Deep Learning Models for Financial Data Analysis: A Focused Review of Recent Advances

This paper presents a focused review of recent academic advances in the application of deep learning techniques to algorithmic trading. While traditional machine learning models have long been used in financial forecasting, the last decade has seen a rapid expansion in the use of deep learning architectures due to their ability to model non-linear dependencies, learn hierarchical features, and process high-dimensional sequential data. We categorize and synthesize developments across three primary paradigms: supervised deep learning models for price prediction and signal generation, unsupervised and generative approaches for feature extraction and data augmentation, and reinforcement learning agents for decision-making in trading environments. By analyzing over 30 recent peer-reviewed studies, we highlight how modern models such as attention-based networks, graph neural networks, and deep Q-learning have enhanced the robustness and adaptability of trading algorithms. We also discuss key limitations—including overfitting, data non-stationarity, and lack of interpretability—and summarize efforts to address them. This review serves as a resource for researchers seeking a clear, academically grounded perspective on how deep learning is currently reshaping algorithmic trading systems.