The LQAI ETF uses an artificial intelligence model that seeks to reflect both market conditions and company-specific information across large U.S.-listed companies. Rather than replicating a specific index, it is an actively managed exchange-traded fund that builds core U.S. large-cap exposure through security selection and weight adjustments.

It is suited to long-term investors who want to invest in U.S. large caps and who understand and accept a data-driven active selection approach rather than traditional index tracking.

It is an ETF run with an active (actively managed) approach by LG Craft.

In its management process, the fund takes U.S.-listed large caps as its universe of analysis and combines signals drawn from structured information and unstructured information such as documents and news to support security selection and weighting decisions. Its list of holdings includes NVDA, MU, TSLA, SNDK, and MSFT. Because the model's judgments can vary depending on the market environment and how company-specific information is interpreted, results may differ from those of index-tracking products.

• Use of unstructured information
• Non-index-replicating structure
• Periodic portfolio adjustments

Assets under management (AUM) stand at $2.2M, and the total expense ratio is 0.75% per year.

Because this product does not replicate an index, its holdings and weights can change depending on the model's selection results. Compared with index-tracking funds, it is therefore necessary to check not only fees and trading conditions but also the characteristics of its management decisions.

Performance trends can be influenced by the direction of the U.S. large-cap market, price swings in technology-related companies, and the company-specific signals the model reflects. Because this is an active structure that does not aim to match an index, the performance investors actually experience may differ between phases when the market moves in one direction and phases when dispersion among individual stocks widens.

When interpreting fund flows, it is better not to rely solely on the AI-management theme, and instead to check trading volume, bid-ask spreads, net asset size, and distribution policy together. Smaller exchange-traded funds may see wider gaps between market price and net asset value when trading, so investors should be careful about how they place orders.

Its hallmark is active stock selection using artificial intelligence, but investors must also accept dependence on the model and the volatility of large caps and individual stocks.

The HELO ETF in the comparison table combines equity exposure with a defensive design, so its management objective may differ from that of the LQAI ETF, which pursues purely active large-cap selection. The CBOX ETF has a relatively low cost burden, while the FHEQ ETF differs in composition in that it is an equity strategy that includes hedging elements. The INFO ETF can be viewed as a comparable product that actively manages a large-cap core, and the SFTY ETF can be examined alongside when reviewing a managed-risk approach. These products differ in management style, fees, and asset size, so they should be compared by distinguishing their objectives and trading conditions.

Before investing, check in order how selection through an artificial intelligence model differs from index tracking, whether holdings are concentrated in particular industries and large companies, and whether trading conditions suit your own order method. Quarterly distributions are separate from total return, so it is advisable not to judge the fund on cash flow expectations alone.

If the model's predictions or data interpretation do not fit changes in the market, stock selection may produce results that differ from expectations. In addition, movements in U.S. large caps and certain industries, exchange rate fluctuations, and bid-ask spreads when trading can combine to affect investment results. Investors should also note that quarterly distributions do not guarantee preservation of principal or performance.

The LQAI ETF is a product for investors who want to add AI-based active management to their U.S. large-cap exposure. When comparing it with index-tracking products, it is appropriate to review not only fees and trading conditions but also model risk, the concentration of holdings, and the nature of the quarterly distributions together.