The AI Rally, Mega-Cap Dominance & What Comes Next

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In this episode of ETF Spotlight, I speak with Christopher Dahlin, Invesco’s Senior Factor & Core Equity Strategist, about the AI trade, equal-weighted strategies, and factor investing.

The AI trade came roaring back over the past few weeks. The Magnificent Seven's combined market capitalization topped $25 trillion during last Tuesday's trading session, while NVIDIA (NVDA - Free Report), the AI darling, is on track to become the first company ever to reach a $6 trillion valuation.

Investors appear to be treating mega-cap technology stocks as safe havens once again amid rising uncertainty surrounding the upcoming midterm elections, the ongoing war in the Middle East, and elevated interest rates.

The Nasdaq-100 Index is one of the most effective ways to play the AI rally, as it provides exposure to some of the most innovative companies leveraging AI. The Invesco QQQ ETF (QQQ - Free Report) and its cheaper counterpart, the Invesco Nasdaq 100 ETF (QQQM - Free Report), have returned more than 175% since bottoming in October 2022.

BlackRock (BLK) and State Street (STT) have also launched their own ETFs tracking the Nasdaq-100. NVIDIA, Apple (AAPL - Free Report), Microsoft (MSFT - Free Report), and Micron (MU - Free Report) account for more than 26% of the portfolios of these ETFs.

While mega-cap stocks continue to perform well, there's still a lot of interest in equal-weighted strategies. The Invesco S&P 500 Equal Weight ETF (RSP - Free Report) recently reached $100 billion in assets under management (AUM), suggesting that some investors have become concerned about concentration in the indexes.

The top 10 holdings now account for about 40% of the S&P 500, while the information technology and communication services sectors together make up almost half of the index and are dominated by AI-related names.

Momentum and high beta have been the strongest U.S. equity factors this year, while low volatility, dividend yield, and size have lagged. Factor returns are cyclical, and it's always difficult to predict market timing and factor cycles.