Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective

• URL: http://arxiv.org/abs/2406.12983v1
• Date: Tue, 18 Jun 2024 18:02:35 GMT
• Title: Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
• Authors: Samuel Atkins, Ali Fathi, Sammy Assefa,
• Abstract summary: A corporate bond trader provides a quote by adding a spread over a textitprevalent market price For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices. In this paper, 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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