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Found 15 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).
Omega and Sharpe ratio
We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions, the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio.
Variance Reduction in Actor Critic Methods (ACM)
After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators.
NGO-GM: Natural Gradient Optimization for Graphical Models
This paper deals with estimating model parameters in graphical models.
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)
In particular, we prove in this paper that gradient policy method can be cast as a supervised learning problem where true label are replaced with discounted rewards.
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.
A discrete version of CMA-ES
This allows creating a version of CMA ES that can accommodate efficiently discrete variables.
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.
Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets
In this paper, we revisit the Kalman filter theory.
