The AMOM ETF is an actively managed ETF that seeks capital growth by selecting U.S. large-cap stocks with strong price momentum. It uses artificial intelligence to analyze data patterns as part of its management decisions, and its stock selection approach differs from simple index tracking.

It suits investors who want U.S. large-cap momentum exposure and who understand the characteristics of AI-based stock selection and active management.

This is an actively managed ETF run by Qraft AI.

The AMOM ETF is an actively managed ETF that uses past price momentum within the U.S. large-cap universe as the starting point for selection, while AI interprets data patterns to support stock selection and portfolio construction. Its publicly disclosed holdings include MU, AMD, CSCO, CAT and PANW, so changes in individual stocks and sectors can affect performance.

• A combination of the momentum factor and AI-based stock selection
• Active management within the U.S. large-cap universe

Assets under management (AUM) stand at $21.9M, and the total expense ratio is an annual 0.75%.

It is worth checking the fee difference against peer momentum ETFs, along with trading conditions.

A momentum approach can deliver relative outperformance when stock price trends continue, but when trends reverse quickly, portfolio turnover and volatility may increase. There is no guarantee that AI-based selection will always anticipate market moves, so the limits of the management approach should also be considered.

Fund flows can be affected by investor sentiment toward growth stocks and technology-related large caps, interest rates, corporate earnings, and shifts in risk appetite. Because of the ETF structure, changes in the activity of market participants and liquidity providers can affect the gap between trading price and net asset value, so trading conditions deserve a check.

The fund is defined by its combination of momentum exposure and AI-based selection, but model risk and a higher cost burden than comparable products should be weighed as well.

In the comparison table, the MTUM ETF reflects U.S. large-cap momentum through an index-based approach, while the AMOM ETF also emphasizes AI-driven stock selection. The SPMO ETF is another benchmark within large-cap momentum, and the XMMO ETF and XSMO ETF show momentum exposure in the mid-cap and small-cap segments respectively, making it possible to check differences by market capitalization range. The JMOM ETF is a useful comparison for the momentum factor approach, allowing investors to review management style and cost structure together.

When evaluating the AMOM ETF, investors should understand both the characteristics of the momentum factor and how AI-based selection affects the portfolio. Costs, distribution method, concentration in individual stocks and sectors, and trading conditions are key items to verify before holding.

Because the AMOM ETF relies on momentum signals and an AI model, changes in market conditions or limits in data interpretation can lead to performance swings. Concentration in individual stocks and sectors, the gap between trading price and net asset value, and exchange rate changes against the Korean won should also be taken into account.

The AMOM ETF can be considered by investors who want an active approach that adds AI-based stock selection to U.S. large-cap momentum. Compared with index-based momentum exposure, it is appropriate to check differences in management style, fees, holdings composition, and trading conditions together.