Build portfolios that survive what you can't predict.
We organize our holdings by true economic exposure, model how correlations shift in crises, and simulate outcomes across regimes before committing capital. These are the same tools we use.
Why We Built Our Own
Most portfolio tools are built on a flawed assumption.
Most portfolio analysis calculates correlations from the past five years of calm markets, runs Monte Carlo with those fixed numbers, and calls it diversified. We weren't satisfied with that—so we built something better.
The Research: Correlation Breakdown
Academic research has documented a troubling pattern: asset correlations spike during market stress—precisely when diversification matters most.
Forbes & Rigobon (2002)
Correlations increase significantly during crises due to volatility amplification
Longin & Solnik (2001)
Stock correlations increase in bear markets; diversification benefits decline
Ang & Chen (2002)
Downside correlations exceed upside correlations for most asset pairs
Campbell et al. (2002)
Stock-bond correlation turns negative in crashes (flight to quality)
What this means: Your "diversified" portfolio may be concentrated in a crisis. Correlations calculated from calm markets will underestimate tail risk.
"Diversification works until you need it to work."
Our Approach
Think in economic exposures, not pie charts.
A "sleeve" is a grouping based on economic exposure—how a set of holdings responds to growth, inflation, and risk sentiment.
The Problem with Asset Classes
- • "Stocks" includes both defensive utilities and speculative tech
- • "Bonds" includes both safe Treasuries and risky high-yield
- • "Alternatives" is a meaningless catch-all
The Sleeve Solution
- • Group by economic drivers
- • US Growth ≠ US Value (different regimes)
- • Treasuries ≠ High Yield (different risks)
- • See your true concentration
Harvest Volatility Through Rebalancing
A well-documented result: when holdings are organized into genuinely uncorrelated sleeves and rebalanced systematically, volatility itself becomes a source of incremental return.
The Rebalancing Bonus
When sleeves diverge, rebalancing forces you to "sell high, buy low" automatically. Over time, this generates incremental returns that a static portfolio would miss.
Why Sleeves Matter
Traditional asset classes often move together. Sleeves based on true economic exposure are more likely to be genuinely uncorrelated—which means more volatility to harvest.
Academic foundation: This concept is rooted in research on the "rebalancing premium" (Bernstein, Booth & Fama) and information theory (Shannon's Demon). The less correlated your sleeves, the greater the harvesting opportunity.
Risk Premium Sleeves
Academic research identifies distinct risk premiums you can harvest independently
Equity Premium
Compensation for bearing market risk
Term Premium
Duration risk in bonds
Credit Premium
Default risk compensation
Volatility Premium
Selling insurance (short vol)
Carry Harvesting
Yield differentials across assets
Illiquidity Premium
Compensation for lock-up
Value Factor
Cheap vs. expensive (Fama-French)
Momentum Factor
Trend continuation (Jegadeesh-Titman)
Economic Sensitivity Sleeves
Group by what macro factor drives returns—not by what the asset "is"
Growth Beta
Moves with GDP (cyclicals, EM)
Inflation Beta
Moves with CPI (commodities, TIPS)
Rate Beta
Sensitive to Fed policy (duration)
Dollar Beta
Currency exposure (int'l assets)
Risk Appetite
VIX inverse (high beta, credit)
Quality Compounding
High ROIC, stable earnings
Crisis Alpha
Profits in dislocations (trend, vol)
Tail Protection
Insurance for left-tail events
Regimes
Markets change. Your analysis should too.
The Four Economic Quadrants
Every market environment is a combination of growth and inflation (Ray Dalio's All Weather framework)
Goldilocks
Growth: Rising | Inflation: Low
Performs: Equities, credit
Deflation
Growth: Falling | Inflation: Falling
Performs: Bonds, cash
Stagflation
Growth: Falling | Inflation: Rising
Performs: Commodities, TIPS
Reflation
Growth: Rising | Inflation: Rising
Performs: Value, real assets
Pre-Built Historical Regimes
2008 Financial Crisis
Sep 2008 - Mar 2009
Equity correlations spike, bonds rally
COVID Crash
Feb 2020 - Mar 2020
Rapid de-risking, everything sells
2022 Inflation Shock
Jan 2022 - Oct 2022
Stocks and bonds fall together
Post-COVID Recovery
Apr 2020 - Dec 2021
Risk-on, high correlation to equities
Simulation
Monte Carlo that respects how markets actually work.
Traditional MC
- • Single set of correlations
- • Constant volatility assumptions
- • Ignores regime transitions
- • Underestimates tail risk
Regime-Aware MC
- • Different correlations per regime
- • Volatility varies by regime
- • Models transitions between states
- • Captures crisis behavior
What the Simulations Reveal
Terminal Wealth
Where you might end up in 10, 20, 30 years
Drawdown Probabilities
How bad could it get? How likely?
Risk Attribution
Which sleeves drive your risk?
Regime Outcomes
How does your portfolio perform in recession vs. inflation?
The Research
This isn't a new idea. It's how institutions have always done it.
Regime-Aware Allocation Outperforms
Ang & Bekaert (2002)
Investors who account for regime changes achieve better risk-adjusted returns than those using static models.
Risk Budgeting Improves Outcomes
Qian (2005)
Allocating by risk contribution—rather than capital—produces more balanced and resilient portfolios.
Tail Risk is Underestimated
Mandelbrot & Taleb
Extreme events occur far more often than normal distributions predict. Regime-aware models better capture these "fat tails."
Correlation Structure Matters
Markowitz (1952) limitations
MPT assumed stable correlations. Decades of research have shown this assumption breaks down—especially during stress.
The Process
How we go from holdings to conviction.
Organize into Sleeves
Import your holdings. Map each to a sleeve based on economic exposure. See your true risk concentration.
→ True exposures revealed.
Define Your Regimes
Use pre-built historical windows or define custom return and correlation assumptions for each regime.
→ Multiple scenarios to test.
Simulate & Decide
Run regime-aware Monte Carlo. See drawdowns, terminal wealth, and risk attribution. Adjust until confident.
→ Data-driven decisions.
Our Standards
The standards we hold our portfolio tools to.
Sleeves-First
How we organize
Regime-Aware
Model what matters
Auditable
Traceable assumptions
Reproducible
Same inputs, same outputs
Rigorous
The math is right
This is how we build portfolios. The tools are yours.
Import your holdings. Organize by economic exposure. Stress-test across regimes. The same process we follow—open to you.