Machine learning and corporate bond trading

Article type: Research Article

Authors: Wright, Dominic | Capriotti, Luca^{; *} | Lee, Jacky

Affiliations: Department of Mathematics, University College London, Gower Street, London WC1E 6BT, United Kingdom

Correspondence: [*] Corresponding author: Luca Capriotti, Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK. E-mail: luca.capriotti@gmail.com.

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

DOI: 10.3233/AF-180258

Journal: Algorithmic Finance, vol. 7, no. 3-4, pp. 105-110, 2018