AIEQ ETF Remains a Hold as AI-Driven Stock Selection Continues to Trail Benchmark Indices

The Quick Take

• The Amplify AI Powered Equity ETF (AIEQ) maintains a Hold rating as its AI-driven stock selection strategy continues to lag benchmark indices IVV and QQQM on a risk-adjusted basis.
• High portfolio turnover — an inherent byproduct of frequent AI-driven rebalancing — generates tax drag and transaction costs that compound the benchmark performance gap.
• AIEQ raises a fundamental empirical question about current-state AI in investment management: does applying language models to stock selection generate alpha, or primarily generate fees and complexity?

AIEQ's persistent underperformance relative to simple S&P 500 and Nasdaq 100 index funds raises pointed questions about the current state of AI-driven active management in public markets. The fund's strategy — applying IBM's Watson AI to analyze vast quantities of news, financial data, and market signals to dynamically select and weight equities — represents a genuine attempt to deploy machine learning at scale in investment management. The theoretical appeal is real: AI systems can process more data faster than human analysts, potentially identifying patterns that traditional research misses. But the live track record has not substantiated the theoretical promise, with AIEQ lagging both IVV and QQQM on risk-adjusted metrics across extended holding periods.

The high turnover problem is structural, not incidental to AIEQ's specific implementation. When AI models respond to new information by signaling portfolio changes, acting on those signals requires transactions that generate frictional costs — both explicit (commissions and spreads) and implicit (market impact for larger position changes). Frequent rebalancing also creates taxable events in non-registered accounts where capital gains distributions reduce after-tax returns relative to buy-and-hold alternatives. The academic literature on AI and systematic trading generally finds that alpha generated at the signal level is substantially eroded by these implementation frictions, leaving end investors with marginal or negative net alpha after accounting for all costs. A high turnover AI strategy compounds this challenge by generating more taxable events than a lower-turnover systematic approach.

For investors drawn to the AI-in-investing concept, AIEQ's empirical track record demands careful scrutiny before committing fee dollars. Not all AI investment applications are equivalent: quant strategies with long live-trading histories and transparent factor exposures differ substantially from black-box language model applications that haven't demonstrated sustained alpha generation. The Hold rating reflects the absence of compelling evidence that AIEQ's specific approach has crossed the performance threshold needed to justify its complexity and cost premium relative to the passive alternatives it consistently trails. Investors with a specific thesis that the AI strategy will improve as the underlying model is refined may hold; new allocators should rigorously benchmark against passive alternatives before accepting the embedded active management premium.

Sources: SeekingAlpha | Published 2026-09-26