In 2020 and 2021 quant funds have had a tough time of it, as algorithms struggled to navigate the whipsaw conditions created by the pandemic and has managers revisiting the Person + Machine question. The pandemic has led to significant dispersion of returns, with many household names including Bridgewater Associates, Renaissance Technologies, Winton Capital and AHL Dimension posting negative returns through May 2020. Some quant funds shut down altogether, including Philippe Laffont’s USD350 million quant fund at Coatue Management.
Unlike humans, algorithms lacked the ability to think creatively and imagine what ‘could’ happen amidst the chaos of the pandemic.
Michael Weinberg is Managing Director, Head of Hedge Funds & Alternative Alpha at APG, a leading Dutch pension provider. He is also Adjunct Professor of Economics and Finance at Columbia Business School.
In his view, the overall dispersion of returns, not just in quant funds but hedge funds more broadly, showed that alpha can be generated through manager selection. “Last year was a great example of that where you had some quant funds who were up materially while others were very publicly down materially,” Weinberg says.
He attributes the poor performance of quants to the fact that securities that had previously been low beta suddenly became high beta (i.e. consumer product companies), which had an adverse impact on quantitative models. “They hadn’t forecast this would happen, or couldn’t forecast it, and quant managers had a choice to make: either to take their models out of commission or change their model assumptions,” continues Weinberg.
Weinberg spends a lot of time looking at AI-focused investment strategies, also known as Autonomous Learning Investment Strategies (ALIS).
These are strategies that combine big data, data science and machine learning techniques, which, coupled with ever more powerful and cheaper computing power and storage, are pushing the envelope in terms of what is possible in quant trading.
“One manager we know has built a quant AI fund strategy specifically around causality. The strategy is based on a confluence of statistics, probability, calculus, and elements of physics. They believe their AI model will determine a lot of its decisions in a way similar to the way the human brain makes decisions, but with less opacity than using neural networks.”
“Nevertheless, we still firmly believe in ‘person plus machine’. In our experience, those strategies where the coders and the portfolio managers have left the machines to do everything themselves, may have succeeded initially but over time they generally don’t. We would expect the dynamic to continue to be person plus machine.”
Scientific Financial Systems has designed its flagship product Quotient to solve this dynamic. We give our users control over their models and allow them to explore their creativity. We keep the person and the machine working together in harmony.
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