Paper: arXiv 2503.01591

Authors: Pedro Reis, Ana Paula Serra, João Gama

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.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper is a systematic literature review analyzing 612 deep learning articles in asset management published between 2018 and 2023. While it contains virtually no mathematical derivations, it employs a rigorous metadata selection process (Scopus database methodology) and offers a comprehensive, well-structured synthesis of recent empirical trends, including XAI, alternative data, and DRL.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Systematic review of Deep Learning (DL) in Asset Management"] --> B["Data Collection:<br>Scopus Database (2018–2023)<br>N = 934 articles"]
    
    B --> C["Screening & Selection:<br>Apply inclusion/exclusion criteria<br>N = 612 relevant articles"]
    
    C --> D["Synthesis & Analysis:<br>Evaluate DL, DRL, XAI, Hybrid Models & Alternative Data (ESG, Sentiment)"]
    
    D --> E["Key Outcomes:<br>• Enhanced portfolio performance & forecasting<br>• Rise of XAI and Transformer models<br>• Shift toward non-traditional data integration"]