We’re excited to welcome speakers from CheckRisk to the next DataScience@work seminar.
David Greenwood, COO and Director of Data Science
Title: AI-driven Discretionary Investment & Risk Management: A personal view on the of unintuitive quirks of investment and the stimulating Data Science challenges to be found within financial markets.
Abstract: By the end of this presentation you won’t have become a hedge fund manager or received any financial advice. However you will have been on an unconventional whistle stop tour of the fascinating challenges of operating ‘Data Science’ in financial markets, including some of unintuitive quirks such as (1) how to profit from a completely random walk? (2) that markets may not be a completely random walk? (3) that forecasting and machine / statistical learning methods play an important role but probably not for predicting future “prices”? (4) a glimpse of how we apply ‘data science’ to get our work done.
Dr Edmund Barter, Lead Data Scientist.
Title: Putting Theory Into Practice: the data-science of implementing research.
Abstract: In many areas, and in finance in particular, there are an array of theory driven models that attempt to explain the world. However, there is often a gap between model and reality that needs to be bridged in order to realise a models full potential. In this talk we will explore the role of data science in connecting models to reality and see how financial theory and data science work together to produce updated solutions to age old problems.
“CheckRisk provides fintech enabled services focused on helping our clients to build successful wealth management, investment management or risk management practices. We are unique in that our Huxley risk ecosystem offers a science-based risk-responsibility-reward approach that we developed in collaboration with the University of Bath and Bristol. We put responsibility at the heart of decision-making.”
We have a limited space for non-Compass attendees. To register for this and future events, please submit this registration form.