Optimal Market Making by Reinforcement Learning
This addresses liquidity provision in financial markets, but it is incremental as it applies existing RL methods to a classic problem.
The paper tackled the market making problem in quantitative finance by applying reinforcement learning to balance inventory price risk and bid-ask spread profits, finding that Deep Q-Learning recovered the optimal agent compared to benchmarks.
We apply Reinforcement Learning algorithms to solve the classic quantitative finance Market Making problem, in which an agent provides liquidity to the market by placing buy and sell orders while maximizing a utility function. The optimal agent has to find a delicate balance between the price risk of her inventory and the profits obtained by capturing the bid-ask spread. We design an environment with a reward function that determines an order relation between policies equivalent to the original utility function. When comparing our agents with the optimal solution and a benchmark symmetric agent, we find that the Deep Q-Learning algorithm manages to recover the optimal agent.