Deep Learning in Asset Management: Architectures, Applications ...
A new survey finds that standard deep learning models from other fields often fail in finance due to unique challenges like unpredictable markets and fleeting patterns. The authors propose organizing models by economic problems and stress the need for finance-specific neural networks that use domain knowledge. Notably, models built for financial realities outperform generic ones. The study also highlights ongoing issues such as data scarcity and the need for explainable, trustworthy AI in financial applications.
What it examines
This survey reviews how deep learning models are used in asset management, focusing on their unique challenges in finance like noisy data and changing market conditions. It highlights the need for finance-specific model designs and practical considerations for real-world deployment, aiming to guide both researchers and practitioners.
What it concludes
Deep learning can improve forecasting, portfolio optimization, and risk management in finance, but models must be robust, explainable, and economically grounded. Future research should focus on trustworthy AI, adapting to market changes, and better data integration. Applications include smarter investment strategies, risk control, and financial decision support.
Evidence objects
Standard deep learning models from other fields often fail in finance, as they ignore market unpredictability, low signal-to-noise ratios, and the fleeting nature of profitable patterns, the survey reveals.
key_findings bullet 1 · key_findings · validation V0
The authors propose a critical framework organizing models by economic problems, not technical design, and introduce terms like 'decision-informed learning' and 'finance-native architectures' to stress finance-specific, interpretable, and robust neural networks.
key_findings bullet 2 · key_findings · validation V0
Tailored models that embed economic reasoning, multi-modal data, and adaptive mechanisms consistently outperform generic approaches, but challenges remain: data scarcity, operational hurdles, and the urgent need for trustworthy, explainable AI in finance.
key_findings bullet 3 · key_findings · validation V0
This paper offers a structured, critical survey of deep learning in investment management, emphasizing finance-specific model adaptations and integration of LLMs and multi-modal data. Its originality stems from synthesizing current research and providing forward-looking analysis, making it compelling for readers seeking timely insights, despite not introducing fundamentally new algorithms or methodologies.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
The asset management industry, which is responsible for allocating trillions of dollars in capital and managing global savings, is at a critical juncture. The
Source row: 539 · abstract type: snippet