Investor Overview

A validated methodology.
A market that does not have it.

The global financial data and analytics market exceeds $40 billion annually, dominated by Bloomberg, FactSet, Refinitiv, and S&P Global. Every institution subscribes to the same platforms and produces research that converges on identical conclusions. ContextQuant operates in the gap between what is publicly filed and what is actually priced.

The Opportunity
Public data. Private insight.
SEC filings contain thousands of pages of management-authored text per company per year. This text changes quarter to quarter in ways that predict stock performance. Virtually nobody exploits this systematically at scale, and certainly nobody measures it as competitive deltas against direct peers.
$40B+
Total Addressable Market
Global financial data and analytics. Dominated by incumbents who do not offer peer-relative textual intelligence from corporate filings.
~4,000
US-Listed Companies
Current coverage: 185 (Dow 30 + competitors). Immediate scaling target via EDGAR. Architecture built for this expansion.
0
Direct Competitors
No existing product combines peer-relative filing analysis, regime-aware signal weighting, and out-of-sample validation in a single platform.
Proof of Concept
Not a pitch deck with projections. A working system with results.
We did not build a slide presentation. We built a platform, ingested seven years of data, and ran rigorous out-of-sample tests. The results speak for themselves.
-6.9%
Average Quintile Spread
Companies with the most specific risk language underperform the least specific by 6.9% over 6-12 months. Consistent across 4 of 6 test years.
6/6
Perfect Signal Consistency
MD&A sentiment drift produced the correct directional spread in every single test year from 2020 through 2025. Probability by chance: 1.6%.
p < 0.01
Statistical Significance
The 2023 risk specificity result (t = -3.48) exceeds the Harvey-Liu-Zhu threshold for multiple-testing-adjusted significance in finance research.
75%
Combined Hit Rate
Two independent signals combined outperform either alone. 75% directional accuracy across all window-year combinations tested.
198K+
Features from 7M+ Words
Over 7 million words of management-authored text processed from 17,207 filings parsed into 73,660 sections. 198,000+ features covering risk specificity, sentiment drift, peer deviations, and macro conditioning, and more.
7/7
Hypotheses Reported Honestly
Three of seven hypotheses were weak or produced nuanced results. We report everything. Intellectual honesty is foundational to the platform's credibility.
Revenue Model
Four paths to monetization
Trading Desks & Quant Teams
Signal Feed
Subscription-based delivery of real-time filing alerts with specificity scores, sentiment drift measures, and peer-relative rankings. Priced per seat or per signal. Delivered via API or flat file. Integrates into existing quantitative workflows without disruption.
Equity Research Teams
Dashboard License
Interactive platform for research teams to explore filing changes, peer comparisons, historical signal performance, and regime indicators across their coverage universe. Annual license per team or per institution.
Wealth Management
White-Label Advisory Intelligence
Client-facing intelligence reports powered by ContextQuant's engine, branded to the institution. Narrative-driven insights for high-net-worth advisory conversations. Per-AUM or per-report pricing.
Institutional Clients
Research & Consulting
Custom hypothesis testing and signal development for clients with proprietary datasets or specific coverage universes. Project-based or retainer pricing for bespoke analysis.
Competitive Moat
Why this is hard to replicate
Integration, Not Technology
Any institution can hire NLP engineers. The moat is the integration: competitive peer mapping, cross-filing textual analysis with TF-IDF specificity scoring, compensation parsing, transcript tone analysis, political spending patterns, and macro regime conditioning, unified in a single system measuring peer-relative deltas across a seven-year validated panel.
First-Mover Data Advantage
The platform accumulates signal performance data with every filing cycle. Each quarter adds more evidence, refines signal calibration, and expands the training history. A competitor starting today begins with zero validated track record. By the time they replicate the methodology, ContextQuant has years of live signal generation as a head start.
Academic Credibility
The methodology is grounded in published research from top finance and accounting journals (JFE, TAR, RAS). The findings extend established work by Campbell et al., Jiang et al., Huang et al., and Kravet & Muslu. This is not a black box. It is transparent, reproducible, and positioned for peer-reviewed publication.
Jurisdiction-Agnostic Architecture
The peer-relative framework works wherever companies file public disclosures. US (EDGAR), Canada (SEDAR+), UK, Europe, Asia-Pacific. What changes across markets is the filing format, not the fundamental insight that disclosure behavior relative to peers predicts returns. Each new market is an expansion, not a rebuild.
Roadmap
Where we are. Where we are going.
Completed
Proof of Concept
Working platform with validated results on 185-company universe.
  • 17,207 filings ingested and parsed
  • 198,192 NLP features from 7M+ words
  • 7 hypotheses tested, 2 strong signals validated
  • 6 years out-of-sample evidence
  • Regime conditioning confirmed
Future
Global Expansion
European and Asia-Pacific markets. Advanced NLP. Institutional partnerships.
  • Companies House, AMF, BaFin (Europe)
  • EDINET, HKExnews, SGXNet (Asia-Pacific)
  • Transformer-based sentiment (FinBERT, GPT-4)
  • Patent and insider transaction signals
  • White-label enterprise deployments
Interested in the opportunity?
We are looking for partners who share our conviction that the next edge in financial markets comes not from better models on the same data, but from reading the data that everyone else has chosen to ignore.
info@contextquant.com