Adversarial recovery of agent rewards from latent spaces of the limit order book
J Roa-Vicens, Y Wang, V Mison, Y Gal… - arXiv preprint arXiv …, 2019 - arxiv.org
Inverse reinforcement learning has proved its ability to explain state-action trajectories of
expert agents by recovering their underlying reward functions in increasingly challenging …
expert agents by recovering their underlying reward functions in increasingly challenging …
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
J Roa-Vicens, C Chtourou, A Filos, F Rullan… - arXiv preprint arXiv …, 2019 - arxiv.org
Multi-agent learning is a promising method to simulate aggregate competitive behaviour in
finance. Learning expert agents' reward functions through their external demonstrations is …
finance. Learning expert agents' reward functions through their external demonstrations is …
Portfolio optimization for cointelated pairs: SDEs vs Machine learning
B Mahdavi-Damghani, K Mustafayeva… - Algorithmic …, 2021 - journals.sagepub.com
With the recent rise of Machine Learning (ML) as a candidate to partially replace classic
Financial Mathematics (FM) methodologies, we investigate the performances of both in …
Financial Mathematics (FM) methodologies, we investigate the performances of both in …
[PDF][PDF] Portfolio Optimization for Cointelated Pairs: Financial Mathematics or Machine Learning?
We investigate the problem of dynamic portfolio optimization in continuous-time, finite-
horizon setting for a portfolio of two stocks. The stocks follow the Cointelation model recently …
horizon setting for a portfolio of two stocks. The stocks follow the Cointelation model recently …