Professor Michael Harrington
Professor Michael Harrington is the founder and guiding figure of the VMA Community, shaped by a Princeton physics background and decades of institutional market experience. Known for a calm, risk-first mindset, he advocates model-driven decision systems and structured investor learning focused on preparation, verification, and long-horizon discipline.
Opinion
Harrington’s core view is that markets reward preparation more reliably than prediction. He favors decision frameworks that remain stable under stress: define assumptions, measure uncertainty, and let tested rules—not emotion—determine action when volatility rises.
In the VMA Community, he is often associated with a calm, disciplined style that prioritizes risk containment, process clarity, and post-trade learning so that investors can improve their judgment over time.
Method
- 1 Model first, narrative second: start with measurable hypotheses, define inputs and limits, and avoid retroactive storytelling.
- 2 Risk before return: set drawdown controls, position sizing rules, and monitoring triggers before deployment.
- 3 Review and iterate: run disciplined post-analysis, track decision quality, and refine rules based on evidence.
Profile
Princeton-trained in physics, Harrington built a decades-long Wall Street career in systematic investing, spanning portfolio leadership and risk oversight at institutional firms.
“Real investing is not about predicting the future—it is about preparing for it.”
Career
Early Quant Systems Architect
Joined the first wave of Wall Street practitioners building systematic trading workflows, emphasizing monitoring, testing discipline, and repeatable execution rules.
Investment Director at Leading Funds
Held senior investment leadership positions across top-tier funds, aligning strategy design with operational controls and long-horizon portfolio stewardship.
Crisis-Tested Risk Leadership
Navigated multiple crisis environments with a focus on drawdown control, liquidity awareness, and decision calm—prioritizing survival and consistency over short-term noise.
Founder & Guiding Voice of the VMA Community
Built the VMA Community around structured, data-driven learning—helping investors develop repeatable decision habits, clearer assumptions, and stronger risk awareness.
Research & Opinion
Quantitative Modeling for Repeatability
Research focus on turning uncertainty into measurable variables—building models that can be tested, monitored, and refined instead of relying on intuition-driven decisions.
Macro Regimes & Risk Transmission
Studies how market regimes shift and how risk travels through liquidity, positioning, and correlation changes—supporting strategies that stay robust when conditions flip.
Decision Discipline & Investor Learning
Emphasizes teaching investors to separate signal from noise, document assumptions, and evaluate performance through process quality—not only outcomes.