Quantitative strategies encompass a wide variety of styles, markets, and philosophies. Back in the 80s, quants were defined by the application of computing power and data crunching — think Moneyball by Michael Lewis.
Today, firms labeled quant most often invoke images of high-frequency trading, application of artificial intelligence, and opaque black boxes.
Relative to fundamentals strategies, which most investors could easily relate to, even Winton Capital, a leading USD22 billion CTA quant firm founded in 1997, continues to struggle with its quant image.
“The great and the good, you know, are always on about fundamentals. In some way, we are slightly, you know, morally suspicious for looking at numbers.”
Winton’s approach, he adds, “always seems to end up being positioned as ‘Wow, that’s really wacky.’ ”
Dave Harding, founder and CEO, Winton Capital
A recent Financial Times opinion piece “A qualitative guide to the quants” (subscription paywall) is an excellent window to the world of quant strategies.
The author Jane Buchan, chief executive of Martlet Asset Management, describes three broad categories of quants:
- Fundamentals on steroids: Quants applying increased computing power and data at a larger scale relative to the traditional fundamentals approach.
- AI and machine learning: Application of big data and complex algorithms rather than economic theory.
- Fund of funds of individual algorithms: Combining dozens of different styles and anomalies to whatever makes money on a risk-adjusted basis.
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Of the three, AQM is closest to the third category.
AQM consists of 33 individual algorithmic trading models. Each trading model is a sub-set or variant of six classes of time-tested investment strategies.
A good way to understand AQM’s approach to trading models is to reference this Primer on Trading Model Diversification.
Similar to funds in the third category, AQM’s innovation is to blend trading models with different, uncorrelated return-to-risk profiles in order to produce consistent returns.
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The author suggests four questions to ask of quant managers:
- Robustness of the investment process: Interpretation of past events and data for future patterns;
- Distribution of returns: Whether a few positions contribute to outperformance;
- Scalability: The size of the firm versus the capital base; and
- Reliance on equilibrium: Degree to which the strategies involve purchasing securities that are “mispriced” and waiting for them to return to equilibrium.
These are worthy and important questions for any hedge fund investor interested in looking beyond the cosmetics of recent past performance to ask.
