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Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization

Abdullah Ali Salamai¹

¹Department of Management, Applied College, Jazan University, Jazan, KSA, Saudi Arabia.

Neural Networks : the Official Journal of the International Neural Network Society

|July 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep reinforcement learning (RL) model for automated portfolio management (PM). The framework enhances traditional RL by incorporating asset relationships, leading to improved risk minimization and return maximization in stock markets.

Keywords:

Curriculum learning Deep reinforcement learning Graph neural networks Portfolio management Transformer network

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Area of Science:

Computational Finance
Artificial Intelligence
Machine Learning

Background:

Portfolio management (PM) involves optimizing asset allocation for profitability under risk constraints.
Reinforcement learning (RL) is widely used for automated PM, but existing methods often ignore inter-asset relationships.
Market dynamics necessitate advanced models that capture both temporal price variations and asset interdependencies.

Purpose of the Study:

To develop a novel deep learning model for portfolio management that addresses the limitations of current RL approaches.
To integrate temporal price analysis with the learning of inter-asset relationships for enhanced decision-making.
To improve automated portfolio management by minimizing risk and maximizing cumulative returns.

Main Methods:

A novel deep model combining a temporal learner for historical prices and a relation graph learner (RGL) for inter-asset relationships.
Integration of these learners into a curriculum reinforcement learning (RL) scheme, formulating PM as a curriculum Markov Decision Process.
Development of an adaptive curriculum policy to enable the RL agent to dynamically adjust risk and return objectives.

Main Results:

The proposed framework successfully learns temporal representations of asset prices and their interrelationships.
Experiments on S&P500, NYSE, and NASDAQ data demonstrate superior performance compared to existing RL solutions.
The model effectively minimizes risk value while maximizing cumulative returns in portfolio management.

Conclusions:

The novel deep RL model offers a significant advancement in automated portfolio management by considering asset relationships.
The integrated approach of temporal and relational learning within a curriculum RL framework proves effective.
This research provides a valuable tool for improving long-term performance in dynamic stock market environments.