- Some of the biggest funds are pumping money, technology, and time into building out credit quant teams, sources tell Business Insider.
- Firms like Steve Cohen’s Point72, Renaissance Technologies, and Millennium have all built out their teams as they try to apply their expertise in quant equity and FX trading strategies to the $9.2 trillion world of US corporate bonds.
- Recruiters tell Business Insider that firms are unsure of what to look for when hiring for these roles because “it’s just so different from the equity space.”
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Some of the world’s largest quant funds are working to turn their mathematical wizardry to a market that has long remained outside their reach: Bonds.
Point72, Renaissance Technologies and Millennium Management are among a growing group of hedge funds hoping to leverage their expertise in analyzing large chunks of data to systematically trade US corporate bonds in what they believe could be a big opportunity in a market valued at $9.2 trillion in 2018.
Even those funds without a background in quantitative strategies are looking to take a more data-driven approach to credit trading.
Ben Hodzic, an executive director of North America at recruiter Selby Jennings who focuses on quantitative placements, told Business Insider of the top 10 credit hedge funds in New York, 80% of their open roles require a quantitative background.
“There is really no more landscape to hire traditional credit analysts,” he said. “Just about every credit hedge fund is a lot more geared toward hiring quantitative talent.”
Bond trading’s electronic shift created an opportunity
The increased interest in bond trading isn’t a coincidence. It comes as the market is going through a revolution. For years, credit trading was almost entirely relationship-based, with everything done over the phone and a handful of the largest banks maintaining the most significant market share.
The rise of electronic marketplaces such as MarketAxess and Tradeweb in recent years has led to drastic change, as an increasing amount of trading is done electronically. As a result, data, the lifeblood of any quantitative strategy, has slowly become more available thanks to trades moving from phones to computers.
Market makers, especially those maintaining large businesses trading fixed income exchange-trade funds, have already taken notice, as firms like Jane Street and GTS have started to push into the space.
And now, too, so are quantitative hedge funds, hoping to systematically trade corporate bonds the same way they do equities and foreign exchange: Using a sophisticated proprietary pricing engine to find discrepancies in how the market is pricing securities.
Point72, the Stamford, Conn.-based hedge fund run by Steve Cohen, who is currently in talks to acquire 80% the New York Mets, is building a team out of its Cubist Systematic Strategies group. In July, the firm posted a listing for an execution trader to work with the Cubist portfolio management team “specializing in the systematic trading of US credit markets.”
The role was explained as, “an opportunity for someone with expertise in sourcing (electronic and voice) liquidity in the credit markets and piloting automated trading systems to take a central role on the front lines of the team’s investment process.”
A spokesperson for Point72 declined to comment.
Meanwhile, Long Island-based Renaissance Technologies was one of the first adopters of a tool released in August by Bloomberg, which also operates an electronic market for trading bonds, that helps firms predict if a corporate bond’s spread will widen or tighten, according to sources familiar with the matter.
An Institutional Investor piece from 2017 lays out just how long the secretive hedge fund has been working on this — this story starts by noting that the firm “has had near real-time prices on corporate bonds and other debt for years.”
Through a spokesperson, Renaissance declined to comment.
Millennium Management is also investing resources in building out a team that can trade bonds in a systematic way, according to multiple sources. Billionaire Izzy Englander’s firm also declined to comment.
Staffing quants to credit teams has proved difficult
Despite these firms’ history of making billions of dollars through quantitative investing strategies, the credit market has proved to be a tricky endeavor. Firms have quickly learned trading bonds is not as simple as slightly reconfiguring systems designed to trade stocks or FX.
At its core, systematic trading is about quickly understanding if the market is under or overvalued and acting on it, all through the use of complex algorithms. To do that, one must first establish the notion of what the right price is.
But pricing credit is a far cry from FX or equities. Unlike a particular stock or currency, a bond might only trade once a day, requiring investors to consider how to trade it in a completely different way.
“There is a false narrative that you can take an FX system or an equity system and apply it to credit,” a veteran of the credit industry told Business Insider. “You need to warehouse risk in credit where you don’t in other asset classes, which means you therefore need to price risk from a warehousing perspective not from an instantaneous transaction perspective.”
As a result, staffing for such projects has proved competitive, according to sources. Vickram Tandon, a partner and founder of A-Squared Search, told Business Insider that funds are looking for “old-school” quants to take up these roles.
Because of the lack of data in the credit space compared to the equities market, machine-learning specialists and artificial intelligence designers aren’t going to be of much use.
Tandon said people that were building mortgage models a decade ago at big banks like JPMorgan and Morgan Stanley who have now shifted into risk roles at banks and insurance companies are surprisingly solid candidates if they’ve kept up with the technology.
“It’s just so different from the equities space,” he said.
Ideally, a quant with a background in credit trading would be the perfect candidate. However, because the industry lacked significant data for so long, few have entered the space, instead gravitating towards markets with an abundance of information such as equities, FX or commodities.
As a result, Hodzic said hedge funds looking to bring quants onto their credit trading desks have typically gone two routes.
One is to hire recent PhD graduates with no experience trading at all with the belief the knowledge of the asset class can be taught quickly enough. The other is to hire credit-trading veterans that have learned computational skills later in life.
“I don’t see very much success, personally from our placements, in people coming from an equities background or maybe even a commodities background,” Hodzic said. “I think what it comes down to is liquidity. If you are working in a very liquid market, chances are you can move into another asset class that is equally as liquid. If it is not that case, then you are working with a very different beast when it comes to data and speed of technology.”