In early 2026, FINQ's AI-managed ETFs subtly surpass Wall Street's performance.

      Artificial intelligence has long been expected to transform asset management, and by 2026, evidence of that transformation is beginning to appear in performance metrics. The AI-managed ETFs from FINQ are early examples of the outcomes achieved when portfolio construction is handled by a fully systematic, constantly learning model as opposed to human judgement. Since their launch on February 5, 2026, on NYSE Arca, both funds have not only matched the S&P 500 but have also significantly outperformed it.

      While the results may seem straightforward at first glance, they carry deeper implications: AI is now responsible for making investment decisions from start to finish, rather than merely aiding in them.

      Remarkable Early Performance

      As of May 31, 2026, FINQ’s leading ETFs have shown the following performance since their inception:

      FINQ FIRST U.S. Large Cap AI-Managed U.S Equity ETF (AIUP): 15.30% return compared to the S&P 500’s 10.07%

      FINQ Dollar Neutral U.S. Large Cap AI-Managed U.S Equity ETF (AINT): 27.13% return versus the S&P 500’s 10.07%

      AIUP has consistently beaten the benchmark at the end of each month since its launch. Meanwhile, AINT, after a single month of underperformance, has reliably remained ahead of the index.

      Market pricing has closely mirrored net asset value: AIUP closed at $28.00 (NAV $27.93), and AINT finished at $31.78 (NAV $31.74), indicating a strong correlation between the trading price and underlying assets.

      The comparison is evident. The S&P 500 produced a return of 10.07% during the same period, solid by historical measures, but notably lagging behind both AI-driven funds in a brief timeframe.

      Inside the AI Model: Capital Allocation

      At the heart of both ETFs is FINQ’s proprietary AI system, designed to continuously rank, select, and weight index components based on real-time data signals.

      Instead of depending on periodic rebalancing or analyst discretion, the model analyzes large volumes of financial and market information across all index constituents, dynamically adjusting exposures based on changing probabilities of performance.

      This leads to two different applications of the same intelligence framework:

      AIUP is a long-only large-cap equity ETF focusing on top-ranked stocks while ensuring broad index alignment.

      AINT is a dollar-neutral long/short strategy that invests in high-ranked stocks while shorting the lowest-ranked, effectively isolating the AI’s ranking signal.

      This dual structure enables FINQ to evaluate the same intelligence system under two distinct market scenarios: directional and market-neutral.

      Importance of Early Outperformance

      Short-term performance of ETFs is rarely sufficient to establish a structural advantage. Markets can be unpredictable, and initial outcomes may stem from timing luck.

      However, FINQ’s early data shows a more compelling trend: consistency. AIUP’s uninterrupted month-end outperformance indicates that the model is not just capturing random sector swings but is continuously adapting to changes in index leadership. AINT’s recovery after the initial month suggests the ranking engine may be enhancing itself in response to real market conditions.

      In both scenarios, the underlying premise is the same: rapid adjustments become increasingly important during periods of macroeconomic volatility, sector rotation, and concentrated market leadership.

      A Transformation in Portfolio Construction

      Traditional active management heavily depends on analyst interpretations layered over fundamental and quantitative signals. FINQ’s methodology eliminates that interpretive step entirely.

      Allocation decisions are made by an independent system that recalibrates positions as new information is received, without the delays of human review cycles.

      As the company outlines, the system is intended to react to market changes instead of analyzing them retrospectively.

      This distinction is increasingly significant in a climate where index performance is frequently influenced by a small number of quickly changing leaders.

      “Autonomous Investing Will Continue to Shape Asset Management”

      “These results illustrate the effectiveness and reliability of our AI framework in dynamic market conditions,” stated Eldad Tamir, founder and CEO of FINQ. “I believe autonomous investing will continue to redefine asset management, and the performance of AIUP and AINT showcases the expanding capacity of AI to adapt, spot opportunities, and react to market shifts on a large scale.”

      His remarks signal a broader shift in industry discussions, moving from whether AI can contribute to portfolio management to whether it might ultimately replace the decision-making layer altogether.

      Looking Forward

      Both ETFs are still in the early stages of their performance records, and FINQ acknowledges the usual disclaimer: past performance does not predict future results. Nonetheless, the initial trajectory is significant, as it is not based on a single concentrated investment or isolated market conditions.

      Rather, it reflects a continuously operating system striving to outperform an index in real-time, a task that human-managed strategies have traditionally found challenging to maintain over extended periods.

      Whether this initial advantage continues will depend on how the model adapts through different macroeconomic regimes. For now