Machine learning and corporate bond trading
| Autor: | Jacky Lee, Dominic Wright, Luca Capriotti |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Bond Recommender system Machine learning computer.software_genre Market maker Computer Science Applications Corporate bond Computational Mathematics Filter (video) Scalability Collaborative filtering Computer Vision and Pattern Recognition Artificial intelligence Set (psychology) business computer Finance |
| Zdroj: | Algorithmic Finance. 7:105-110 |
| ISSN: | 2157-6203 2158-5571 |
| DOI: | 10.3233/af-180258 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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