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Found 10 papers, 2 papers with code
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting
We also adapt traditional RL methods to real-life situations by considering only past data for the training sets.
Bridging the gap between Markowitz planning and deep reinforcement learning
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go.
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations?
Time your hedge with Deep Reinforcement Learning
Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions?
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning
Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving).
NGO-GM: Natural Gradient Optimization for Graphical Models
This paper deals with estimating model parameters in graphical models.
BCMA-ES: A Bayesian approach to CMA-ES
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm.
BCMA-ES II: revisiting Bayesian CMA-ES
We prove that the expected covariance should be lower in the normal Wishart prior model because of the convexity of the inverse.
Trade Selection with Supervised Learning and OCA
We derive this new method using coordinate ascent optimization and using block variables.
Feature selection with optimal coordinate ascent (OCA)
OCA brings substantial differences and improvements compared to previous coordinate ascent feature selection method: we group variables into block and individual variables instead of a binary selection.
