ML-assisted Optimization of Securities Lending

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.