Machine learning and corporate bond trading

Dominic Wright, Luca Capriotti () and Jacky Lee
Additional contact information
Dominic Wright: Department of Mathematics, University College London, Postal: United Kingdom
Luca Capriotti: Department of Mathematics, University College London, Postal: Gower Street, London WC1E 6BT, United Kingdom
Jacky Lee: Department of Mathematics, University College London, Postal: United Kingdom

Algorithmic Finance, 2018, vol. 7, issue 3-4, 105-110

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 (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0071

Access Statistics for this article

Algorithmic Finance is currently edited by Phil Maymin

More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().