Citadel Datathon Featured in Bloomberg!

I was delighted to be quoted in Bloomberg Technology in their coverage of the Datathon organized by Citadel and Correlation One- definitely an unexpected honor, and I’ve been humbled by the responses. For more the Datathon itself, check out my post about the Correlation One Datathon.

In the weeks since the Datathon, I’ve had the chance to reflect a bit about the experience at the Datathon, and the insights I’ve gained from talking with the folks at Citadel and at other financial firms. Prior to the Datathon, I viewed my lack of experience in finance as a barrier to getting a job in the field- even though my research on energy markets has made me very interested in optimal bidding strategies and price forecasting.  I thought that coming from an engineering background would be a handicap- I was wrong.

It turns out that some firms specialize in the type of data analysis which we did at the Datathon, and which I do in my work as an Engineering PhD student. Instead of focusing on schmoozing and deal-making like conventional banks, these firms work more like a technology start-up: working with big datasets to identify valuable insights, developing and testing production code, and deploying algorithms to automatically make trades. Their offices look like tech startups complete with ping-pong tables and stocked fridges, they compete with Google and Facebook for talent, and they recruit with Kaggle competitions and Datathons.

From talking with these firms, I’ve learned some useful insights about some big changes that are happening in this space:

  • Traditional hedge funds have under-performed the market since the Great Recession, while quantitative and high-frequency funds have offered better returns- this has spurred conventional funds to develop quantitative arms, and quantitative firms to see massive inflows from investors.
  • The Dodd-Frank Financial Reform act forced investment banks to stop some of their trading operations, leading to the creation of new firms- many of which have found that quantitative tools have been extremely effective at identifying trades and making markets.
  • There’s been an explosion in the data available to financial firms, fueled by startups which are scraping data into products for financial companies.
  • A boom in data science companies in Silicon Valley has made it harder for Wall Street firms to find people to fill openings.
  • Engineering firms in Machine Learning and Artificial Intelligence have begun profitably trading in financial markets, making other quantitative finance firms take notice.

Taken together, this has created a turbulent time in the investment space, and led to a dramatic increase in recruiting from quantitative finance, algorithmic trading, and high-frequency trading firms.  As they are directly competing with big tech companies, they offer comparable perks -flexible hours, game rooms, stocked kitchens, good vacation allowances- but bonuses are in cash, not equity. The goal is to create an academic atmosphere where PhDs have the support they need to be creative- very different from my preconceptions.

I hadn’t been aware that this was a career path for engineering PhDs, and hope that others get the chance to explore this space!

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