2023/07/04 by Zhou Fang, Fang, Zhou, Xu Haiqing +1
Business, Management and Accounting · Decision Sciences · #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Economics and business #Supply Chain and Inventory Management #Trading and Market Microstructure (q-fin.TR)
paper · pdf · doi:10.48550/arxiv.2307.01816
openalex publication_date 2023/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an innovative solution utilizing reinforcement learning techniques to tackle the OTC market-making problem. By assuming a linear inverse relationship between market order arrival intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask spreads follows a Gaussian distribution. We apply two reinforcement learning algorithms to conduct a numerical analysis, revealing the resulting return distribution and bid-ask spreads under different time and inventory levels.