AI Trend in Investment: Promising But Challenging
Oct 18 2023
Artificial intelligence is making waves in the world of investment, with technology and semiconductor-focused funds experiencing significant gains. However, AI-powered exchange-traded funds (ETFs) are yet to fully capitalise on this trend, facing some challenges holding them back.
In a year dominated by the AI trend, funds such as the $207.2 billion Invesco QQQ Trust (QQQ) and the $9.4 billion VanEck Semiconductor ETF (SMH) have grown, showing remarkable returns of 39.3% and 47.9%, respectively. These gains are primarily attributed to the performance of key holdings like Nvidia (NVDA). In contrast, ETFs employing AI for portfolio construction, including new and repurposed funds, struggle to keep up with broader market performance.
The AI Powered Equity ETF (AIEQ), with $103.6 million in assets, boasts a 6.5% year-to-date return and a 4% annualised five-year return. Despite its promise, it carries an annual expense ratio of 0.75%. Meanwhile, the largest AI-based ETF by assets, the $1.6 billion index-based SPDR S&P Kensho New Economies Composite ETF (KOMP), has seen a meagre 0.08% year-to-date return and a three-year annualised return of 4.3%, with an annual expense ratio of 0.20%. The passively managed Merlyn.AI Bull-Rider Bear-Fighter ETF (WIZ), managing $16 million, is down 1.1% for the year and down 3% on a three-year average, featuring a 1.2% annual fee.
These AI-focused funds are not only trailing behind the S&P 500 and Nasdaq Composite but also underperforming their respective peers, according to Morningstar.
Chida Khatua, CEO and co-founder of EquBot, a partner in the AI-Powered ETF, states that their fund utilises IBM Watson’s language-processing capabilities to analyse millions of financial and non-financial data points for over 6,000 U.S. companies. This data-driven approach targets a risk-adjusted return compared to the broader U.S. equity market.
The AI-powered ETF is currently overweight in financial services and industrials but significantly underweight in technology compared to its Morningstar peer group. Despite being classified as large-cap by Morningstar, Khatua emphasises that it also includes small and mid-cap equities, making it more akin to the small-blend iShares Russell 2000 ETF (IWM). The AI ETF/s underperformance can be attributed to its focus on large-cap stocks, which have outperformed smaller-cap counterparts.
According to Komson Silapachai, a partner at Sage Advisory, the AI-powered ETF’s struggles may indicate active management underperforming indexes in general rather than a fault in its AI-based methodology.
Matt Bartolini, head of SPDR Americas Research, explains that the New Economies ETF employs natural language processing to identify innovative companies, categorise them into themes, and employ a modified equal-weighting strategy. It does not conform to a specific style, market cap, or sector categorisation, which may be contributing to its underperformance against broader markets.
The primary misconception with retail investors’ interest in AI-focused funds is their desire for pure-play investments solely dedicated to companies creating AI technology. However, AI is pervasive in various economic use cases, making such investments challenging to define.
As interest rates rise, speculative and innovation-focused ETFs have experienced setbacks, but it’s essential to understand that the goal of AI-focused funds isn’t solely to outperform the market but to shape and define it.
Chris Berkel, investment advisor and founder of AXIS Financial, highlights the need for investors to remember that AI is meant to improve against preset objectives rather than predict the future. The purpose of AI is not to serve as a crystal ball.
However, AI funds have their critics. They raise concerns due to their lack of transparency about the factors influencing their investment choices. Some AI strategies are intended more as a form of “window dressing,” serving an aesthetic purpose rather than actively contributing to performance.
AI-driven stock-picking may evolve over time, but scepticism remains. While AI has immense potential in various applications, it faces the challenge of proving its superiority in portfolio construction compared to traditional methods.
Brett Manning, a senior market analyst at Briefing.com, believes that AI is unlikely ever to surpass the current market model’s efficiency, suggesting that stock prices already incorporate all publicly available information.
In summary, while the AI trend and its role in the investment landscape continues to evolve, AI-focused ETFs are struggling with challenges that prevent their performance against traditional market benchmarks. The future of AI in investment remains promising, but for now, it has not yet displaced conventional strategies. Investors must carefully evaluate AI-based funds and recognise that AI is a tool for enhancing performance rather than predicting the future.