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Millburn’s Adaptive Approach Proves Its Worth

Statistical learning combines with trend-following

Millburn has advanced in three core directions: statistical learning techniques added to trend-following to make systems more adaptive, responsive and empathetic to the moods of the markets; multi-factor models mean Millburn has more radars picking up more useful signals; and a wider investment universe, including non-directional trades, further expands the potential opportunity set; execution efficiency has also been enhanced. The result has been an increasingly differentiated return profile.

The art and science of statistical learning

While Millburn continues to make significant use of trendfollowing, and feels it has advanced and refined that approach over decades, the utilisation of statistical learning in concert with trend-following reflects a global shift. In fact, statistical learning has superseded other approaches in many industries. Says Barry Goodman, Millburn’s co-CEO and executive director of trading, “Flexible statistical learning methods have already displaced traditional forecasting in many fields. Approaches have improved remarkably in the last 10 years, especially with the push it has been given as firms like Google, Amazon and Netflix came to understand the opportunities it provides. But it is not perfect, and still requires real understanding of how to apply it, and, we think, a mindset of what I call heightened humility.”

Statistical learning from other industries cannot simply be copied and pasted into finance, though, as it entails unique features. For instance, as Grant Smith, Millburn’s head of research and Goodman’s co-CEO counterpart, points out: “The financial markets are extremely noisy, and present a very difficult problem in return forecasting when compared with many other applications. This is our challenge.”

Once signals have been disentangled from noise, the next stage in the process is determining how to combine different types of signals. Millburn has made breakthroughs in this area. For a number of years Millburn followed the standard quantitative investment paradigm and simply applied the scientific method to each data set discretely or individually, developing standalone models producing unique signals that were combined ex post. But Goodman explains why this approach was found to be suboptimal, and part of the answer lies in behavioural bias. “While the addition of non-price data was valuable, the ‘averaging effect’ that occurred when individual signals were combined meant a certain amount of information loss. Also, the combination of signals—specifically the weights that were given to each signal—was subject to the influences of hindsight and other behavioural biases that human researchers have. In short, we knew that we were not being as efficient as possible in terms of extracting information from this data.”

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About The Hedge Fund Journal

INFORMING THE HEDGE FUND COMMUNITY With access to some of the industry’s biggest names and an astute and talented group of writers and contributors, The Hedge Fund Journal has established itself as a trusted source of information on the hedge fund industry.

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