  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.
