Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
<|reference_start|>Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective: 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.<|reference_end|>
@article{atkins2024reinforcement,
    title={Reinforcement Learning for Corporate Bond Trading: A Sell Side
  Perspective},
    author={Samuel Atkins, Ali Fathi, Sammy Assefa},
    journal={arXiv preprint arXiv:2406.12983},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12983},
    primaryClass={q-fin.CP cs.LG math.OC}
}