The Nelson A. Rockefeller Center for Public Policy and the Social Sciences

Mini-Grants Recap: QuantCon 2015

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Students reflect on the opportunities provided to them by the Rockefeller Center's Mini-Grants program through this ongoing series. The Mini-Grants program funds registration fees for students attending conferences relevant to the Rockefeller Center's mission as well as the costs of bringing speakers to the Dartmouth campus.

Prior to attending Quantopian’s QuantCon 2015 summit in New York City, I was well aware that the financial industry was moving ever closer to a future with mostly algorithmic trading and investment strategies that incorporate advanced machine learning. When I arrived at the conference and talked to people in the industry, however, it became apparent that financial firms were already beginning to widely adopt data science as more than just a single tool in their investing repertoire. Traders, quantitative analysts, and investment bankers all had plenty to say in response to how "algos," or trading algorithms, were growing in use and helping open the industry to smaller firms with smart, automated strategies.

Mebane Faber, writer and Co-Founder of Cambria Investment Management, discussing uncorrelated returns in portfolio management.

Throughout the conference, I had the opportunity to meet and speak with numerous leaders in the field of finance. CEOs, founders, and managing directors of algorithmic trading departments discussed the most recent developments and broad strategies used by their firms. Professors and researchers in machine learning and networked systems shared their findings and presented new tools for use in financial analysis. A primary focus of the summit was to connect leading experts in the fields of data science and finance with everyday investors looking for ways to improve their investing portfolio and algorithmic trading strategies. QuantCon was replete with time to mingle with experienced data scientists and traders who were filled with insightful views on how algorithmic trading will evolve over the next decade. I spoke with business leaders over what the largest pain points were in operating an algorithmic trading department. I listened to professors and high frequency trading researchers discuss their expectations for the future and how safe regulation and oversight might need to be implemented to prevent catastrophe.

Closing remarks are given by Matthew Grenade, former Head of Resaerch at Bridgewater Associates.

The true value of the conference was in inspiring fledgling investors and data scientists to embrace a powerful disruptive engine that will have immense consequences for the financial industry and may begin a new, modern trend in automating non-repetitive, higher-level thinking occupations with intelligent, predictive systems.

Tucker Balch, Professor of Computer Science at Georgie Tech, talks about his online course on Artifical Intellgience and Finance.

Data science and machine learning are rapidly becoming integrated into many sectors in order to solve large, complex problems with intensive data analysis. We currently have only a small taste of the implications in fields with massive amounts of readily available data such as trading and investing. However, what is now clear to me after attending QuantCon is that data science is becoming an indispensable tool in many industries, and it will come to affect each of us in critical ways over the next decade.

-Written by Kevin Kenneally '18

The Nelson A. Rockefeller Center for Public Policy and the Social Sciences