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Millburn

Machine Learning, Now 100%, Continues To Perform Multi-Factor, Multi-Data Quant

Ever since the latest phase in Millburn’s strategy evolution began in 2013, its flagship diversified strategies have delivered a Sharpe ratio near one. These strategies have also significantly outperformed most traditional CTAs, including positive performance in each of the last five consecutive calendar years. Beyond this, Millburn’s niche, relative value commodity strategy (which is now soft closed) has produced some truly exceptional returns. Investors have clearly appreciated the performance — AuM has grown steadily over the period to more than USD 6.5 billion today.

As always, Millburn’s process aims to combine the firm’s substantial market knowledge — decades of experience systematically trading global futures and FX markets — with techniques to understand data. Today, though, the process is distinguished by the application of powerful machine learning technologies (sometimes referred to as statistical learning) — tools more commonly used for single corporate securities — to macro markets. And far from an overlay or sub-strategy, the approach is used holistically for signal generation in the form of both return and trade cost forecasting.

New millennium marked early transition

Some CTAs remain to this day substantially or entirely committed to traditional trend-following (which we define as using purely technical or price data and only momentum-based models). While Millburn was one of the original innovators in trend-following — its roots date back to 1971 —interestingly it was also among the earliest managed futures firms to move away from traditional trend-following in a significant manner. Millburn began reducing allocations in mid-2000, complementing pure trend-signals with signals from independent, hypothesis-driven, single-strategy models that were based on fundamental, behavioural and other non-price data.

Getting more out of the data

The move in 2013 to the ensemble multi-factor, statistical learning-based framework, blending a variety of models, was therefore less a move away from something than a move towards what the firm saw as a better technology to deploy and harness the growing range of data that had already been feedstock for the models. “Over the last decade, it became clear to us that the explosion in data had the potential to have a major impact on understanding markets. This meant simple rules-based approaches to using momentum were perhaps not going to work as well going forward. And while some peers were moving in the direction of providing trend beta, we made the decision early-on to remain on the hunt for alpha —which meant investing heavily in a framework that could make better use of a range of data, both price and non-price. But we also wanted a process that had a built-in ability to adapt, to better match the increasing pace of change we were seeing in markets. We saw this as the source of our edge,” says Barry Goodman, Millburn’s co-CEO and Executive Director of Trading.

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