ChainLens: A Cross-Market Supply Chain Stock Price Prediction System Based on Graph Neural Networks

ChainLens is an innovative open-source project that uses Graph Neural Networks (GNN) to analyze supply chain relationships and predict stock performance in the Taiwanese and U.S. markets. The project models complex supply chain networks as graph structures and captures inter-enterprise dependencies and influence transmission mechanisms through deep learning, providing a new perspective for quantitative investment. The project is maintained by LLUVV and hosted on GitHub; the original link is https://github.com/LLUVV/chainlens.

In the modern economy, enterprises are interconnected through complex supply chain networks, and a company's performance is closely related to its upstream and downstream partners. Traditional stock analysis methods mostly focus on individual companies' financial indicators and ignore network effects. For example, Apple's strong financial report not only reflects its own status but may also benefit supply chain partners such as chip manufacturers and assembly plants. ChainLens aims to capture this network transmission effect.

GNN Basics

ChainLens may adopt GNN architectures such as GCN (aggregating neighbor features), GAT (attention mechanism), GraphSAGE (sampling and aggregation), or their combinations to directly learn representations of nodes (enterprises) and edges (supply relationships).

Cross-Market Modeling Challenges

Integrating the Taiwanese and U.S. markets faces issues like time zone differences, currency risks, regulatory differences, and data heterogeneity, which require special alignment mechanisms to handle.

1. Event-driven trading: When major industry events (natural disasters, policy changes) occur, quickly identify affected enterprises and their supply chain partners to generate trading signals.
2. Earnings season prediction: Before earnings reports are released, predict the target company's performance using pre-disclosure information from upstream and downstream partners.
3. Risk transmission analysis: Identify key node enterprises in the supply chain and evaluate the potential impact of their financial distress on the network.

Data Layer

• Supply chain data: Extract enterprise relationships from public disclosures, industry reports, and news
• Market data: Time-series data such as stock prices, trading volumes, and financial indicators
• Graph construction: Map enterprises to nodes and supply relationships to edges

Model Layer

• Node encoder: Encode multi-dimensional features of enterprises into vectors
• Graph convolution layer: Aggregate neighbor information through message passing
• Temporal modeling: May use RNN or Transformer to capture temporal dynamics
• Prediction head: Output stock return predictions or classification results

Training Strategy

• Supervised learning: Use historical stock price data as labels
• Graph sampling: Optimize the efficiency of large-scale graph processing
• Regularization: Prevent overfitting

Innovative Value

Compared to traditional factor models, the GNN approach can:

1. Capture non-linear relationships between enterprises
2. Handle heterogeneous information of structure (graph topology) and attributes (enterprise features)
3. Achieve interpretable analysis through attention weights

Limitations

• Data quality: Integrity and accuracy of supply chain data
• Market efficiency: Alpha signals are easily eliminated by arbitrage
• Model stability: May fail when market regimes change
• Backtesting bias: Past performance does not guarantee future returns

Future Directions

1. Multimodal fusion: Integrate unstructured data such as news sentiment and social media
2. Dynamic graph modeling: Consider the time-varying characteristics of supply chain relationships
3. Reinforcement learning: Combine prediction models with trading execution strategies
4. Enhanced interpretability: Provide more intuitive decision-making basis

Summary

ChainLens represents a fintech trend, applying GNN to complex relational data and shifting from isolated enterprise analysis to networked ecosystem analysis. Its cross-market feature provides a new tool for understanding the interaction between global supply chains and capital markets, which is worthy of attention from quantitative researchers, data scientists, and financial practitioners.