on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and … Bayesian learning strategy, we compare it with an optimal nonlearning strategy that keeps the drift constant at all times. In … order to emphasize the prevalence of the Bayesian learning strategy above the nonlearning one in different situations, we …

Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model …

to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model …

returns. This project seeks to address the Tactical Asset Allocation problem by employing Deep Reinforcement Learning (DRL …) Algorithms in a Machine Learning Environment as well as employing Neural Network Autoencoders for selection of portfolio assets …

This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a …

We investigated investment-portfolio optimization using Reinforcement Learning (RL) with risk assessment. Due to market …

We adopt deep neural networks and reinforcement learning towards dynamic bottom-up portfolio construction by directly …