Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
arXiv:2603.05917v1 Announce Type: new 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 capt
arXiv:2603.05917v1 Announce Type: new 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%.
Executive Summary
This article presents an innovative stock market prediction framework integrating a node transformer architecture with BERT-based sentiment analysis. The proposed model leverages graph-based modeling to capture intricate patterns and cross-sectional dependencies among stocks, while BERT sentiment analysis enhances predictions with social media insights. Experiments on S&P 500 stocks demonstrate improved accuracy and robustness compared to baseline models. The model's performance is statistically validated through paired t-tests. The integrated framework offers a promising solution for investors, financial institutions, and policymakers seeking to navigate complex market environments.
Key Points
- ▸ The article presents a novel stock market prediction framework combining node transformer architecture with BERT-based sentiment analysis.
- ▸ The model represents the stock market as a graph structure, capturing relationships among stocks, sectors, and supply chain connections.
- ▸ BERT sentiment analysis is integrated with quantitative market features through attention-based fusion, enhancing predictions with social media insights.
Merits
Improved Accuracy
The integrated model achieves a mean absolute percentage error (MAPE) of 0.80% for one-day-ahead predictions, outperforming ARIMA and LSTM models.
Robustness in High-Volatility Periods
The model maintains MAPE below 1.5% during high-volatility periods, whereas baseline models exceed 2%.
Enhanced Sentiment Analysis
Sentiment analysis reduces prediction error by 10% overall and 25% during earnings announcements.
Demerits
Data-Intensive Requirements
The model relies on extensive historical market data and social media posts, which can be challenging to obtain and process.
Interpretability and Explainability
The complex architecture and integration of multiple components may limit interpretability and explainability of the model's predictions.
Scalability and Generalizability
The model's performance may not generalize to other markets or time periods, and its scalability to larger datasets requires further investigation.
Expert Commentary
While the article presents a promising stock market prediction framework, its limitations and potential biases warrant further investigation. The model's reliance on extensive historical data and social media posts raises concerns about data quality and availability. Additionally, the complex architecture may limit interpretability and explainability, which are essential for regulatory and investment purposes. Nevertheless, the integrated framework offers a valuable contribution to the field, highlighting the importance of addressing complexity in financial markets and incorporating alternative data sources into financial modeling. Future research should focus on addressing these limitations and exploring the model's scalability and generalizability to other markets and time periods.
Recommendations
- ✓ Future research should investigate the model's scalability and generalizability to larger datasets and alternative markets.
- ✓ The authors should provide more detailed explanations of the model's architecture and the attention-based fusion mechanism used to integrate BERT sentiment analysis with quantitative market features.
