LLM Latent Edge Measurement: Point-in-Time Economic Graphs for Quantitative Investing from Corporate Disclosures

Abstract

Standard industry classification systems such as GICS assign each firm to a single sector, but the economic relationships through which shocks propagate, such as supplier agreements, customer concentration, intellectual property licensing, cloud service dependencies, and power purchase contracts frequently cross sector boundaries and are often disclosed only in unstructured text. We formulate the construction of a firm-level adjacency matrix as a measurement problem and propose an LLM based pipeline that extracts a weighted, directed, point in time corporate network from public disclosures. Applied to the most recent 10 K and 10 K filings of 42 Nasdaq 100 constituents, the proposed pipeline produces a network containing 149 directed edges. An adversarial audit confirms 88% of sampled edges with weights of at least 0.1, increasing to 100% when economically plausible but weakly documented relationships are included. Refuted edges are concentrated entirely in the lowest-weight portion of the network. The resulting network is consistent with GICS where sector classifications are informative, exhibiting a 1.9-fold increase in within-sector connectivity, while also recovering economically meaningful cross-sector relationships that standard classifications cannot represent. Examples include nuclear power-purchase agreements connecting utilities with hyper scale technology firms and GPU-cloud dependencies within the emerging AI infrastructure ecosystem. Ablation studies further demonstrate that multi-agent fusion, inverse-document-frequency filtering, and relative thresholding each make measurable contributions to network quality.