In the midst of a fervor surrounding artificial intelligence in the investment realm, the performance of AI-powered exchange-traded funds (ETFs) is raising eyebrows. While technology and semiconductor-focused funds soar, the spotlight turns to AI-driven ETFs, which, despite the promise of advanced algorithms and data processing, are grappling to outperform traditional market benchmarks. The real story lies in the underwhelming performance of these funds, challenging the prevailing narrative that AI is set to revolutionize portfolio management.
The struggles of AI-powered ETFs
Artificial intelligence, heralded as a game-changer in the investment landscape, is facing a reality check as AI-focused ETFs falter in comparison to traditional counterparts. The Invesco QQQ Trust and VanEck Semiconductor ETF have seen remarkable gains, fueled by tech giants like Nvidia. In stark contrast, ETFs utilizing AI for portfolio construction, even with three- or five-year track records, are trailing behind broader markets.
The AI Powered Equity ETF (AIEQ), managing $103.6 million, has experienced a mere 6.5% year-to-date gain and an annualized 4% five-year return, falling short of the impressive surges seen in traditional funds. EquBot’s CEO, Chida Khatua, explains the fund’s approach, utilizing IBM Watson to analyze vast datasets for over 6,000 U.S. companies. But, the fund’s performance, especially compared to the S&P 500, reveals a struggle to live up to the AI hype.
The largest player in the AI ETF arena, the $1.6 billion SDPR S&P Kensho New Economies Composite ETF (KOMP), is also underperforming. With a negligible 0.08% year-to-date gain and a 4.3% decline on a three-year annualized basis, questions arise about the viability of AI in constructing robust portfolios.
Challenges and misconceptions in the AI investment landscape
Komson Silapachai from Sage Advisory suggests that the struggles of AI-focused funds might be more aligned with the broader challenges faced by actively managed approaches rather than inherent flaws in the AI methodology. The AI Powered ETF, following an active management model, relies on historical market performance to make probabilistic forecasts. Matt Bartolini of SPDR Americas Research emphasizes that it’s still early for AI-focused actively managed funds, citing the unconventional nature of the New Economies ETF, which defies conventional style, market cap, or sector categorization.
The expectations of retail investors regarding AI-driven funds may contribute to the mismatch between anticipation and reality. Chris Berkel, investment advisor and founder of AXIS Financial, points out a common misconception among retail investors who seek pure-play AI funds exclusively investing in companies creating the technology. Berkel cautions against misunderstanding the role of AI, emphasizing that it’s not a crystal ball but a tool to improve over time against preset objectives.
AI portfolio management to shape investment strategies
While AI’s potential is undeniable, doubts persist about its ability to consistently outperform traditional markets. Chida Khatua, CEO of EquBot, stresses that AI, in the case of AIEQ, aims to complement the S&P 500 rather than replace it. The AI ETF’s underperformance against the S&P is attributed to the latter being a large-cap index, which has historically outperformed smaller-cap indexes.
AI’s role in portfolio methodology varies, as seen with the Merlyn.AI fund. Scott Juds, Chairman & CEO of Merlyn.AI, clarifies that their AI serves as “window dressing” rather than a performance contributor. The adaptive momentum strategy utilizes genetic algorithms to counter hindsight selection bias, showcasing that the true value of AI in portfolio construction may extend beyond its direct contribution to returns.
As the debate over AI’s effectiveness in portfolio management continues, perspectives on its future diverge. While Silapachai sees potential for improvement in AI stock-picking over time, Brett Manning, senior market analyst at Briefing.com, asserts that the current use of AI supports the efficient market hypothesis. Manning contends that AI’s inability to move outside existing models and perceive market dynamics differently will hinder its ability to beat the market.
The narrative of AI revolutionizing portfolio management encounters a reality check as AI-powered ETFs grapple with underwhelming performances. As investors navigate the evolving landscape of AI in finance, the conversation shifts from AI’s promise to its practical application, urging a closer examination of these funds’ methodologies and objectives.
