August 2, 2017    6 minute read

The Rise of the Quant Fund: It’s Not Only About the Machines

Man vs Algorithm #PGC    August 2, 2017    6 minute read

The Rise of the Quant Fund: It’s Not Only About the Machines

Hedge fund pioneers like Stephen Cohen can usually be trusted to spot a long-term fund management trend.

So when the founder of one of the preeminent hedge funds of the 1990s and 2000s, S.A.C. Capital, starts aggressively hiring quantitative specialists for his new firm Point72 Asset Management, while at the same time cutting his stock-picking team of fund managers by nearly two thirds, it is a pretty good guess that quant is here to stay.

Cohen’s move suggests that there is a paradigm shift going on in the fund management industry. Quant, or systematic, or “quantamental” strategies, where human judgement is replaced or augmented by data, algorithms, artificial intelligence (AI) and machine learning, are in vogue.

And this movement is permeating all aspects of the fund management industry – from pure quantitative strategies, where mathematical algorithms make all of the asset allocation or stock-picking decisions, to “quantamental” strategies, which combine the traditional stock-picking skills of fund managers with the use of data and computing power.

The Rise of the Machines

These various quant strategies are now being adopted by an increasing number of fundamental fund management firms in an asset management version of Hollywood’s “Rise of the Machines.” But although advances in mathematical techniques have driven this growing quant trend, there is another, more mundane, explanation and it is due to a different branch of science: economics.

The stats certainly back up the ‘rise’ part. Researchers at Barclays have calculated that assets run by systematic strategies hit a record $500bn by the end of 2016, making up about 17% of total hedge fund assets. Data from eVestment, a fund management data analysis firm, shows that funds that use quant models raised over $21bn in 2016, while other active hedge fund strategies saw $60bn of outflows.

And as for the ‘machines’, the basic concept of using AI and algorithms to crunch through billions of lines of data-sets and replace human fund managers is well established. Advances in the application of these have undoubtedly added to fund managers’ arsenals in their search for alpha or outperformance. 

Risk Appetite Spoiled

But it is simple economics – specifically, changes in investor risk appetite and fee structures – and not only the ‘machines’ that are driving assets into quant funds and this trend is forcing fundamental changes on the $3trn global hedge fund industry.

As high returns have been arbitraged out of the industry, the good old days of rock star fund managers and high fees have given way to more sober industry practices. Only the very best hedge funds can charge the old industry “two and twenty” fees (where clients pay an annual 2% of funds under management plus 20% of outperformance over a benchmark), and performance must be pretty good even to charge “one and ten”.

The asset allocation scene is driving these changes. Fund of funds, who feed assets to hedge funds, have shrunk and the main sources of capital now are the giant asset allocators, such as the California Public Employees Retirement Scheme (CalPERS), and fund consultants who are much more sensitive to a low-return environment.

These asset allocators are conservative. They are allocating less to higher return, higher risk and higher fee hedge fund products. But they still want the outperformance even though they have lost the appetite for risk and this is where the quant fund comes in.

The Machine Says Yes

Quant has become the catch-all term to describe the very different mathematical proprietary models and strategies that fund management firms now use to manage funds. The common aim of these strategies is to take advantage of the growth of data and eliminate human cognitive bias in the chase for returns.

And what these funds also provide is a perception of value and lower risk compared with the old hedge fund model. The replacement of lots of expensive human fund managers with computers, and a handful of scientists, results in lower costs and fees. Quant strategies use repeatable and sustainable analysis of vast data pools to manage large, liquid pools of funds generating uncorrelated returns, or alpha, that are managed effectively for risk.

Simply speaking, quant strategies are in the right place at the right time. Asset allocators, reticent to take on hedge fund risk, want products with reliable returns, lower risk and cheaper fees. Quant fits the bill and these alternative investment strategies are flourishing.

The quant space is in too early a stage to back-test long-term performance against active management, but that is beside the point. Quant provides an alpha-generation middle ground between expensive high-risk active styles and cheap index tracking ETF passive funds.


Quant strategies are now penetrating the whole fund management industry in a move that is different from the initial emergence of hedge funds 20 years ago.

At that time, new hedge fund strategies provided higher, non-correlated returns using new techniques such as shorting, hedging and the use of derivatives. A whole new generation of fund management firms – the hedge funds – emerged, and they could charge higher fees for a premium product that was distinct from the long-only funds run by traditional asset managers.

But this time around, it is the established fund management names that are leading the way in the development of quant funds. Some, such as London firm Winton which has over $30bn of assets under management, are pure quant. Others, such as the 30-year-old global active fund specialist GAM, who bought quant specialist Cantab in 2016, have built up a Systematic platform that sits alongside their discretionary products.

Many traditionally fundamental hedge funds are increasingly using a hybrid of the two in a further sign that quant is penetrating all aspects of the fund management industry. “Quantamental” techniques, which use data to track economic trends and company performance to look for inflection points and trading ideas for their human fund managers, have become an integral part of the strategies of many of the fastest growing hedge funds. As Anthony Lawler, the Co-Head of GAM Systematic says: “Flows into certain alternatives remain very strong as investors continue to seek diversifying returns and Quant is at the core of this current demand.”

There should be little doubt that quant has arrived when venerable old Schroders Asset Management, founded in 1804, has set up systematic funds with US quant specialist Two Sigma and the world’s largest asset manager, Blackrock, last year shifted $6bn of funds from active to quant strategies.

The machines are here. Now we will need to see whether the individual fund firms can crunch the data with their quant models to deliver the outperformance their investors want.

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