Contextual Intelligence for Equity Research

The filing satisfies a legal obligation.
The language reveals the truth.

An AI-driven contextual intelligence platform that reads corporate disclosures as a connected system. Currently built on the Dow 30 and their direct competitors (17,000+ SEC filings, 7 million+ words analyzed and counting), with earnings call transcripts, macro data, executive compensation, and political spending. Expanding to material coverage across major North American and global markets.

17,207SEC Filings Analyzed
185Companies Tracked
7 Yearsof Market Data
7M+Words Analyzed by NLP
198K+Features Generated
The Platform

Read every filing as a connected system

ContextQuant ingests the full textual and financial output of publicly listed companies and measures every disclosure against what direct competitors are saying. The result is a peer-relative intelligence layer that surfaces what changed, where it diverges from the peer group, and what it historically predicts.

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NLP Filing Parser
Every 10-K, 10-Q, 8-K, and DEF 14A decomposed into individually labeled sections. Over 7 million words processed through the NLP pipeline so far. Risk factors extracted as discrete items. Sentiment scored using Loughran-McDonald financial dictionaries. TF-IDF specificity analysis separates company-tailored language from boilerplate.
Δ
Competitive Deltas
Every metric measured as a peer-relative deviation. Direct competitor mappings at the business-line level, not SIC codes. When a company's risk language becomes more specific while peers remain generic, that divergence is the signal.
Macro Regime Engine
The system knows which signal matters in which environment. Risk specificity strengthens during high-uncertainty periods. Sentiment drift strengthens in calm markets. Signal weights adjust dynamically to VIX, rate cycles, and policy regimes.
Multi-Source Ingest
SEC filings, earnings call transcripts, proxy statements, executive compensation, political contributions (FEC), 60 macroeconomic time series (FRED), and daily price data. 198,000+ features from a single integrated pipeline.
Out-of-Sample Validation
Every signal tested using a rolling framework: train on data before year t, test on year t. Six independent annual tests from 2020 through 2025. No lookahead. No in-sample optimization. The results are real.
Scalable Architecture
Built for expansion from 185 companies to full market coverage. North American markets first (EDGAR + SEDAR+), then European and Asia-Pacific filing repositories. The methodology is jurisdiction-agnostic. What changes is the filing format, not the insight.

First wave: seven hypotheses tested. Two validated signals. More in the pipeline.

The market is slow to price what companies disclose in their own filings. We measured how slow, and we can predict where it is headed. These are our initial findings from the first round of hypothesis testing. Additional signals covering transcript analysis, patent innovation, and insider transactions are in active development.

Risk Factor Specificity
H1: Specific risk language predicts underperformance
Avg IC-0.121
Avg Spread-6.9%
Consistency4 / 6 years
Best Year2023: t = -3.48
Strong
MD&A Sentiment Drift
H2: Excessive optimism is a contrarian indicator
Avg IC-0.062
Avg Spread-2.04%
Consistency6 / 6 years
Sign Testp = 0.016
Strong · Perfect Consistency
Combined Signal
H3: Independent signals improve when combined
Avg IC-0.051
Hit Rate75%
90-180d IC-0.115
Best Config60/40 risk-wt
Strong · Multi-Factor
Macro Regime Conditioning
H10: Signals are regime-complementary
Risk in HIGH2.7x stronger
Sentiment in LOW2.1x stronger
Composite HIGH6/6 hit rate
Confirmed
8-K Events / Compensation
H5 & H6: Event clustering & pay structure
8-K EventsDirectional only
CompensationN=15 insufficient
StatusNeeds more data
Exploratory
Political Spending
H7: Direction depends on absolute vs. size-relative measurement
Total Spend IC+0.159
Intensity IC3/3 negative
Sample134 tickers / 406K records
Size-Dependent
YearNICSpreadt-stat
2020113-0.163-16.1%-2.39 *
2021112-0.082-1.2%-0.20
2022110-0.110-7.7%-2.07 *
2023108-0.275-18.5%-3.48 **
2024110-0.061-4.8%-0.83
202574-0.034+6.9%+1.09
AVG104-0.121-6.9%-1.32
* significant at 5%    ** significant at 1% (exceeds Harvey-Liu-Zhu t>3.0 threshold)
High Uncertainty Regime
Risk specificity IC0.066
Sentiment IC0.028
Composite 90-180dIC = -0.137 · 6/6
Low Uncertainty Regime
Risk specificity IC0.024
Sentiment IC0.059
Sentiment 0-30dIC = -0.30 · 4/4
Regime: VIX > 20 or Fed Funds Δ > 50bps / 6 months
Who It's For

One platform. Multiple applications.

Institutional

Contextual intelligence for equity research, trading, credit risk, wealth advisory, and risk management teams.

  • Filing anomaly alerts for research desks
  • Independent signal feed for quant strategies
  • Credit early warning from borrower disclosures
  • White-labeled advisory intelligence
Explore Business Applications →
Academic

A peer-relative textual analysis framework grounded in established literature, with novel contributions to disclosure research.

  • Extends Campbell et al. (2014), Jiang et al. (2019)
  • Competitive delta framework (novel)
  • Regime-complementary signal behavior (novel)
  • Collaboration and co-authorship opportunities
Explore Research →
Investor

A validated methodology ready to scale from 185 companies to full North American and global market coverage.

  • Working platform with six years of evidence
  • Signal feed + dashboard + white-label revenue
  • No comparable product in $40B analytics market
  • Architecture scales to 4,000+ companies
Explore the Opportunity →

From 185 companies to global coverage

The methodology works. We proved it on a focused universe. The architecture is built to scale across jurisdictions, languages, and filing formats. The insight that peer-relative disclosure behavior predicts returns is not limited to US markets.

EDGAR (US)
SEDAR+ (Canada)
Companies House (UK)
AMF (France)
BaFin (Germany)
EDINET (Japan)
HKExnews (HK)
SGXNet (SG)
Get in Touch

Let's talk about what this means for you

Whether you are an institution exploring new signal sources, a researcher interested in collaboration, or an investor evaluating the opportunity, we would welcome the conversation.

info@contextquant.com

We typically respond within 24 hours.