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

ArXiv ID: 2407.13687 “View on arXiv”

Authors: Unknown

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.

Keywords: Securities Lending, Contextual Bandits, Dynamic Pricing, Agent Lenders, Revenue Optimization, Securities Lending

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies existing contextual bandit frameworks (like Thompson Sampling) without extensive novel mathematical derivations, focusing on adaptation to a specific market structure. However, it demonstrates strong empirical rigor by using offline evaluation on real historical data to quantify a consistent 15% revenue improvement over baseline methods.

  flowchart TD
    A["Research Goal<br>Optimize Agent Lender Revenue<br>via Dynamic Pricing"] --> B["Data Source<br>Historical Securities Lending Market Data"]
    B --> C{"Methodology Comparison"}
    C --> D["Baseline: Static Rules /<br>Supervised ML Models"]
    C --> E["Proposed: Contextual Bandit<br>for Adaptive Pricing"]
    
    D & E --> F["Offline Evaluation<br>Simulated Trading on Historical Data"]
    
    F --> G["Key Finding<br>Contextual Bandits Outperform"]
    G --> H["+15% Total Revenue<br>Scalable & Adaptive to Market Conditions"]