Learning to Rank Requests for Quotes for Bond Traders

Venkatesh Elango, a research engineer in Bloomberg’s AI Group, will participate in the next meeting of the Data Science @ Regensburg – Meetup group and will speak about search problems in the financial domain.


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.

Wednesday, 05-19-2021, 07.00 p.m. – 09.00 p.m., virtual event

More details & registration at: https://www.meetup.com/de-DE/Data-Science-Regensburg/events/277833346/.

This is an event of the Data Science @ Regensburg – Meetup group.