Machine Learning and Corporate Bond Trading

5 Pages Posted: 21 May 2019

See all articles by Dominic Wright

Dominic Wright

Credit Suisse AG

Luca Capriotti

University College London; NYU Polytechnic School of Engineering - Department of Finance and Risk Engineering

Shinghoi (Jacky) Lee

Quantitative Strategies - Investment Banking Division - Credit Suisse Group

Date Written: October 26, 2018

Abstract

We demonstrate how machine learning based recommender systems can be effectively employed by market makers to filter the information embedded in Requests for Quote (RFQs) to identify the set of clients most likely to be interested in a given bond, or, conversely, the set of bonds that are most likely to be of interest to a given client. We consider several approaches known in the literature and ultimately suggest the so-called latent factor collaborative filtering as the best choice. We also suggest a scalable optimization procedure that allows the training of the system with a limited computational cost, making collaborative filtering practical in an industrial environment.

Keywords: Machine Learning, Recommender Systems, Collaborative Filtering, Corporate Bond Trading

Suggested Citation

Wright, Dominic and Capriotti, Luca and Lee, Shinghoi (Jacky), Machine Learning and Corporate Bond Trading (October 26, 2018). Available at SSRN: https://ssrn.com/abstract=3386669 or http://dx.doi.org/10.2139/ssrn.3386669

Dominic Wright

Credit Suisse AG ( email )

CRTI 4
P.O. Box
Zurich, CH-8070
Switzerland

Luca Capriotti (Contact Author)

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

NYU Polytechnic School of Engineering - Department of Finance and Risk Engineering ( email )

Brooklyn, NY 11201
United States

Shinghoi (Jacky) Lee

Quantitative Strategies - Investment Banking Division - Credit Suisse Group ( email )

Eleven Madison Avenue
New York, NY 10010
United States

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