Back in the day, a quant in finance could devise a strategy, sit back and let the money roll in while lounging about in a silk robe with a fat cigar. Such are the halcyon dreams of the contemporary quantitative finance type who finds him/herself forced to grind continuously in front of a screen in search of illusory alpha while every man/woman with a piece of Python code does the same.
This wasn’t the exact complaint at today’s Quant Conference (held digitally this year), but it came close.
“The barrier to entry is lower and the barrier to success is much higher,” said Boris Lerner, the Global Head of Quantitative Equity Research at Morgan Stanley, of today’s quant strategists. “You don’t need to write your own sophisticated tools – there are plenty of off-the-shelf Python packages and complex machine libraries you can use.”
As competition has increased, Lerner said quants are being forced into a cycle of perpetual innovation, and are being compelled to look for returns in ever more unusual places. “Strategies that were more persistent in nature a few years ago are harder to make money from today,” he said. “You have to keep rethinking research, to go out of large asset classes like equity and into smaller and less liquid asset classes like crypto…”
When quant traders push into less liquid areas of the market, Lerner said concepts like market microstructure, which help predict how particular securities trade, are becoming more important. Market microstructure is something that Michael Steliaros, the global head of quantitative execution services at Goldman Sachs, has also been seen talking-up in sessions with students, and seems the sort of quant expertise to focus on right now.
While all of this is taking place, quants also have another issue on their plates: data is stealing some of their glory. Nowadays, the success of a quant strategy is largely a product of the quality of your data, and the quality of your data is partly determined by how much you pay for it.
“Data is expensive,” said Lerner. “Successful quant investors make large investments in data and also large investments in people.”
If you’re a quant who receives a dwindling bonus after working relentlessly to tweak your models, the cost of data might therefore be one reason why.
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