Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Revolutionizing Financial Portfolio Management: The NSTD Model’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework

Yuchen Liu
ORCID logo ,
Daniil Mikriukov
ORCID logo ,
Owen Christopher Tjahyadi
,
Gangmin Li
ORCID logo ,
Terry R. Payne
ORCID logo ,
Yong Yue
ORCID logo ,
Kamran Siddique
ORCID logo ,
Ka Lok Man
*
Version 1 : Received: 12 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (09:59:50 CET)

A peer-reviewed article of this Preprint also exists.

Liu, Y.; Mikriukov, D.; Tjahyadi, O.C.; Li, G.; Payne, T.R.; Yue, Y.; Siddique, K.; Man, K.L. Revolutionising Financial Portfolio Management: The Non-Stationary Transformer’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework. Appl. Sci. 2024, 14, 274. Liu, Y.; Mikriukov, D.; Tjahyadi, O.C.; Li, G.; Payne, T.R.; Yue, Y.; Siddique, K.; Man, K.L. Revolutionising Financial Portfolio Management: The Non-Stationary Transformer’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework. Appl. Sci. 2024, 14, 274.

Abstract

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

Keywords

Portfolio Management (PM); Deep Reinforcement Learning (DRL); Non-Stationary 13 Transformer; Sequential Processing; Data Heterogeneity; Market Uncertainty; Diverse Knowledge 14 Integration; Multimodal Learning15

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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