# Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio

arXiv: https://arxiv.org/abs/2407.13687 · HTML: https://mlsanity.com/paper/2407.13687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/1801.10242/) (2018)
- [Semi-parametric dynamic contextual pricing](https://mlsanity.com/paper/1901.02045/) (2019)
- [Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques](https://mlsanity.com/paper/1405.3536/) (2014)
- [Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit](https://mlsanity.com/paper/2203.03003/) (2022)
- [ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions](https://mlsanity.com/paper/2205.12231/) (2022)
- [Optimal, Truthful, and Private Securities Lending](https://mlsanity.com/paper/1912.06202/) (2019)

## Built on by (most cited first, 1 of 1)

- [Multi-Task Dynamic Pricing in Credit Market with Contextual Information](https://mlsanity.com/paper/2410.14839/) (2024)

## Abstract (arXiv)

Securities lending is an important part of the financial market structure, where agent lenders help long term institutional investors to lend out their securities to short sellers in exchange for a lending fee. Agent lenders within the market seek to optimize revenue by lending out securities at the highest rate possible. Typically, this rate is set by hard-coded business rules or standard supervised machine learning models. These approaches are often difficult to scale and are not adaptive to changing market conditions. Unlike a traditional stock exchange with a centralized limit order book, the securities lending market is organized similarly to an e-commerce marketplace, where agent lenders and borrowers can transact at any agreed price in a bilateral fashion. This similarity suggests that the use of typical methods for addressing dynamic pricing problems in e-commerce could be effective in the securities lending market. We show that existing contextual bandit frameworks can be successfully utilized in the securities lending market. Using offline evaluation on real historical data, we show that the contextual bandit approach can consistently outperform typical approaches by at least 15% in terms of total revenue generated.