Abstract: 

What can financial analysts learn from computer scientists about data science? And can computer scientists change their approach to appeal to finance professionals?

Data scientists typically argue about the relative merits of the statistical and algorithmic approaches to data mining (Leo Breiman - The Two Cultures). Anyone who has done data science in applied finance (trading, sell side research, portfolio management) recognizes that there is a third culture that is radically different from the other two. In this session we explore these cultures and why it is important that we bridge the gap between the traditional data science cultures and applied finance - the third culture.

Bio: 

Stephen Lawrence is the Head of QuantextualSM Research at State Street Global ExchangeSM. He oversees research aggregation services for clients by innovatively blending machine learning and human knowledge. His team helps accelerate the implementation of investment ideas based on academic research and provides an “Idea Lab” platform to help summarize a wide range of research including academic and sell-side research. Since joining State Street in 2003, he has been involved in the development of FX investor behavior indicators and quantitative investment strategies based on those measures.

Stephen is also a TED speaker with a 2015 talk titled “The future of reading: it’s fast".