Deep Learning in Asset Management: Architectures, Applications, and Challenges

Abstract

This paper provides a critical survey of the essential considerations for applying deep learning models within the financial domain, particularly in asset management. While deep learning has shown immense promise, its direct application is hindered by formidable challenges unique to finance, including low signal-to-noise ratios, pervasive non-stationarity in time series data, and the adversarial and adaptive nature of markets where discovered patterns quickly decay. This work serves as a guide for both researchers and practitioners, navigating the gap between the idealized performance reported in academic studies and the significant hurdles to robust, real-world deployment. Rather than developing a rigid classification or forecasting system, the authors survey the crucial points to consider across the entire deep learning pipeline. They begin by examining the inherent model–data mismatch that arises when standard architectures like CNNs, RNNs, and Transformers are applied to financial time series. They then review key application areas not to provide an exhaustive list, but to highlight the domain-specific adaptations and practical trade-offs required for success. The survey further addresses a suite of practical considerations often overlooked in theoretical research, including the challenges of data acquisition and quality, rigorous model validation to prevent overfitting, the impact of transaction costs, and the operational hurdles of integrating these technologies into legacy systems. Finally, the authors outline a forward-looking perspective on the most significant open challenges and argue that the future of the field depends on moving beyond a narrow focus on predictive accuracy toward a more holistic emphasis on causality, robustness, and economic grounding.