Grizzly Bulls

Lee Bailey

Founder and primary researcher at Grizzly Bulls.

I build Grizzly Bulls around a simple research standard: important financial claims should be traceable to evidence, the arithmetic should be inspectable, and uncertainty should stay visible when the source record is incomplete.

My work sits at the overlap of software engineering and markets. I hold both a bachelor's and master's degree in computer science from Wake Forest University, have spent more than a decade building software, and have traded for more than 15 years.

I founded Grizzly Bulls after applying that engineering background to systematic trading models in 2020. The work has since expanded into original quantitative research, public-company analysis, SEC ownership and governance evidence, billionaire wealth research, and financial education.

Published research and data

The strongest case for expertise is the work itself. This portfolio reflects the current body of published Grizzly Bulls research attributed to me, including each study’s publication date, data date, and reusable CSV and JSON outputs.

30Published studies
30Public data packages
5Research areas

Ownership & Holdings

What reviewed filings can establish about public-equity ownership, how the evidence reaches the broader billionaire universe, and where ownership form or source coverage changes the answer.

Voting Power & Governance

Companywide voting power, how that power changes through time, the legal mechanics behind enhanced-vote structures, and the difference between voting authority and share-count ownership.

SEC Evidence & Methodology

Research about what SEC source families can support, what they cannot support, and why filing presence alone does not make a broader ownership or transaction claim ready to publish.

Valuation & Market Data

Research about point-in-time valuation inputs, market-data timing, financial-statement normalization, private-company unit economics, and the source and capital-structure boundaries required before a valuation output is complete.

Systematic Markets

Measured market and macro studies that test concentration, forecasting signals, and market structure outside a clean textbook story.

Browse the research hubResearch source kit

Research and methodology

  • Prefer primary evidence when it is available, including SEC filings and issuer disclosures.
  • Separate what a source directly reports from what a model, estimate, or calculation infers.
  • Keep observation dates, publication dates, and later updates distinct.
  • Show material limitations instead of converting missing evidence into a stronger claim.
  • Make important calculations and aggregate research outputs reproducible when the underlying rights allow it.

Research standards

Grizzly Bulls publishes a separate standards page covering source quality, research dates, versioning, citations, corrections, and data reuse.

Read the research standards

Engineering and markets

Before Grizzly Bulls, I co-founded Dragonboat, a product and portfolio management software company. My professional background is in software engineering, with computer science training that included machine learning.

That background shapes the way I approach investing research. I care about definitions, reproducibility, failure modes, data lineage, and whether a result survives a simple baseline. In markets, a polished story is much less useful than a model or claim whose assumptions can be inspected.

I also wrote Practical Algorithmic Trading with Node.js, a hands-on guide to building the data, backtesting, strategy, execution, and risk-management pieces of an algorithmic trading system.

Accountability and contact

Financial research can be wrong, incomplete, or overtaken by new evidence. Grizzly Bulls keeps a public path for corrections, methodology questions, data-reuse requests, and press inquiries so important claims can be challenged and updated when necessary.

Research, corrections, and press contactLinkedIn