Year: 2024
Type: preprint
Abstract: This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various decay kernels, we show that our algorithm successfully approximates the optimal execution strategy. Additionally, the proposed algorithm demonstrates adaptability to evolving market conditions, where parameters fluctuate over time. Our findings also show that modern reinforcement learning algorithms can provide a solution that reduces the need for frequent and inefficient human intervention in optimal execution tasks.
Source: arXiv (Cornell University)
Authors: Micheli, Alessandro, Monod, Mélodie
Institution: Imperial College London
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
Funder: Imperial College London
Award: G4591806611
OpenAlex ID: W4403579761