Exploring the Landscape of Neural Networks for Stock Market Prediction

Stock price prediction is a paramount task in revenue forecasting, providing vital outcomes to retail investors, businesses, traders and regulators. The inherent nonlinearity, high volatility, and constantly evolving nature of financial time series data present formidable challenges. This literature review examines various state-of-the-art studies focusing on stock price forecasting techniques, ranging from traditional models like Autoregressive Integrated Moving Average (ARIMA) to advanced deep learning architectures such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and hybrid models like Complementary Ensemble Empirical Mode Decomposition with CNN and LSTM (CEEMD-CNN-LSTM). Key innovations include the integration of sentiment analysis using Bidirectional Encoder Representations from Transformers (BERT), attention mechanisms, and multi-view learning approaches, which collectively enhance predictive accuracy. This study also addresses widely used datasets, assessment measures, and enduring issues including model interpretability and data sparsity. The review concludes by identifying future research directions, emphasizing the need for more comprehensive hybrid models, real-time prediction systems, and standardized evaluation frameworks to further advance the field of revenue forecasting.