Abstract: Increased digitalization of communication and recent advances in natural language processing allow us to satisfy new regulatory requirements and to advance automation in the financial industry. But our industry has its own quirks and challenges – a unique, highly formalized parlance coupled with a lack of large sets of labeled data. We use neural nets and a variety of tools from statistical machine learning to help us solve these evolving problems. Even more exciting, these methods can now be applied to pricing and risk management methods; fields that have largely stagnated over the last few decades, and that have not adapted to the reduced holding periods of risk by liquidity providers. Comprehensive data policies and the ability to integrate probabilistic models on this data are preconditions for successful deployment of machine learning in capital markets.
Bio: Peter works in the Rates Trading group at Citi. He focuses on machine learning for the implementation of pricing and risk analytics. Peter has developed neural net applications for natural language processing, as well as probabilistic graphical models for pricing. Peter joined Citibank’s Fixed Income Algo trading group in 2011. This team has deployed the largest bank systematic trading and execution platform for treasuries and bond futures.