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

Deep learning is reshaping how financial professionals manage investments, with recent research (2018-2023) showing these AI systems can improve both portfolio performance and price forecasting accuracy, though the paper notes the specific magnitude of these improvements varies across studies.

Explainable AI is becoming essential for responsible financial AI deployment—the review identifies the integration of explainable artificial intelligence (XAI) as an emerging trend, addressing a critical concern that financial institutions need to understand why AI systems make investment decisions, not just what decisions they make.

AI models are increasingly incorporating real-world factors beyond traditional financial data, including ESG (Environmental, Social, Governance) indicators and sentiment analysis from alternative data sources, meaning investment algorithms now consider broader stakeholder and sustainability concerns alongside traditional metrics.

Hybrid AI approaches combining multiple techniques show promise, with transformer-based architectures and deep reinforcement learning representing new developments that may outperform single-method approaches, suggesting the field is moving toward more sophisticated, multi-faceted AI systems.

The rapid growth in financial AI research (612 relevant studies identified) indicates this is an active, evolving field, but the paper's acknowledgment of "limitations" suggests investors and regulators should remain cautious about assuming all deep learning applications in finance are equally reliable or well-validated.