Traditional stock market models often treat price movements—like a sudden 5% drop—as isolated events, ignoring the intricate web of relationships that define a company's ecosystem. But what if a firm's position within the market's underlying structure matters more than the price change itself? That hypothesis drove the creation of a comprehensive knowledge graph mapping the U.S. public markets, revealing unexpected insights about predictive power and the limits of social connections in financial modeling.

Engineering the Market Graph

The project constructs a graph with approximately 207,000 edges categorized into four distinct relationship layers:
- Operational: Supply-chain links (e.g., SUPPLIES_TO, PRODUCES)
- Flow: ETF and institutional ownership networks
- Social: Board interlocks (SHARES_DIRECTOR_WITH)
- Environmental: Geographic proximity and competitive overlaps

For each layer, centrality scores were computed using PageRank-style algorithms, incorporating inverse-degree weighting to prevent distortion from high-degree nodes like dominant ETFs. These structural features—capturing how "central" a company is within its network—were then combined with basic price and volume data. The ensemble was fed into an XGBoost model to rank stocks likely to rebound after sharp declines, moving beyond simplistic price-based heuristics.

Surprising Validation Results

When tested out-of-sample on 2024–2025 data using Alphalens to avoid look-ahead bias, the graph-driven model delivered a key revelation: operational and flow relationships contributed most to predictive lift, roughly doubling ranking quality compared to price-only baselines. Meanwhile, social connections (board interlocks) added minimal value—a counterintuitive outcome given assumptions about human influence in corporate behavior.

This underscores that physical and financial interdependencies, like supply chains and ownership ties, are far stronger signals of resilience than shared leadership, challenging conventional wisdom in network analysis.

The implications extend beyond finance. For developers and data engineers, this highlights how graph features can dramatically enhance machine learning in domains like risk assessment or recommendation systems. However, it also raises questions about graph design: How should heterogeneous edge types be balanced? What normalization techniques scale best? As the creator moves from a research notebook to a production dashboard, they emphasize the need for robust graph schemas that handle real-world complexity without overcomplication.

A Call for Collective Wisdom

This work isn't just about predicting stocks—it's a case study in scaling graph-based AI. The creator invites feedback from practitioners experienced with large graphs: Have social edges proven predictive in your domains? What normalization tricks prevent performance pitfalls at scale? And how do you manage the integration of diverse edge types without introducing noise? By fostering dialogue, the project aims to refine approaches that make graph analytics more accessible and impactful, turning abstract connections into actionable intelligence.

Source: Based on a discussion from Hacker News.