MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management

TL;DR

This paper introduces MIGT, a novel transformer-based framework with a gated attention mechanism for financial portfolio management, achieving significant improvements in returns and stability over existing strategies.

Contribution

The study proposes a new Memory Instance Gated Transformer framework with a Gated Instance Attention module, enhancing DRL-based portfolio management's performance and stability.

Findings

At least 9.75% increase in cumulative returns

Minimum 2.36% improvement in risk-return ratios

Outperforms fifteen other strategies on key financial metrics

Abstract

Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is widely used, the stock market is more volatile and dynamic as it is affected by several factors such as global events and investor sentiment. Therefore, it remains a challenge to construct a DRL-based portfolio management framework with strong return capability, stable training, and generalization ability. This study introduces a new framework utilizing the Memory Instance Gated Transformer (MIGT) for effective portfolio management. By incorporating a novel Gated Instance Attention module, which combines a transformer variant, instance normalization, and a Lite Gate Unit, our approach aims to maximize investment returns while ensuring the learning process's stability and reducing outlier impacts. Tested on the Dow Jones Industrial Average 30, our framework's performance is evaluated against fifteen other strategies using key financial metrics like the cumulative return and risk-return ratios (Sharpe, Sortino, and Omega ratios). The results highlight MIGT's advantage, showcasing at least a 9.75% improvement in cumulative returns and a minimum 2.36% increase in risk-return ratios over competing strategies, marking a significant advancement in DRL for portfolio management.