About Altus Labs
Open Research at the Intersection of Finance and Technology
We publish open-source quantitative research exploring how markets compound wealth over time — bridging the gap between how portfolios are constructed and how returns actually behave under non-linear dynamics.
The Structural Void
Why Current Approaches Fall Short
Modern Portfolio Theory, Risk Parity, and traditional 60/40 allocations share a common characteristic: they tend to be pro-cyclical. These frameworks can increase leverage at exactly the point where systemic risk is elevated.
As options adoption and algorithmic passive investing increase, markets become prone to abrupt liquidity vacuums. Traditional allocations often lack explicit dynamic tail-risk hedging— they optimize for the middle of the distribution while the tails are where wealth is actually made and destroyed.
This creates a volatility feedback loop that can amplify drawdowns precisely when capital preservation matters most.
The Ergodicity Problem
Traditional finance optimizes for ensemble averages—what happens across many investors. But you experience a single sequence of returns over time. These are fundamentally different optimization targets.
Pro-Cyclical Construction
Risk parity and volatility targeting increase leverage when volatility is low and decrease it when volatility spikes—which may not align with what long-term compounding requires.
Transparency Gap
Many institutional-grade strategies come with high minimums, lock-up periods, and limited visibility into methodology. There's room for more transparent approaches.
Our Approach
Research Philosophy
Our research is grounded in three principles: optimise for geometric compounding rather than ensemble averages, measure endogenous risk before it manifests in price, and publish methodology transparently so results are fully reproducible.
Geometric Compounding
Our research focuses on what matters for a single investor over time: the geometric mean growth rate. This means understanding why survival and drawdown avoidance dominate expected return maximisation in the long run.
Endogenous Risk
We study how systemic stress builds endogenously — through position crowding, liquidity withdrawal, and reflexive feedback loops — and how convex portfolio construction can exploit the non-ergodic nature of these dynamics.
Open Methodology
All published research includes the methodology, assumptions, and limitations in sufficient detail for independent reproduction. No black boxes.
Vision
Making Institutional-Grade Research Accessible
Quantitative research on convexity, tail risk, and non-ergodic compounding is typically locked behind institutional paywalls or buried in academic papers that don't translate to practitioner workflows.
We publish at the intersection — rigorous enough to be useful, accessible enough to reach practitioners, portfolio managers, and independent researchers who want to incorporate these ideas into their own thinking.
Our research covers portfolio construction through a convexity lens, systematic stress detection through a convexity and non-ergodicity lens, and the backtesting and validation methodology that underpins it all.
Research Areas
Non-Ergodic Portfolio Theory
Geometric compounding, volatility drag, and survival-first construction
Market Microstructure & Convexity
Options pricing, volatility surfaces, and asymmetric payoff design
Endogenous Risk & Convexity
Non-ergodic frameworks applied to systemic stress detection and tail-risk positioning
Backtesting & Statistical Validation
Methodology for distinguishing genuine edge from noise