Physical therapist. Dental hygienist. Personal financial adviser.
Such occupations are perennials on ‘best jobs of the future’ lists.
Market participants say a more esoteric title can be added: fixed income data scientist.
The projected robust future for fixed-income data science is based on the premise that fixed income markets are in the very early stages of effectively collecting, managing, and exploiting the reams of data that flow through trading desks every day. With so much room for improvement in such a massive market, it stands to reason that a bright young person who goes into fixed income data science can carve out a long and lucrative career in the field.
Among market constituencies, institutional owners of fixed income securities -- who invest on behalf of mom-and-pop end users -- are seen as furthest behind, especially in corporate bonds. Such institutions can be huge, and slow-moving when it comes to updating archaic processes.
“The fixed-income investment industry is far behind the curve in terms of data,” said one institutional corporate-bond trader, who spoke with Markets Media on condition of anonymity. “Real-money investors -- your traditional pension funds, mutual funds, insurance companies -- are so far behind the curve it’s almost laughable. Some of these firms are still figuring out how to use Excel.”
“So because the industry has yet to figure out data, smart investors see an opportunity,” the trader said. “There’s a lot of low-hanging fruit to be profitable. If you can figure out how to use data more efficiently and automate your investing processes even just marginally, you can crush your competition.”
One sign that bond players are at least starting to address the topic of data comes from industry conferences. For a number of years, talking points at events typically centered around market structure and liquidity for a number of years; just over the past year or so, the focus has shifted to data science and technology.
The trader said that historically, corporate-bond data has been “crappy” compared with data for equities and U.S. Treasuries. That shortcoming has limited the participation of shorter-term quantitative and systematic traders, who are now ubiquitous in equities. The paucity of quants in credit markets means that there are a lot of trading profits that have not been arbitraged away, as they have in equities.
“There is opportunity for people who really understand market structure on a deep level and are flexible enough mentally to figure out how to tap into emerging data-science protocols and technology,” the corporate trader said. “Look, there are people still doing things the same way they did them 20 years ago. Meanwhile, the world is changing incredibly fast. Those who can figure out how to adapt are going to make a lot of money.”
