Oil and water. Yankees and Red Sox fans. The Sharks and Jets. Quantitative and fundamental trading.
Some things just seem destined to never get along.
However, when it comes to the latter, at least, some folks are dead set on bringing the two sides together.
That comes after quant funds were rocked by unprecedented market volatility in March, and as some experts have advocated for computer-driven strategies to shift towards trying to react to data in real time as opposed to relying on models and forecasts.
On the surface, the two strategies — quantitative and fundamental — are worlds apart. Quantitative approaches focus on applying data and models to find characteristics of swaths of companies worth investing in. Fundamental strategies, meanwhile, are about analyzing and understanding key metrics about individual companies.
But in hopes of getting the best of both worlds, Wall Street has looked to combine the two strategies. "Quantamental" funds are hoping to bridge the gap and get the most out of humans and machines.
"It's a leveraging of fundamental expertise to analyze data where it matters most," said Carson Boneck, chief data officer at Chicago-based Balyasny Asset Management, about the appeal of merging the two sides. "This is the real art that's combined with the science."
Boneck, who spoke on a webinar hosted by alternative data provider Thinknum on Thursday, explained the key considerations firms need to keep in mind when looking to combine quant and fundamental strategies.
Described as somewhat of a "religious debate" between quants and fundamentalists, Boneck said it's important to remember each side might not appreciate what the other has to offer. For example, the fundamental team may have no interest in reviewing t-statistics, while a quant team will have no desire to look into an individual company.
That's why it's key to be open and transparent, Boneck said. Whether it's a matter of recruiting and hiring, or simply talking over ideas, it's best to always be willing to share. Boneck said he can appreciate how that might be a tough pill to swallow at times considering how serious Wall Street takes IP and secrecy.
"At the same time, when looking at data and building things at scale, those things happen best in cultures in which there is a lot of trust and you can share certain ideas," he added.
Openness is also key when it comes to where to put resources. Teams need to be clear about what they are trying to accomplish. There's no need to hire an analyst with a PhD in machine learning if your goal is to have someone write a quick Python module to transfer data from 50 Excel sheets onto one.
To that point, Boneck said having people to act as "translators" to bridge the gap between the two teams is key. Boneck said Balyasny has a team tasked with doing exactly that. The group, known as sector data analysts, are code-savvy fundamental analysts who understand companies and industries and can work with the fundamental team on statistical validation for them.
"A typical question might be, 'I have this idea. What data could we use to prove or disprove an investment thesis?'" he said. "It's people who have a quantitative background. Have those coding chops. But really get the difference between a cross-sectional back test and a KPI analysis and a specific company-specific thesis."
At it's core, though, Boneck said pursuing a quantamental strategy starts with having the technology and data engineering to do so.
To be sure, that doesn't necessarily mean investing in the most high-end, cutting-edge tech all the time. Boneck said shiny-object syndrome can derail the best-laid efforts. Pragmatism is key.
Boneck said one of the initial build-outs was a security master for the firm that helped link third-party data sets.
"That's not the sexiest project, but it really is an accelerant for everything that follows," he added.
