Sparse vectors enable fast corporate event detection from SEC filings
A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings
Computation and Language
Summary
Analyzing financial reports with popular AI models can be slow, costly, and sometimes inaccurate. The authors introduce a new way to study corporate documents without the need for training or complex model alignment, using a method that converts words into simple, consistent codes in a big space. This lets them quickly compare documents and spot important events, like crashes or acquisitions, by looking at the exact sentences where these changes happen. Their approach works on regular computers and avoids usual problems in AI analysis.
What this means in practice
- •For financial analysts: Quickly identify and interpret significant corporate events across multiple years of regulatory filings without requiring expensive AI model training.
- •For risk management teams: Track changes in a company's language over time to detect emerging risks or shifts in corporate strategy using a computationally efficient system.
Authors
Jean-François Delpech
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.