Deep Learning in Fixed Income Markets
Hosted by Lauren Hawker Zafer
With Kay Giesecke
This episode is a unique educational listen that invites us to think about how machine learning is enabling industry innovation and powerful tools that can solve important problems arising in fixed-income and credit markets. It is a conversation for all of us who are interested in the use of AI in the financial industry.
As a financial technologist, Kay provides invaluable insight into how he and his team are solving the challenging modeling, statistical, and computational problems arising in the fixed-income and credit market. This conversation also addresses a more technical audience, given the additional technical angles of exploration that are provided by the Squirro CTO Saurabh Jain.
Kay is the Founder, Chairman and Chief Scientist at Infima. He is also Professor of Management Science & Engineering at Stanford University, the director of the Advanced Financial Technologies Laboratory, and the director of the Mathematical and Computational Finance Program. Kay serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society.
As a financial technologist, Kay is interested in solving the challenging modeling, statistical, and computational problems arising in fixed-income and credit markets. Together with his students at Stanford, Kay has pioneered the core elements of the deep learning and computational technologies underpinning Infima’s solutions. Kay’s research has won several awards, including the JP Morgan AI Faculty Research Award (2019) and the Fama/DFA Prize (2011), and has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, Swiss Re, American Express, Moody's, and several other organizations.