Could Artificial Intelligence Fix Active Management’s Underperformance?

According to an article in Institutional Investor, the attempts by active managers to outperform benchmarks by running concentrated portfolios is being undermined by their efforts to “manage risks by diversifying into hundreds of stocks.”

The article suggests that the use of “ensemble methods,” which rely on artificial intelligence and machine learning, could afford firms the opportunity to use “the highest conviction picks of multiple managers, rather than the single manager employed by most active funds, to provide downside protection.”

Alexey Panchekha, a Ph.D. in math and physics and the tech “guru” for the Ensemble Asset Management Research Consortium–which works to raise awareness about the benefits of using these techniques in active management–says, “you have to manage the risks of owning fewer securities, otherwise your firm could be killed during times of underperformance.”

A recent paper on the subject argues that “the integration of multiple independent investment strategies through the application of ensemble methods techniques allows diversification of individual managers’ biases, and substantially reduces the potential for toxic tail events.”

But according to advisor Bob Willis of Willis Investment Counsel, the research study findings are based on portfolios that were constructed on a “historical and hypothetical basis and the time period analyzed was limited, dating back to July 2007.” He warns, “We should all be appropriately skeptical of any purported breakthrough or silver bullet and remember how often they look impressive on paper with their elegant mathematical equations, but frequently do not work in practice.”