I was introduced to this perennial argument of traders versus quants the first time I walked onto the trading floor – this was back in 2005 on a tour for interns joining the summer program at UBS. I was a fresh computer science graduate at that time. Fast forward 10-plus years of being a trader on the sell-side and then a portfolio manager on the buy-side at one of the largest hedge funds, and I have seen this rivalry from both sides.
This debate is not new. In fact, it is more than a few decades old.
A quant would develop a beautiful, thorough, well-researched model to 'fairly' price a product only to have a trader ruin it when it comes to actually using the model.
On the other hand, the trader is dealing with the challenge of delivering everyday profits using the models to operate in markets that tend to break all the assumptions, often going against the rationale one might have built into a model.
Ultimately the goals are aligned, to be able to price the instrument as fairly as possible so that the institution can profit from the mispricing in the market, quote accurate prices to clients and sell the instrument effectively in the end.
However, there is a relentless love-and-hate relationship between traders and quants. It is a fight for space, who is adding to the bottom-line profit-and-loss (P&L) statement and finding how high each one is perched in the food chain.
In any asset management firm, if the quants are the engine of the sports car, it is the trader that is typically in the driver’s seat running the car and testing the limits of the engine. One would not last without the other.
Unprecedented market events, technological innovation and an explosion of big data are changing the rules of this game.
Excel sheets are being taken over by other third-party tools. Servers are moving to the cloud. The sheer amount of data that we now have available to us has exploded. Marketing and sales have migrated to entirely new digital distribution channels.
This has led to new roles in financial services: machine-learning researcher, data scientist, high-performance computing engineer and quantitative trading algorithm coder, among others.
Innovation is being driven by fintech startups. Many graduates are being scooped up by fintech firms instead of a traditional 100-plus analyst class at a big bank.
Many incumbents are facing the pressure as startups are changing the standards of some of these business lines entirely.
The classic example is the ‘robo-advisory’ industry that has changed all the rules of the wealth management industry. Leading robo-advisers are bringing AI-investing to your smartphone. They have made it cool, accessible and affordable. And now there is suddenly a huge demand for engineers, not just financial advisors in a suit and a tie, in this vertical.
Many big hedge funds are re-branding as ‘quant shops,’ dissolving the line between portfolio managers and quants entirely. They don't want quants supporting the traders. They don't want traders without quantitative models. They want a new breed of engineers to make a system that can trade using quantitative methods and learn as it is given more data.
There will always be certain rainmakers on the trading floor and quants who can quote stochastic calculus in their sleep. The good news is that this new environment has room for other players.
You can be a computer science engineer and still be developing quantitative models that trade products around the world. You could be a front-end developer bringing access via a smartphone app or website. You could be a statistician modeling patterns in payments and loans data using techniques that haven’t been used in financial services, because this kind of data was not there until five years ago.
There is a growing need for people in new roles like big data technologist, data scientist, machine-learning researcher, high-performance computing engineer and more.
This makes it a lot more challenging and fascinating time for this industry. The growing trust in data-driven decision-making over old-school trading stars following their gut feelings is leading this change. This is not just percolating the quant versus trader altercation but also changing the trends of in-demand jobs and the skills that professionals need to survive in financial services.
Mansi Singhal is the co-founder of qplum, a machine-learning-powered quantitative asset manager, and a former trader at Brevan Howard.
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