Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective ArXiv ID: 2406.12983 “View on arXiv” Authors: Unknown Abstract A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. Upon receiving a request for a buy/sell price quote (RFQ), the trader provides a quote by adding a spread over a \textit{“prevalent market price”}. For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices (such as MarketAxess, Bloomberg, etc.). In \cite{“Bergault2023ModelingLI”}, the concept of \textit{“Fair Transfer Price”} for an illiquid corporate bond was introduced which is derived from an infinite horizon stochastic optimal control problem (for maximizing the trader’s expected P&L, regularized by the quadratic variation). In this paper, we consider the same optimization objective, however, we approach the estimation of an optimal bid-ask spread quoting strategy in a data driven manner and show that it can be learned using Reinforcement Learning. Furthermore, we perform extensive outcome analysis to examine the reasonableness of the trained agent’s behavior. ...