A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings

# A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings
Source: [https://arxiv.org/abs/2609.11620](https://arxiv.org/abs/2609.11620)
[View PDF](https://arxiv.org/pdf/2609.11620)

> Abstract:High\-dimensional dense text embeddings and large language models face real obstacles in financial\-disclosure analysis: context\-window limits, hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across independently trained models\. We present a training\-free, alignment\-free framework for corporate intelligence built on deterministic sparse seed vectors\. Hashing word strings into a fixed high\-dimensional basis places all documents and all temporal epochs in a common coordinate system by construction, removing any need for training or alignment\. Accumulating these seed vectors across sentence contexts yields corpus\-specific semantic signatures that compose linearly, supporting sub\-second document comparison, issuer fingerprinting, tracking of how an issuer's vocabulary shifts between filings, and thematic sentence extraction, all on ordinary CPU hardware\. Demonstrating the approach on a multi\-year corpus of SEC filings \(10\-K, 10\-Q, 8\-K\), we show how material corporate events, among them Boeing's 737 MAX crisis, Intel's supply\-chain disruptions, and Bunge's acquisition of Viterra, emerge as distinct, interpretable semantic profiles, each traceable to the exact source sentences that produced it, with no domain\-specific training and no LLM inference\.

## Submission history

From: Jean\-François Delpech \[[view email](https://arxiv.org/show-email/8ba0323d/2609.11620)\] **\[v1\]**Thu, 10 Sep 2026 14:32:35 UTC \(830 KB\)