Review of Neural Network-Based Approaches for Stock Market Price and Volatility Prediction

Abstract

Stock market price prediction and volatility forecasting are challenging financial forecasting problems because stock prices are generally characterized by nonlinear, noisy, non-stationary, and rapidly changing patterns. The increasing availability of historical market data, technical indicators, financial information, and alternative data has encouraged the application of neural network and deep learning techniques for extracting complex temporal relationships from financial time series. This review, entitled “Review of Neural Network-Based Approaches for Stock Market Price and Volatility Prediction,” presents a comprehensive overview of neural network methodologies applied to stock price forecasting, market trend prediction, and volatility estimation. The review examines conventional Artificial Neural Networks (ANNs) along with advanced architectures such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks (CNNs), Bidirectional LSTM, hybrid neural networks, attention-based models, and Transformer architectures. Recent literature indicates that neural networks are increasingly being used to model nonlinear temporal dependencies and integrate heterogeneous financial information. Particular attention is given to volatility forecasting because volatility is an important indicator for financial risk assessment and portfolio management. Previous systematic research has identified challenges related to inconsistent datasets, volatility definitions, experimental settings, and difficulties in making meaningful comparisons among neural-network-based models.
Furthermore, the review discusses major challenges such as market non-stationarity, data leakage, overfitting, changing market regimes, limited interpretability, and differences in backtesting practices. Recent reviews also highlight emerging approaches involving Transformers, Graph Neural Networks, multimodal data, and hybrid architectures. The study concludes by identifying future research directions involving explainable artificial intelligence, robust cross-market validation, multimodal financial data, uncertainty-aware forecasting, and standardized evaluation frameworks. Overall, this review provides a structured foundation for researchers seeking to understand the development, advantages, limitations, and future potential of neural network-based approaches for stock price and volatility prediction.