Temporal Knowledge Graphs for Investment Edge How are investors using temporal knowledge graphs (TKGs) to generate alpha?

Temporal Knowledge Graphs for Investment Edge How are investors using temporal knowledge graphs (TKGs) to generate alpha? Cover: Edge thesis — What specific informational or analytical advantages do TKGs provide over traditional approaches (factor models, NLP sentiment, static KGs)? Where does the edge come from: faster signal extraction, novel relationships, regime detection, event chains? Technical requirements — Graph schema design (entities, edges, time validity), storage choices (Neo4j, TigerGraph, custom), temporal reasoning patterns (bitemporal, event-sourced), embedding approaches (TGN, TGAT, TComplEx), and latency constraints for different strategies. Signal requirements — What makes a TKG-derived signal tradeable? Decay characteristics, capacity, orthogonality to known factors, robustness to graph noise. Data sources & ingestion — Filings, news, transcripts, supply chain data, patents, hiring data, alt-data. How to extract entities/relationships with temporal validity, resolve identities across sources, and handle revisions/corrections. From graph to trade — Pipelines for converting graph queries or embeddings into ranked signals, sizing, and execution. Backtesting pitfalls specific to temporal graphs (lookahead bias in entity resolution, survivorship in the graph itself). Concrete examples — Funds or research groups doing this in production, with citations where possible. Be specific and technical. Skip generic AI-in-finance commentary.