We Test Everything Before We Trust It
Good investment ideas are easy to find. Good investment ideas that hold up to rigorous quantitative testing are much rarer. That’s the standard we apply before anything goes into a client portfolio.
Research-first, always
We start with data, using academic research to model asset allocation, study correlations, and analyze investment risk and return.
Academic research provides the theoretical foundation — the “why” behind a given investment characteristic or strategy. But we don’t stop there. As a matter of practice, we run our own internal quantitative research to test new investments before implementing them.
From academic data to investable funds
There’s a significant gap between an academic study and an actual investment product that delivers those returns. A paper might document a return premium over 40 years of data. The real question is whether an investable fund, with real fees, real liquidity constraints, and real implementation costs, can capture a meaningful portion of that premium net of everything.
That’s what our quantitative process is designed to answer. We scour the market to find the funds that best represent the return profiles of our academic data sources. That process includes careful analysis of fees, liquidity, assets under management, and the manager’s ability to execute efficiently and consistently.
Asset allocation modeling
Before building a portfolio, we run scenarios. How has this combination of asset classes behaved during periods of rising inflation? During bear markets? During credit crises? What does the risk and return profile look like across different time horizons and withdrawal rate assumptions?
These scenarios directly shape how we structure client portfolios. Asset allocation for a retiree taking distributions differs greatly from that of an institution with a long time horizon, and no taxes.
Ongoing monitoring and research
Our quantitative work doesn’t stop at implementation. We continue to monitor academic literature, evaluate new investment products as they enter the market, and run ongoing analyses to see that our existing holdings are performing as expected.
When something changes like funds, assets, or academic basis, our quantitative process surfaces it.
Frequently Asked Questions
Does your quantitative approach mean you use algorithms to trade?
No. Our quantitative research informs portfolio construction and investment selection, but our implementation is thoughtful and deliberate rather than algorithmic or high-frequency. We use data and modeling to make better decisions, not to replace judgment with automation.
What academic sources do you rely on?
We draw on peer-reviewed financial research from leading institutions; the same bodies of work that underpin modern portfolio theory, factor investing, and evidence-based asset allocation. This includes researchers like Fama, French, and others whose work has been widely replicated and accepted in the academic community. We’re skeptical of research that hasn’t been independently confirmed or may have industry biases.
How do you evaluate a new investment before using it with clients?
We start with the academic or theoretical basis for the investment — what return driver is it designed to capture, and what does the evidence say about that driver? Then we evaluate the specific fund: its fees, its factor exposures, its liquidity, its tax efficiency, and how it interacts with the rest of a portfolio. Only after that process does it become a candidate for client portfolios.
Disclaimer: Investing in securities involves a risk of loss. Past performance is never a guarantee of future returns.