Revolutionising Financial Portfolio Management: The Non-Stationary Transformer’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework

Author:

Liu Yuchen12ORCID,Mikriukov Daniil3ORCID,Tjahyadi Owen Christopher3ORCID,Li Gangmin4ORCID,Payne Terry R.2ORCID,Yue Yong12ORCID,Siddique Kamran5ORCID,Man Ka Lok12ORCID

Affiliation:

1. Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

2. Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK

3. School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

4. Faculty of Creative Arts, Technologies and Science, University of Bedfordshire, Luton LU1 3JU, UK

5. Department of Computer Science & Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3