RELATIONAL STOCK MARKET FORECASTING WITH MULTI-MODAL GRAPH NEURAL NETWORKS: CAPTURING SENTIMENT CONTAGION ALONG SUPPLY CHAINS

Mohit Shrivastava; Ayaz Ahmed; Md. Khaja Mohiddin

doi:10.29121/shodhai.v3.i1.2026.81

Authors

Mohit Shrivastava Ph.D. Scholar, CSE Department, Kalinga University, Raipur (CG), India
Ayaz Ahmed Associate Professor, CSE Department, Kalinga University, Raipur (CG), India
Md. Khaja Mohiddin Associate Professor, ECE Department, Bhilai Institute of Technology, Raipur (CG), and AICTE Industry Fellow Member, Garuda Aerospace Limited, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.29121/shodhai.v3.i1.2026.81

Keywords:

Graph Neural Networks (Gnn), Sentiment Contagion, Multi-Modal Fusion, Supply Chain Intelligence, Financial Time Series Forecasting, Temporal Graph Convolution, Finbert, Relational Learning

Abstract

Traditional financial time series forecasting models mostly view each equity as independent or ignore the complicated network relationship in today's global markets. Recent progress in multi-modal deep learning has enabled the effective combination of textual sentiment and numerical price information, but at the “single-asset” level, these approaches are unable to capture the effects of related supply chains and industry connections. In this paper, we introduce a new framework of Relational Sentiment-Temporal Graph Neural Network (RST-GNN) for modeling the market as a temporal and heterogeneous graph. In this architecture, companies are expressed as nodes and explicit supplier-customer relationships (edges) and common sector classification (edges) are present. We use a two-step learning approach: first, we use the Domain-Specific Sentiment Encoder FinBERT to extract high dimensional linguistic features from unstructured financial news, and then, we use the Temporal Graph Convolutional Network (T-GCN) to propagate the sentiment “shocks” on the network to capture the Sentiment Contagion. Through a Graph Attention Mechanism (GAT), the model dynamically identifies the different levels of influence among interdependent firms, and hence, the impact of a negative news event on a primary supplier on the price correction of its downstream customers. Extensive experiments were carried out on a data set of the stocks of S&P 500 combined with the supply chain mapping information. The results show that the RST-GNN framework clearly outperforms state-of-the-art non-relational models, including LSTMs and Transformers, especially in the challenging situations of high market turbulence to foretell “lead-lag” effects. The results indicate that relational intelligence can be an important factor in institutional level risk management and portfolio optimization, offering a more comprehensive picture of the dynamics of the markets compared with conventional stand-alone forecasting.

References

Araci, D. (2019). FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models. arXiv. https://doi.org/10.48550/arXiv.1908.10063

Bahadure, N. B., Sahare, O., Shukla, N., Mandal, R., Pandey, P., Patni, J. C., and Mohiddin, M. K. (n.d.). Air Quality Index Prediction for Clearer Skies Using Improved Long Short-Term Memory. Intelligent Decision Technologies, 19(1), 443–452. https://doi.org/10.3233/IDT-240982

Chen, H., Ying, S., and Zhao, L. (2020). Relational Stock Prediction Via Multi-Graph Network. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data) (1245–1252). IEEE. https://doi.org/10.1109/BigData50022.2020.9378125

Feng, F., He, X., Wang, J., Luo, C., Ng, Y., and Chua, T. S. (2019). Temporal Relational Ranking for Stock Prediction. ACM Transactions on Information Systems, 37(2), Article 27, 1–21. https://doi.org/10.1145/3309547

Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 1025–1035). Curran Associates Inc.

Hou, C., Wu, J., and Xu, M. (2023). A Supply Chain-Based Graph Neural Network for Stock Market Prediction. Journal of Forecasting, 42(3), 567–582. https://doi.org/10.1002/for.2912

Hwang, J., Ryu, J., and Kim, S. (2021). Stock Price Prediction Using Supply Chain Knowledge Graph and Graph Convolutional Network. IEEE Access, 9, 139261–139272. https://doi.org/10.1109/ACCESS.2021.3118320

Kim, H., Kim, J., and Kang, S. (2022). Analyzing the Ripple Effect of Financial News Using Graph Neural Networks. Finance Research Letters, 45, 102143. https://doi.org/10.1016/j.frl.2021.102143

Kipf, T. N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017) (1–14).

Kumar, G. A., Lavanya, B. M., Mohiddin, M. K., and Mitra, S. (2025). IoT-Enabled Recurrent Spatio-Temporal Adaptive Attention of Temporal Convolutional Transformer with Continual Learning for Dairy Farming. Expert Systems with Applications, 282, 127712. https://doi.org/10.1016/j.eswa.2025.127712

Kumar, G. A., Mohiddin, M. K., Mishra, S. K., Verma, A., Sharma, M., and Naresh, A. (2025). Enhancing Autonomous Vehicle Navigation in Complex Environment with Semantic Proto-Reinforcement Learning. Journal of Field Robotics, 42(5), 2042–2061. https://doi.org/10.1002/rob.22506

Kumar, S., Hooi, B., Makhija, D., and Faloutsos, C. (2022). Multi-Scale Graph Neural Networks for Financial Anomaly Detection. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3187–3197). ACM. https://doi.org/10.1145/3534678.3539095

Li, X., Shen, Y., and Ni, J. (2021). Event-Driven Stock Prediction with a Multi-Graph Convolutional Network. Neural Networks, 139, 221–232. https://doi.org/10.1016/j.neunet.2021.03.033

Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018) (pp. 1–15). https://doi.org/10.48550/arXiv.1707.01926

Lopez-Lira, A., and Tang, Y. (2023). Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models. SSRN Electronic Journal, 12(4), 1–45. https://doi.org/10.2139/ssrn.4412788

Niehues, M., and Weber, K. (2023). Explainable AI in Finance: A Survey. IEEE Access, 11, 10245–10260. https://doi.org/10.1109/ACCESS.2023.3241254

Sawhney, S., Agarwal, S., and Shah, R. (2020). Deep Attentive Learning for Stock Prediction Using Multi-Source Data. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 1210–1217. https://doi.org/10.1609/aaai.v34i01.5474

Sharda, R., and Delen, S. (2021). Predicting Stock Market Trends Using Network Analysis and Deep Learning. Decision Support Systems, 145, 113524. https://doi.org/10.1016/j.dss.2021.113524

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018). Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018) (pp. 1–12). https://doi.org/10.48550/arXiv.1710.10903

Wang, G., Zhang, Y., and Sun, L. (2024). Relational Temporal Graph Neural Networks for Stock Market Prediction. Information Sciences, 589, 112–128. https://doi.org/10.1016/j.ins.2021.12.084

Wang, Y., Yang, H., and Chen, S. (2024). Sentiment Contagion in Financial Networks: A Graph Neural Network Approach. Expert Systems with Applications, 182(1), 115214–115228. https://doi.org/10.1016/j.eswa.2023.115214

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2021). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386

Yan, B., Liu, Y., and Zhang, J. (2022). Dynamic Graph Convolutional Networks for Stock Market Trend Prediction. Pattern Recognition Letters, 156, 120–127. https://doi.org/10.1016/j.patrec.2022.02.012

Yang, H., Chen, S., and Wang, Y. (2022). Multi-Task Graph Neural Networks for Financial Sentiment Analysis and Stock Prediction. Knowledge-Based Systems, 235, 107653–107668. https://doi.org/10.1016/j.knosys.2021.107653

Yang, L., Zhang, Y., and Wang, X. (2022). Multi-Modal Graph Neural Networks for Financial Risk Forecasting. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2145–2158. https://doi.org/10.1109/TKDE.2020.3012341

Zhang, C., Wang, Y., and Zhang, J. (2023). Heterogeneous Graph Neural Networks for Supply Chain Risk Assessment. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2100–2112. https://doi.org/10.1109/TSMC.2022.3196506

Zhang, X., Fang, Y., and Wu, S. (2022). Integrating Knowledge Graphs with Deep Learning for Stock Price Prediction. IEEE Transactions on Industrial Informatics, 18(2), 1015–1025. https://doi.org/10.1109/TII.2021.3073256

Zhao, L., Wang, W., and Liu, H. (2022). Deep Learning in Financial Markets: A Review of Relational Modeling Techniques. IEEE Computational Intelligence Magazine, 17(1), 30–45. https://doi.org/10.1109/MCI.2021.3129957

Zhao, Z., Akibi, R., and Tanimura, R. (2019). T-GCN: A Temporal Graph Convolutional Network for Traffic Forecasting. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4461–4471. https://doi.org/10.1109/TITS.2019.2935152

Zhou, J., Cui, G., and Hu, S. (2020). Graph neural networks: A Review of Methods and Applications. AI Open, 1, 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001