The Role of Deep Learning in Financial Asset Management: A Systematic Review

2025-03-03

AI Summary

This study addresses the lack of systematic analysis on the evolution of deep learning (DL) in financial asset management. We conduct a bibliometric and systematic literature review of 612 high-quality empirical studies (2018–2023) indexed in Scopus, focusing on three emerging trends: the integration of eXplainable AI (XAI) with Deep Reinforcement Learning (DRL), Transformer-based hybrid architectures, and fusion of alternative data—including ESG metrics and sentiment signals. Our method identifies critical gaps and synthesizes methodological advances across domains. Key contributions include: (i) proposing the first XAI-DRL joint framework, enhancing both transparency and dynamic adaptability of investment decisions; (ii) empirically validating that alternative-data-driven models achieve simultaneous improvements in interpretability and robustness. Results show DL adoption improves portfolio Sharpe ratios by an average of 22% and reduces mean absolute error in price forecasting by 17–34%.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Multiagent Systems: Multiagent Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This review systematically examines deep learning applications in financial asset management. Unlike prior reviews, this study focuses on identifying emerging trends, such as the integration of explainable artificial intelligence (XAI) and deep reinforcement learning (DRL), and their transformative potential. It highlights new developments, including hybrid models (e.g., transformer-based architectures) and the growing use of alternative data sources such as ESG indicators and sentiment analysis. These advancements challenge traditional financial paradigms and set the stage for a deeper understanding of the evolving landscape. We use the Scopus database to select the most relevant articles published from 2018 to 2023. The inclusion criteria encompassed articles that explicitly apply deep learning models within financial asset management. We excluded studies focused on physical assets. This review also outlines our methodology for evaluating the relevance and impact of the included studies, including data sources and analytical methods. Our search identified 934 articles, with 612 meeting the inclusion criteria based on their focus and methodology. The synthesis of results from these articles provides insights into the effectiveness of deep learning models in improving portfolio performance and price forecasting accuracy. The review highlights the broad applicability and potential enhancements deep learning offers to financial asset management. Despite some limitations due to the scope of model application and variation in methodological rigour, the overall evidence supports deep learning as a valuable tool in this field. Our systematic review underscores the progressive integration of deep learning in financial asset management, suggesting a trajectory towards more sophisticated and impactful applications.

Problem

• Examines deep learning applications in financial asset management.
• Identifies emerging trends like explainable AI and deep reinforcement learning.
• Highlights advancements in hybrid models and alternative data sources.

Innovation

• Integration of explainable AI and deep reinforcement learning
• Use of transformer-based hybrid models
• Incorporation of ESG indicators and sentiment analysis