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

Paper: arXiv 2603.05917

Authors: Mohammad Al Ridhawi, Mahtab Haj Ali, Hussein Al Osman

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, including fundamental analysis and technical indicators, 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 information from social media posts and combines it with quantitative market features through attention-based fusion mechanisms. The node transformer processes historical market data while capturing both temporal evolution and cross-sectional dependencies among stocks. Experiments conducted 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. The inclusion of sentiment analysis reduces prediction error by 10% overall and 25% during earnings announcements, while the graph-based architecture 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 the significance of these improvements (p < 0.05 for all comparisons). The model maintains lower error during high-volatility periods, achieving MAPE of 1.50% while baseline models range from 1.60% to 2.10%.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a solid integration of Graph Transformer networks with fine-tuned BERT sentiment analysis for multi-modal stock prediction. Empirical validation is strong, featuring extensive historical data, baseline comparisons, and statistical significance testing via paired t-tests.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Stock Market Prediction Accuracy & Capture Inter-Stock Dependencies] --> B[Data Inputs: Historical S&P 500 Data 1982-2025 & Social Media Posts]
    B --> C[Sentiment Extraction & Graph Modeling: Fine-Tuned BERT & Graph Structure Setup]
    C --> D[Model Processing: Node Transformer with Attention-Based Sentiment Fusion]
    D --> E[Model Evaluation: 1-Day-Ahead Price Forecasting & Statistical Validation]
    E --> F[Key Outcomes: 0.80% MAPE, 65% Directional Accuracy & Superior Volatility Performance]

Cited References (33)

1. Prospect theory: An analysis of decision under risk (1979)
2. Efficient capital markets: A review of theory and empirical work (1970)
3. Technical analysis of the financial markets: A comprehensive guide to trading methods and applications (1999)
4. Stock market prediction via deep learning techniques: A survey — → 2212.12717
5. Deep learning with long short-term memory networks for financial market predictions (2018)
6. Attention is all you need (2017)
7. A comprehensive survey on graph neural networks (2020)
8. NodeFormer: A scalable graph structure learning transformer for node classification (2022)

1 of 33 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.