ML-Assisted Optimization of Securities Lending

Abhinav Prasad, Prakash Arunachalam, Ali Motamedi, Ranjeeta Bhattacharya, Beibei Liu, Hays McCormick, Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationICAIF 2023 - 4th ACM International Conference on AI in Finance
Pages628-636
Number of pages9
ISBN (Electronic)9798400702402
DOIs
StatePublished - 27 Nov 2023
Event4th ACM International Conference on AI in Finance, ICAIF 2023 - New York City, United States
Duration: 27 Nov 202329 Nov 2023

Publication series

NameICAIF 2023 - 4th ACM International Conference on AI in Finance

Conference

Conference4th ACM International Conference on AI in Finance, ICAIF 2023
Country/TerritoryUnited States
CityNew York City
Period27/11/2329/11/23

Keywords

  • Deep Learning
  • Motif Mining
  • Securities Lending
  • Sequence-to-Sequence Modeling

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