A Quantitative approach
for serious individual investors.
Educational research notes making quantitative investing accessible to individual investors through institutional-grade methods.
Founding principles
Our research standards.
Learn from a quantitative approach
Learn how quantitative investing works, explained simply and clearly, from a specialist with +7 years of experience in the field.
Transparency and rigor
Assumptions are stated before conclusions, methods are described well enough to reproduce, and limitations are named rather than buried.
Sourced to the original
Statutory figures, statistics, and study results are cited to the primary source — the IRS, the agency, or the paper itself — never to a summary of it.
In preparation
The first paper.
Can You Actually Protect a 401(k) From a Stock Market Crash? A Quantitative Backtest, 1970–2025.
A backtest of composite leading macroeconomic indicators across seven US recessions. We measure how much drawdown a disciplined macro-timed reduction in equity exposure would have avoided, the return cost it would have imposed, and where the signal would have misfired.
How the Lab is structured
A three-step reading framework.
01
Read
Educational articles that explain each concept from first principles.
02
Apply
Interactive tools to test the concepts on your own data.
03
Progress
Move from foundations to advanced topics at your own pace.
The researcher behind the Lab
John Bergerat
Founder · MSc Quantitative Finance
- Quantitative Asset Management & Risk Management
- Dozens of algorithmic and risk models built
- Custom mandates for 5 institutional funds in the US, UK, and Canada
- Founder, Astralys LLC & Quantalytics
“The biggest threat in quantitative research is not the market. It is backtest overfitting: strategies that look exceptional in history and fail in reality.”
John's academic thesis applied the Probability of Backtest Overfittingframework of Bailey et al. (2015) to systematic strategies on the S&P 500 and Nasdaq 100 from 1988 to 2020. The conclusion: US equity markets do exhibit statistical inefficiencies, but most of those edges are eroded by transaction costs, slippage, and real-world trading frictions. That insight has shaped every model he has built since.
His professional work focuses on quantitative frameworks that genuinely improve risk-adjusted returns: leverage management systems, position-sizing rules, drawdown controls, and adaptive models that respond to macroeconomic events and news flow. Several of these models are deployed in production at institutional funds, where they continue to operate for direct clients.
Quant Investing Lab brings that same practice into the public domain for serious individual investors. The goal is to teach the frameworks used at research desks like AQR Capital, Verdad Advisers, and Newfound Research, in plain language, so readers can evaluate any strategy (including ours) on the same rigorous terms.
Contact
Get in touch.
For research collaborations, editorial inquiries, media requests, or substantive questions about published work, reach out directly. I read every message and respond within a few business days.
Direct channels
What to expect
Email is the fastest route and reaches me directly. Questions about a specific published figure or methodology are welcome — quote the paper and the section and I can point you to the underlying data.
Quant Investing Lab is an educational research publication. We do not provide personalized investment advice and cannot respond to requests for individual recommendations. See the disclaimer for details.