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

TL;DR

This paper explores the application of contextual bandit algorithms to optimize revenue in securities lending markets, demonstrating significant improvements over traditional methods through offline evaluation on real data.

Contribution

It introduces a novel application of contextual bandit frameworks for dynamic pricing in securities lending, outperforming standard approaches in revenue optimization.

Findings

Contextual bandit methods outperform traditional pricing strategies by at least 15%.

Offline evaluation confirms the effectiveness of the approach on real market data.

The approach adapts well to changing market conditions, enhancing revenue.

Abstract

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.