5 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…
Online Continual Learning for Time Series: a Natural Score-driven Approach
Edoardo Urettini, Daniele Atzeni, Ioanna-Yvonni Tsaknaki +1
Online continual learning (OCL) methods adapt to changing environments without forgetting past knowledge. Similarly, online time series forecasting (OTSF) is a real-world problem w…
Tackling estimation risk in Kelly investing using options
Fabrizio Lillo, Piero Mazzarisi, Ioanna-Yvonni Tsaknaki
The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. Howev…
Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the ti…
Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistenc…