Since winning the Correlation One / Citadel datathon in February, I’ve been fascinated by data science jobs in the Finance sector, and the idea of applying my strategic bidding research to equity markets where trading volumes are greater, physical constraints are looser, and there is less tension between regulators and traders.
However, from the West Coast it’s difficult to get insight into this secretive world, and I recently spent a day in New York meeting with current and former quantitative researchers (or ‘quants’) to understand life within quantitative hedge funds. As my background is with tech startups, I’ve been struck by the similarities -and differences- between these fields, and compare the two here for other rising data scientist who are weighing these career choices.
From across the country, the quantitative finance field seems dominated by large banks and a few large hedge funds – just as the Tech industry might appear to be dominated by post-IPO companies like Facebook, Amazon, Netflix, and Google. However, in both of these fields there is a world of smaller firms, often spinoffs founded by successful traders from the large funds. Operating like startups, these small funds have lean teams in which team members play a variety of roles until the idea is proven and ready to take on additional outside investment. Once an idea has proven its track record in the market, outside investment is sought to grow the idea to full scale- at which point the firm can begin adding more portfolio managers, more researchers, and more strategies until it is a diversified behemoth like Citadel or DE Shaw.
This growth curve can be as rapid as that found in startups, but with an important differentiator: while tech startups can get funding based on a team and an idea, institutional investors need to see a proven track record. This demands that small funds operate with a relatively small amount of capital for a few years before scaling through outside investment. In this formative stage, the team most needs people with deep familiarity with the market and a proven track record, making them difficult for a new engineering graduate to penetrate.
In technology, startups can be differentiated by branding, customer relationships, or unique data- things which are difficult to copy and which can create a “moat” which protects against competitors. In finance, there is no such moat: firms have access to the same market data, the same exchanges, and comparable computing power. In tech, a product’s success snowballs through network effects and market adoption- in finance, the profits of a successful algorithm will be eroded by market impacts, and ultimately get wiped out when competitors stumble across the same edge. Together, these effects make hedge funds more secretive and competitive, part of why I found it so difficult to learn about them.
Intense competition also shapes the career options of people in the field: while employees in the Bay Area may bounce between competing companies, spreading innovation through cross-polination, employment contracts in finance typically contain non-compete clauses that last between 6 months and several years, and are regularly enforced. Once installed at a successful firm, employees are less likely to move- held in place by a combination of good compensation, cultural fit, and non-compete clauses.
This competitiveness also drives a more dynamic work environment: rather than slowly building and curating a product feature over years, quantitative researchers are constantly brainstorming, testing, and rejecting new ideas. The pressure to constantly find a new edge means there is no chance to rest on one’s laurels, and job security is always threatened by the next market turnaround.
Like the Bay Area, Wall Street has its share of celebrity executives and strong-minded CEOs, and rivalries and grudges that emerge as these celebrities move between firms on their way to the limelight. But whereas the Bay Area may bear the historical stereotype of the greasy geek, preconceptions of Wall Street are based on the bellicose brawl of the trading pits. Instead of finding the ‘Big Swinging Dicks’ described by Michael Lewis in Liar’s Poker, I was astonished to find that everyone I met in the industry was patient, kind, and incredibly generous with their time- and that the quants I met were just as nerdy, excitable, and goofy as I.