Learning to Rank Requests for Quotes for Bond Traders
Geoffrey Gunow, Senior Machine Learning Engineer
Ivo Vigan, Senior Machine Learning Engineer and Team Lead
Abstract: The work of a bond trader involves repeatedly responding to request-for-quotes (or RFQs) as quickly as possible and with the best price, sometimes dealing with up to 10,000 orders or RFQs each day. Working with an ever-increasing volume and under challenging time constraints, many traders are finding it difficult to keep up — missing opportunities and not working as efficiently as they could be. As a result, many are looking to technology to aid them in helping optimize this process. Currently, hard-coded rules are used to help automate the process of routing trades and responding to RFQs. However, this approach is difficult to scale. Incorporating machine learning can offer more efficient tools for improving workflows. In this talk, we will discuss a recently developed machine learning model that suggests which orders or RFQs the trader should work on first. With the help of these suggestions, traders can focus their time more efficiently on high-value decisions.
Geoffrey Gunow is a Senior Machine Learning Engineer in Bloomberg’s AI Engineering Group. He has worked over the past 3 years on solving machine learning problems relevant to financial applications. He holds a Ph.D from MIT in Computational Nuclear Engineering.
Ivo Vigan is a Senior Machine Learning Engineer and Team Lead in Bloomberg’s AI Engineering Group. He has worked over the past 5 years on Natural Language Processing problems as well as machine learning problems in the financial domain. He holds a Ph.D from CUNY in Theoretical Computer Science.
Data Science Institute, Columbia University
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