ML-Assisted Optimization of Securities Lending
| dc.contributor.author | Prasad, Abhinav | en |
| dc.contributor.author | Arunachalam, Prakash | en |
| dc.contributor.author | Motamedi, Ali | en |
| dc.contributor.author | Bhattacharya, Ranjeeta | en |
| dc.contributor.author | Liu, Beibei | en |
| dc.contributor.author | McCormick, Hays | en |
| dc.contributor.author | Xu, Shengzhe | en |
| dc.contributor.author | Muralidhar, Nikhil | en |
| dc.contributor.author | Ramakrishnan, Naren | en |
| dc.date.accessioned | 2023-12-04T18:14:14Z | en |
| dc.date.available | 2023-12-04T18:14:14Z | en |
| dc.date.issued | 2023-11-27 | en |
| dc.date.updated | 2023-12-01T08:52:01Z | en |
| dc.description.abstract | This paper presents an integrated methodology to forecast the direction and magnitude of movements of lending rates in security markets. We develop a sequence-to-sequence (seq2seq) modeling framework that integrates feature engineering, motif mining, and temporal prediction in a unified manner to perform forecasting at scale in real-time or near real-time.We have deployed this approach in a large custodial setting demonstrating scalability to a large number of equities as well as newly introduced IPO-based securities in highly volatile environments. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3604237.3626877 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/116727 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | ML-Assisted Optimization of Securities Lending | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |