Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective

TL;DR

This paper applies reinforcement learning to optimize bid-ask spreads for corporate bond trading, especially for illiquid bonds, by learning from data to improve trader profitability and market liquidity.

Contribution

It introduces a data-driven reinforcement learning approach to estimate optimal quoting strategies for illiquid bonds, building on the concept of Fair Transfer Price.

Findings

RL-based strategies outperform baseline methods

Trained agents exhibit reasonable and consistent trading behavior

The approach effectively adapts to market conditions

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.