Skip to search boxSkip to navigationSkip to main content

Revolutionising financial portfolio management: the non-stationary transformer's fusion of macroeconomic indicators and sentiment analysis in a deep reinforcement learning framework

  • Yuchen Liu
    ,
  • Daniil Mikriukov
    ,
  • Owen Christopher Tjahyadi
    ,
  • Gangmin Li
    ,
  • Terry R. Payne
    ,
  • Yong Yue
  • University of Liverpool
    ,
  • Xi'an Jiaotong-Liverpool University
    ,
  • University of Alaska Anchorage
Research Output:
Contribution to journal
Article
 Peer-review

Open access

Abstract

In the evolving landscape of portfolio management (PM), the fusion of advanced machine learning techniques with traditional financial methodologies has opened new avenues for innovation. Our study introduces a cutting-edge model combining deep reinforcement learning (DRL) with a non-stationary transformer architecture. This model is designed to decode complex patterns in financial time-series data, enhancing portfolio management strategies with deeper insights and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing the model’s ability to navigate the complexities of asset management. Rigorous testing demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and sophisticated algorithmic approaches in mastering the nuances of PM.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Article number

274

Journal (Volume, Issue Number)

Applied Sciences (Switzerland) (Volume 14, Issue 1)

Publication milestones

  • Accepted/In press - 26/12/2023
  • Published - 28/12/2023

Publication status

Published - 28/12/2023

ISSN

2076-3417

Publication IDs

  • handle.net: 10547/626183
  • Scopus: 85192449885

Access to documents

Publication metrics

Metrics

Download statistics
Download count
5

PlumX, opens in new tab

Mentions
1
Captures
52
19