Vector retrieval made evidence-grounded financial question answering practical, but similarity search still operates on isolated chunks. Questions that depend on ownership chains, supplier networks, contagion paths, or the timing of relationship changes require explicit structure rather than fuzzy matches across text. Knowledge graphs represent entities as nodes, relationships as typed edges, and provenance as properties — giving analytical queries something to traverse, audit, and reuse.

This chapter covers the construction, query, feature-engineering, and evaluation discipline that make financial knowledge graphs useful rather than novel. It treats graph building as a governance problem (identity, schema, provenance), introduces Graph RAG as deterministic relational retrieval, turns graph structure into leakage-aware ML features, connects the statistical-financial-networks literature to the explicit-KG workflow, and closes with a three-timestamp model for temporal integrity and the engineering choices that keep systems auditable.

Learning Objectives

Distinguish financial questions that genuinely require graph structure from those better served by tabular databases or vector retrieval
Design a compact, typed, and auditable financial knowledge graph with stable entity identity, finite relationship vocabularies, and edge-level provenance
Build and validate LLM-assisted extraction pipelines that convert disclosures into replayable graph objects while controlling schema, duplication, and temporal consistency
Explain how Graph RAG differs from vector retrieval and implement safe relational query workflows using constrained text-to-query generation and deterministic database execution
Transform graph structure into leakage-aware machine learning features — topology, crowding, concentration, and cross-graph interaction terms
Evaluate explicit knowledge graphs, statistical financial networks, and learned graph representations pragmatically for forecasting, portfolio construction, and risk analysis
Apply a three-timestamp framework and disclosure-time cutoff rules to prevent temporal leakage in graph queries, feature generation, and backtests
Make sound engineering choices about graph databases, ontology scope, query safety, and schema evolution for production-oriented financial systems

Chapter Sections

#	Title	Core Idea
23.1	When Relational Structure Unlocks Financial Insight	Graphs earn their overhead when the question is multi-hop, structurally crowded, or temporally evolving
23.2	Constructing Financial Knowledge Graphs	LLM extraction plus identity, schema, and provenance contracts that make the graph replayable and auditable
23.3	Graph RAG: Deterministic Relational Reasoning	A five-stage architecture that delegates relational logic to the database and language generation to the LLM
23.4	From Graphs to Machine Learning Features	Centrality, crowding, co-ownership, and cross-graph interactions become tabular features for downstream models
23.5	Financial Networks: From Correlations to Portfolios	How classic MST and correlation-network methods complement explicit KGs, and where GNNs fit in practice
23.6	Temporal Integrity and Leakage-Safe Evaluation	The three-timestamp model (event, disclosure, extraction) and a protocol for cutoff-safe features
23.7	Building a KG-Ready Pipeline: Engineering Decisions	Engine selection, ontology scope, text-to-Cypher safety, and schema versioning for production

Notebooks

Graph Construction

Notebook	What It Teaches
`01_sp100_sec_download`	Loads and inspects 10-K and 8-K filings for S&P 100 companies from SEC EDGAR, verifies coverage and text quality before graph extraction.
`02_supply_chain_kg_construction`	Extracts supplier, customer, and competitor relationships from 10-K filings using Qwen2.5-7B, resolves entities, loads triples to Neo4j, and visualizes the resulting supply-chain graph.
`05_institutional_holdings_kg`	Builds an institutional-holdings property graph from EDGAR 13F filings, demonstrates shared-holding and crowding queries, and computes Jaccard co-ownership similarity.
`08_8k_event_extraction`	Extracts structured event quadruples from SEC 8-K filings with the FinReflectKG critic-corrector reflection loop, then loads the event graph to Neo4j.

Retrieval and Evaluation

Notebook	What It Teaches
`03_graph_rag_qa`	Implements controlled text-to-Cypher question answering over the 13F holdings graph, enforcing read-only schema validation, dated queries, and row limits on every generated query.
`04_rag_comparison_benchmark`	Benchmarks graph retrieval against embedding-based retrieval on real 13F holdings, measuring support recall and retrieval-token cost across direct-lookup and multi-entity question types.

Graph-Derived Features and Networks

Notebook	What It Teaches
`09_knowledge_graph_features`	Combines supply-chain topology (PageRank, betweenness, HHI) with 13F crowding and cross-graph interactions to produce a tabular feature matrix suitable for gradient-boosted models.
`10_network_portfolio_construction`	Builds correlation networks and minimum spanning trees from US equities, ranks assets by centrality, constructs network-diversified portfolios, and runs a contagion simulation.
`06_gnn_feature_engineering`	Trains a simplified GAT on a stock correlation network to generate relational embeddings, then compares a tabular-only ridge regression to a hybrid model that concatenates the learned embeddings.

Temporal Integrity

Notebook	What It Teaches
`07_dynamic_kg_temporal`	Builds leakage-safe temporal snapshots from the 8-K event graph by separating event, disclosure, and extraction time, then measures relationship churn and centrality drift while verifying no post-cutoff leakage.

Running the Notebooks

# From the repository root
uv run python 23_knowledge_graphs/<notebook>.py

# Test mode (reduced data via Papermill)
uv run pytest tests/test_notebooks.py -v -k "23_knowledge_graphs"

# Headless (no display)
MPLBACKEND=Agg PLOTLY_RENDERER=json uv run python 23_knowledge_graphs/<notebook>.py

A running Neo4j instance is required for 02_supply_chain_kg_construction, 05_institutional_holdings_kg, 08_8k_event_extraction, 03_graph_rag_qa, and 07_dynamic_kg_temporal. Local LLM extraction for the supply-chain and 8-K pipelines uses Qwen2.5-7B via a vLLM-compatible endpoint.

Dependencies

Upstream

Chapter 4 (Fundamental and Alternative Data) — 13F institutional-holdings loader
Chapter 10 (Text Feature Engineering) — NER and filing parsing patterns
Chapter 22 (RAG for Financial Research) — retrieval-augmented generation foundation

Downstream

Chapter 12 (Advanced Models for Tabular Data) — consumes cross-graph features
Chapter 14 (Latent Factor Models) — consumes co-ownership and crowding features
Chapter 24 (Autonomous Agents) — uses structured retrieval, memory, and evidence tracking

Chapter-specific libraries

neo4j — graph database driver (Cypher client)
networkx — graph analytics and MST construction
torch_geometric — Graph Attention Network implementation
edgartools — SEC EDGAR filings ingestion
sentence-transformers — embedding baseline for the RAG comparison

References

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Chanyeol Choi et al. (2025). FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation.
Darren Edge et al. (2025). From Local to Global: A Graph RAG Approach to Query-Focused Summarization.
Sarah Elhammadi et al. (2020). A High Precision Pipeline for Financial Knowledge Graph Construction. International Committee on Computational Linguistics.
Robin Greenwood and David Thesmar (2011). Stock price fragility. Journal of Financial Economics.
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Natthawut Kertkeidkachorn et al. (2023). FinKG: A Core Financial Knowledge Graph for Financial Analysis.
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Gautier Marti et al. (2021). A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets. Springer International Publishing.
Rui Miao et al. (2019). A Dynamic Financial Knowledge Graph Based on Reinforcement Learning and Transfer Learning.
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Samreen Zehra et al. (2021). Financial Knowledge Graph Based Financial Report Query System. IEEE Access.