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What Happens When Everyone Is a Quant?

 6 min read / 

“No man is better than a machine. And no machine is better than a man with a machine.”

Those are the words billionaire investor Paul Tudor Jones used to rally his firm and prepare it for the continuous and evolving shift towards quantitative investment strategies. And he makes a great point.

The investment world is steadily moving towards a future where everyone will be a quant. But when you consider the amount of hype that quantitative strategies have generated over the last several years, it’s surprising that quant funds have generally underperformed traditional funds.

According to a recent report from J.P. Morgan, quant funds have returned 5.5 percent per year since 2003, compared to 6.4 percent for their traditional counterparts. And in the short term, performance has been even worse. During the first half of 2017, quant funds returned a mere 3.17 percent, while returns for traditional funds were nearly twice as high at 5.99 percent. That’s not a great showing for either strategy, considering the S&P 500 rose 8.2 percent during the same period.

Data is Winning the Day

Despite any proof that systematic investment strategies will produce superior returns – and if anything, providing evidence to the contrary – the market continues to steadily move in that direction. The quantitative hedge fund industry will surpass $1trn in assets under management this year, according to HFR. And J.P. Morgan’s lead quant estimates that passive and quantitative investing now accounts for 60 percent of stock trading volume, a number that has more than doubled over the past decade.

In the investment world, data is winning the day. And data from more sources is becoming available all the time. It’s a foregone conclusion that quantitative strategies will be a pervasive part of the investment business because there is unquestionably value to these mountains of data. But as an industry, society just needs to figure out better ways to find and unlock that value.

Short-term Struggles

The shift towards quant strategies will be a bumpy road in the short term, and this is being seen now. The technology that firms are trying to deploy is extremely complex and a major obstacle for an industry trying to find its way.

Cost is also a significant barrier to entry and firms trying to build their own cutting-edge infrastructure are finding that it’s not cheap. However, cost will ultimately be less of an issue if the technology lives up to expectations. What’s more difficult is managing the infrastructure and figuring out how to effectively use it.

For example, one of the most rudimentary steps of working with big data – cleansing it and getting it ready to use – is a major barrier to harnessing its full value. Analysts can spend as much as 70 percent of their time managing raw data, cleaning it and preparing it for analysis, according to the head of client analytics at UBS Wealth Management. This means only a fraction of their work hours is focused on their real job: extracting insights and guiding strategic decisions.

Since cost won’t ultimately be a major impediment, these struggles mostly point to a people problem. Quant funds employ dozens of engineers and data scientists in their attempt to find the hidden signals within massive data sets that the rest of the market isn’t seeing.

Even firms just testing quant strategies need at least some highly-qualified talent to get a start in the right direction. But with technical talent being in such high demand, financial firms are beginning to face a skills gap, especially because they’re now competing with Silicon Valley for the right people.

In addition to complex and expensive infrastructure and the challenge of finding people that know how to manage and use it, there’s a much larger foundational issue. The transition to quantitative strategies is placing more investment decisions in the hands of the technically-minded. While many of these people are capable investors, it marks a major shift in the way investment decisions have been made for hundreds of years.

This begs some important questions. For example, would one rather have technically-minded or financial and business-minded people driving investment decisions? And if engineers and data scientists are calling the shots based on mathematic formulas, how easy is it to find differentiated strategies that produce alpha? Also, how will algorithms in these complex systems perform against the subjectivity and unpredictability of human behaviour?

Success in the Long Run

We’re currently in a place where we know there’s value in data, but the technology needed to find that value is complex, there aren’t enough people that know how to use it, and those people are versed more in technology than finance. Taking a holistic look at those challenges, there’s one way to address them all together – by making the technology easier to use for everyone.

Remember the second part of Tudor Jones’ quote: “No machine is better than a man with a machine.” In the long run, traditional and fundamental managers need tools that will let them use data in ways that only their quantitative counterparts can now. This would solve the people problem, allowing firms to rely once again on business and finance-minded professionals while lessening the burden of competing for tech talent.

There are already instances of firms beginning to adopt this mindset and one of the world’s most successful quant funds, Two Sigma, is a great example. The Financial Times described Two Sigma’s approach as one where:

“…evolution, adaptation and idea generation all come from the human element. More specifically, it’s drawn from as diverse a set of human practitioners as possible.”

The best way to tap into diverse backgrounds and knowledge while still embracing data is by developing tools that will make everyone a quant. Advancements in technology make it easier to use every day, so that’s undoubtedly the direction we’re headed.

When everyone’s a quant and traditional investors have the ability to devise and test data-driven strategies easily on their own, we’ll start to find the returns hidden in data that the industry’s been searching for so desperately.

 

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