4 papers
Can Reinforcement Learning Efficiently Discover Price Manipulation?
Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio Lillo
In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that…
Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning
Fabrizio Lillo, Andrea Macrì
The use of reinforcement learning algorithms in financial trading is becoming increasingly prevalent. However, the autonomous nature of these algorithms can lead to unexpected outc…
Deep reinforcement learning for optimal trading with partial information
Andrea Macrì, Sebastian Jaimungal, Fabrizio Lillo
Reinforcement Learning (RL) applied to financial problems has been the subject of a lively area of research. The use of RL for optimal trading strategies that exploit latent inform…
Deviations from Tradition: Stylized Facts in the Era of DeFi
Daniele Maria Di Nosse, Federico Gatta, Fabrizio Lillo +1
Decentralized Exchanges (DEXs) are now a significant component of the financial world where billions of dollars are traded daily. Differently from traditional markets, which are ty…