Identifying recessions using machine learning

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Our Approach

We believe an interdisciplinary and scientific approach to asset management leads to breakthroughs in portfolio construction.

Traditional optimization techniques using mean-variance analysis may result in unstable weights and poor out-of-sample performance. Risk-based methods such as risk parity or equal risk contribution provide more stable portfolios, but they do not consider expected returns.

Our approach combines advances in computing power, the widespread availability of data, and recent academic and internal research to re-incorporate expected return estimates in portfolio optimization.  We seek to provide investors with better performance compared to mean-variance or risk-based strategies.


Advancing the boundaries of investment research requires us to challenge conventional wisdom.  The complementary perspectives of our diverse team members come together to unlock large, complex datasets and generate proprietary investment signals.

We combine insights from cutting-edge statistical learning methods and decades of market experience.


Our investment allocation process is driven by careful empirical and theoretical analysis, allowing us to engineer unique products and solutions with potentially attractive risk/reward characteristics.

This scientific approach to dynamic asset allocation leverages our sophisticated computer algorithms and aims to make the best of macroeconomic forecasting and factor investing.