A new type of investment model has hit the scene, and its proponents believe it can potentially beat both passive and active management on a consistent basis. That’s a bold claim to live up to, but backtested data—and a brief run as live models—suggest it might have something to add to the investment conversation. 

Melius Investments has introduced a platform containing 10 model portfolios that use artificial intelligence to identify the highest-conviction picks from successful active mutual fund managers, and then puts them into tidy packages designed to generate investment alpha.

The underlying principle behind these portfolios is something called ensemble active management, or EAM, which is based on mathematical techniques known as ensemble methods that strive to improve the accuracy of predictive algorithms.

According to a 2018 white paper from the EAM Research Consortium, this methodology links together multiple, independent predictive algorithms and looks for consensus or near-consensus agreement between them. “Ensemble Methods generate ‘multi-expert’ predictive systems, which have been proven to be superior to stand-alone ‘single-expert’ predictors,” said the paper, whose authors work in the fields of technology, exchange-traded funds and financial planning.

The white paper said ensemble methods date from the late-1970s and are considered a foundational approach for most AI and machine learning applications. It further stated that ensemble methods have been successfully used in applications ranging from facial recognition and self-driving cars to weather prediction and medicine.

To test the potential usefulness of this methodology with investments, researchers used ensemble methods to scour the portfolios of actively managed mutual funds and apply predictive algorithms designed to identify securities that are likely to outperform the market. They crunched data covering the period from July 2007 to December 2017, and according to the white paper came up with the following results that compared the algos versus both actively managed mutual funds and the S&P 500 Index:

• EAM portfolios outperformed the S&P 500 72% of the time over rolling one-year periods, with an average annual excess return of 3.4%.

• EAM portfolios achieved a 94% success rate versus the S&P 500 for rolling three-year periods, with an average annual excess return of 3.8%.

• EAM portfolios outperformed traditional active management 82% of the time over rolling one-year periods, and 95% of the time for rolling three-year periods.

Meanwhile, the white paper said, the average large-cap actively managed fund outperformed the average large-cap passive fund just once out of 255 rolling periods from January 2008 to December 2017.

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