would be the new norm. At that point the model was incorporated and helped to generate continued outperformance. This is very different from “hand tweaking” a model to make it fit reality a little better and illustrates how quants incorporate human judgement into the loop. Someone has to decide that the time has come to give the higher volatility model a run, but that decision is made on the back of model driven analysis, not human emotion.

Time – or in other words, the next vicious downward spiral – will show how well the spectrum of quant funds have learned from the global crash. Right now, things seem to be looking promising...

(For a recent article on quant funds during and after the crash for IPE Magazine's June 2011 edition: see

Further reading on investing and quants:

Tags: hedge funds , investing , investments , IPE , mathematical modelling , portfolio , quant funds , quantitative investment , quants , volatility
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  1. omvco says:
    Wed Jun 08 15:43:12 BST 2011

    A very interesting article. We are trying to go about the problem in a way that is different to the quant process pre- (if not post) 2007. Using web 2.0 technology we believe that a “crowd” of experienced modelers collaborating in a scalable and controlled wiki environment can produce "best practice valuations" not beholden necessarily to traditional practice but intent only on using the most robust yet sensible process for determining securities values. Modelers will have a financial "stake" in the business tied to the portfolio size of assets being valued. What's more, clients will be assured that valuations are truly independent, well vetted and open to their inspection and verification. AMackenzie

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