2026-09-25T08:01:49Zhttps://skemman.is/oai/request

oai:skemman.is:1946/486762024-10-17T11:09:18Zcom_1946_6867com_1946_6001col_1946_34321

Háskólinn í Reykjavík Úlfar Andri Snæfeld 2001- Patrick Weiss 1991- This thesis explores the application of reinforcement learning (RL) strategies to market making in illiquid markets. Traditional market making approaches often rely on static, rule-based strategies, which can struggle in illiquid environments. The study implements three RL algorithms: Deep Q-Networks (DQN), Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). They are evaluated in a simulated stock market environment on their performance in liquid and illiquid market conditions. Findings show that DQN outperforms the other in both conditions. The research highlights the challenges RL models face in illiquid markets, characterized by higher volatility, wider bid-ask spreads, fewer trades, and higher risks of adverse selection. The results contribute to the growing field of RL in financial markets by showing how these algorithms can be adapted to improve market making in challenging environments. The study also emphasizes the importance of understanding market conditions when deploying algorithmic trading strategies. 2024-10-17T11:09:18Z 2024-10-17T11:09:18Z 2024-10-17T11:09:18Z 2024-10-04T13:10:35Z 2024-09 https://hdl.handle.net/1946/48676 en Fjármál fyrirtækja Meistaraprófsritgerðir Námsaðferðir Fjármálamarkaðir Lausafé Corporate finance Reinforcement learning Financial markets Liquidity (Economics) Market making in dry waters : reinforcement learning strategies for market making in illiquid markets Thesis Master's