Abstract: Given the high complexity of financial markets and the inherently noisy,non-linear nature of their data,artificial intelligence(AI),particularly machine learning and deep learning,has emerged as a core driving force in financial data prediction.This paper systematically summarizes the latest research progress and core methodologies in this domain over the past three years.In traditional machine learning,the trend has shifted from applying single learners toward sophisticated model fusion strategies,such as Stacking,and hybrid paradigms that integrate optimization algorithms for automated feature selection and hyperparameter tu-ning.Deep learning applications demonstrate a clear evolutionary trajectory,starting with foundational Recurrent Neural Networks(RNNs) enhanced by data decomposition and attention mechanisms,and progressing to hybrid architectures like CNN-RNN for capturing multi-dimensional features.This paper further details the adoption of cutting-edge models:Graph Neural Networks(GNNs) for modeling entity relationships,Transformers for capturing long-range dependencies,and Deep Reinforcement Lear-ning(DRL) for shifting the paradigm from prediction to autonomous decision-making.Furthermore,the review outlines key feature engineering techniques and introduces an innovative “structured neural network modeling” paradigm,which proposes aligning the model’s architecture with the intrinsic topology of financial products to enhance interpretability.Finally,this paper synthesizes the core challenges facing the field—including data quality,model robustness,and interpretability－and provides a forward-looking perspective on future research directions such as deep multi-modal fusion,causal inference,financial Large Language Models(LLMs),and explainable AI(XAI).

Key words: Financial forecasting, Time series, Portfolio management, Artificial intelligence, Machine learning, Deep learning