QQQ vs SPMO: Which ETF Wins in AI Boom?

The Invesco S&P 500 Momentum ETF (SPMO) has emerged as the clear short-term winner in the AI-driven market surge, posting remarkable performance figures that have surpassed even the tech-heavy QQQ. Year-to-date, SPMO has gained just shy of 26%, significantly outpacing the QQQ’s 17.6% return. Zooming out to a two-year timeframe reveals an even more pronounced outperformance, with SPMO delivering a staggering 105% gain compared to QQQ’s still-respectable 63% return.

This momentum-driven success stems from SPMO’s unique construction as a factor ETF that captures the hottest-performing stocks across the entire S&P 500 universe. Unlike QQQ’s exclusive focus on Nasdaq-listed companies, SPMO draws from both NASDAQ and NYSE listings, allowing it to capture explosive movers like GE Aerospace, which surged 242% over two years, and Oracle, which gained 187% – both companies that QQQ completely missed due to their NYSE listings.

The SPMO portfolio demonstrates significant concentration in high-momentum AI plays, with Palantir commanding a substantial 4.64% weighting compared to QQQ’s 1.3% allocation. This momentum strategy has proven particularly effective during the AI boom, capturing winners not just from technology but across financial, retail, and industrial sectors that have benefited from the AI revolution’s broad economic impact.

The Invesco QQQ Trust offers investors a fundamentally different approach to AI exposure through its tracking of the Nasdaq 100 index. While this strategy has resulted in lower recent returns compared to SPMO, it provides what many consider a purer play on the core drivers of the AI revolution. The QQQ’s heavy concentration in the Magnificent Seven companies represents both its greatest strength and potential vulnerability.

This concentrated approach means investors get substantial exposure to the tech titans leading AI development and deployment – companies making massive investments in AI infrastructure that investors hope will yield significant returns in the future. The author argues that fears about overconcentration are overrated, suggesting investors should actively seek greater weighting in these market leaders rather than avoiding them.

For younger, growth-oriented investors who can withstand potential tech sector volatility, the QQQ presents an attractive alternative to broader market ETFs. The Nasdaq 100 represents a well-constructed basket of growth stocks positioned to benefit if the AI boom continues for years or even decades. The author recommends treating QQQ as a buy-and-hold investment to be accumulated during market weakness, viewing it as a long-term vehicle for AI revolution participation.

When evaluating these two ETFs for AI exposure, investors face a classic growth versus momentum dilemma. The SPMO has demonstrated its ability to capture the hottest AI-related movers across multiple exchanges and sectors, resulting in superior recent performance. Its momentum factor approach has proven particularly effective during periods of strong market trends, as evidenced by its 105% two-year return.

Meanwhile, QQQ offers what the author views as more direct exposure to the AI boom through its heavy weighting in the Magnificent Seven. This concentration in companies making massive AI infrastructure investments could position QQQ for stronger long-term performance if these investments yield significant returns. The exclusion of NYSE-listed AI players like Oracle represents a notable gap in QQQ’s coverage that investors must consider.

The author’s ultimate recommendation favors QQQ for core AI exposure while suggesting investors supplement with individual stocks like Oracle to fill coverage gaps. This hybrid approach acknowledges QQQ’s strength in concentrated tech exposure while addressing its limitations regarding exchange restrictions. Both ETFs represent compelling options for AI-focused investors, with the choice ultimately depending on individual risk tolerance, time horizon, and belief in the sustainability of the current momentum trend versus long-term tech dominance.