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Why quants and traditional traders need to get into bed with each other

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Get the right mix, not a Frankenstein of trading.

Previously the old-school fundamental traders were seen as a separate species from quantitative traders. There was no middle ground between quants and traditional traders – you were on one side or the other. However, that is changing as more fundamental traders are implementing big-data analysis and other quantitative methods, while an increasing number of quants and systematic hedge funds are realizing that certain fundamental philosophies and techniques have value as well.

“The future of trading is more of a true hybrid between quantitative – Mr. Spock – and fundamental – Captain James T. Kirk,” said Rosemary Macedo, the chief investment officer of QS Investors, which was the quantitative strategies group of Deutsche Bank that was spun out and later acquired by Legg Mason. “A lot of fundamental firms have adopted quantitative practices to take advantage of tactics that were successful, and quants have adopted some fundamental practices,” she said at the Trading Show Chicago 2016.

When blending the approaches of quants and traditional traders doesn’t work 

That said, not all combinations of the two schools of thought have been successful.

Qualitative stock picking with an overlay of mean variance optimization portfolio construction and quantitative screens with a qualitative filter for securities selection are two examples of what Macedo calls “quanta-franken-mental,” referring to the bolting together of the two approaches, as opposed to true integration.

“You can’t use fast-moving quant models for screening ahead of time-consuming qualitative analysis,” Macedo said. “As another example, when firms have two teams, one that makes qualitative picks and the other using a quantitative model, they select the overlapping picks from the two separate processes. That’s not the best of both worlds either; it’s an attempt to offset risk,” she said.

Smart beta

Smart beta, which involves quantitative stock-picking and rules-based portfolio construction avoiding a consideration of cap-weigh, also falls under the “quanta-franken-mental” category, in her estimation.

“Smart beta pales in comparison to the opportunity set if you have information of when value works, and when do you need low volatility, momentum, low size, high yield or quality,” Macedo said. “The big data revolution is amazing, and traders should take advantage of it.

“The market is creating tremendous opportunities for a true hybrid ‘quantamental’ approach” she said. “The stock market has different drivers at different times, and new factors are proliferating.”

Timing is everything

There is much company-specific information that used to be available only to large research teams that went and kicked the tires on companies, but now that data is available to quants.

“Taking characteristics that you believe to be meaningful that fundamental traders have used for years, you can do better when you pay attention to what works when, which I call smart alpha,” Macedo said. “Figure out the reasons and conditions that make factors work so you can figure out the timing.”

There are proliferating factor options, but traders are faced with the same old dilemma: When do you want to actually hold securities based around these factors? It’s all about knowing which ones to own when.

A true “quantamental” hybrid would combine fundamental insights into stock-selection factors, the current investment environment and their complex interactions into empirically validated insights that traders systematically apply.

“We can collectively do a better job engineering specific investment outcomes to help institutional investors as well as individual people so they have enough money to spend in retirement,” Macedo said.

Using Big Data 

Venture capital funding of financial technology startups is driving the pursuit of “automated truth from data” and fostering the growth of the big-data ecosystem.

As Silicon Alley brings fintech into the mainstream, new opportunities await ambitious, forward-thinking traders and fund managers, while those who stick to business as usual will be threatened by disruptions.

Michael Beal, the CEO of Data Capital Management, which he describes as a “news-aware systematic hedge fund,” gave the Day 2 morning keynote address at the Trading Show Chicago 2016. He has a B.A. and MBA from Harvard, experience working as an M&A investment banking analyst at Morgan Stanley and deal associate at TPG Capital, and co-founder and head of strategy and finance of the big data business at J.P. Morgan before breaking away to start his own firm.

Beal mentioned that Data Capital Management has a quantitative tilt, and he hired people with quant backgrounds, but that he himself comes from the fundamental trading school of thought, and it’s important to glean insight from both.

“In essence all professional investment managers share the same process to make decisions: data acquisition and analysis to inform the investment decision,” Beal said. “It’s important to have breadth of novel data sources, depth of information and speed.

“Big data is a disruptive force in financial investing due to the exponential growth in asset-price-relevant information,” he said. “The volume of data out there is mind-boggling.”

Photo credit: Dan Butcher

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