Bridging the gap between Markowitz planning and deep reinforcement learning

09/30/2020
by   Eric Benhamou, et al.
0

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. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

08/04/2020

A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles

Deep reinforcement learning (DRL) is becoming a prevalent and powerful m...
09/07/2020

Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning

Deep reinforcement learning (DRL) has reached super human levels in comp...
02/01/2022

Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

The recent growth of emergent network applications (e.g., satellite netw...
08/12/2019

A review on Deep Reinforcement Learning for Fluid Mechanics

Deep reinforcement learning (DRL) has recently been adopted in a wide ra...
05/19/2021

Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning

Machine Learning (ML) has been embraced as a powerful tool by the financ...
11/10/2021

A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection

This work proposes a novel portfolio management technique, the Meta Port...
03/24/2022

Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology

A general control policy framework based on deep reinforcement learning ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.