Comparative Analysis of LSTM, GRU, and Transformer Models for Tesla Stock Price Prediction: An Empirical Study
LSTM models demonstrate superior performance in predicting Tesla stock prices compared to GRU and Transformer models, achieving a 94% accuracy rate in an empirical study.
CausalStock: Predicting Stock Movements by Identifying Causal Relationships Between Stocks Using News and Historical Price Data
CausalStock is a novel deep learning model that leverages news sentiment analysis and causal discovery to predict stock market movements with improved accuracy and explainability compared to traditional correlation-based methods.
The Impact of Bank Fundamentals on Stock Price Co-Movement in the Chinese A-Share Market: A High-Frequency Data Analysis
Banks exhibiting a balance between risk-taking in core operations, adherence to regulatory requirements, and strategic growth initiatives exert a stronger influence on the stock price movements of other banks in the Chinese A-Share market.
Application of a Hybrid LSTM and ANN Model for Improved Stock Market Prediction
Combining LSTM and ANN models in a hybrid architecture significantly improves the accuracy of stock market predictions compared to using either model independently.
Predicting Stock Market Trends Using Sentiment Analysis and Random Forest: An Evaluation of the SARF Model
Integrating sentiment analysis from financial news articles with a Random Forest model, a novel approach called Sentiment-Augmented Random Forest (SARF), demonstrates improved accuracy in predicting stock market movements compared to traditional Random Forest and LSTM models.
SAMBA: An Efficient Deep Learning Model for Stock Price Prediction Using Bidirectional Mamba and Adaptive Graph Convolutional Networks
This research paper introduces SAMBA, a novel deep learning model for predicting stock market returns that combines the efficiency of the Mamba architecture with the relational modeling capabilities of graph neural networks, achieving state-of-the-art prediction accuracy while maintaining low computational complexity.
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