Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis

• URL: http://arxiv.org/abs/2603.05917v1
• Date: Fri, 06 Mar 2026 05:15:22 GMT
• Title: Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
• Abstract summary: This paper presents an integrated framework combining a node transformer architecture with BERT-based sentiment analysis for stock price forecasting.<n>The proposed model represents the stock market as a graph structure where individual stocks form nodes and edges capture relationships.<n> Experiments on 20 S&P 500 stocks spanning January 1982 to March 2025 demonstrate that the integrated model achieves a mean absolute percentage error (MAPE) of 0.80% for one-day-ahead predictions.
• Score: 1.2362187555287152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market environments characterized by noise, non-stationarity, and behavioral dynamics. Traditional forecasting methods often fail to capture the intricate patterns and cross-sectional dependencies inherent in financial markets. This paper presents an integrated framework combining a node transformer architecture with BERT-based sentiment analysis for stock price forecasting. The proposed model represents the stock market as a graph structure where individual stocks form nodes and edges capture relationships including sectoral affiliations, correlated price movements, and supply chain connections. A fine-tuned BERT model extracts sentiment from social media posts and combines it with quantitative market features through attention-based fusion. The node transformer processes historical market data while capturing both temporal evolution and cross-sectional dependencies among stocks. Experiments on 20 S&P 500 stocks spanning January 1982 to March 2025 demonstrate that the integrated model achieves a mean absolute percentage error (MAPE) of 0.80% for one-day-ahead predictions, compared to 1.20% for ARIMA and 1.00% for LSTM. Sentiment analysis reduces prediction error by 10% overall and 25% during earnings announcements, while graph-based modeling contributes an additional 15% improvement by capturing inter-stock dependencies. Directional accuracy reaches 65% for one-day forecasts. Statistical validation through paired t-tests confirms these improvements (p < 0.05 for all comparisons). The model maintains MAPE below 1.5% during high-volatility periods where baseline models exceed 2%.

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