FINQ's AI-managed ETFs outperform S&P 500 since launch

FINQ's two AI-managed ETFs, AIUP and AINT, launched on NYSE Arca on February 5, 2026, have outperformed the S&P 500 since inception, with AIUP returning 15.30% and AINT returning 27.13% as of May 31, 2026, compared to the S&P 500's 10.07%. The funds are the first SEC-registered U.S. ETFs fully managed by an autonomous AI without human intervention, marking a milestone in AI-driven portfolio management.

FINQ's AI-managed ETFs outperform S&P 500 since launch FINQ's two AI-managed ETFs, AIUP and AINT, launched on NYSE Arca on February 5, 2026 and have continued to outperform the S&P 500. According to The Next Web, as of May 31, 2026, AIUP returned 15.30% since inception versus the S&P 500's 10.07%, and AINT returned 27.13% versus the same benchmark. Both funds are driven by FINQ's proprietary autonomous AI framework, with AIUP as a long-only large-cap vehicle and AINT as a dollar-neutral long/short implementation. The Next Web reports tight NAV-to-market pricing: AIUP at $28.00 NAV $27.93 and AINT at $31.78 NAV $31.74 . These are the first SEC-registered U.S. ETFs in which stock selection, weighting, and rebalancing are handled entirely without human portfolio management. The four-month sample is operationally encouraging but insufficient to confirm persistent alpha. What happened FINQ's two SEC-registered AI-managed ETFs, AIUP and AINT, launched on NYSE Arca on February 5, 2026. According to The Next Web, as of May 31, 2026, AIUP returned 15.30% since inception versus the S&P 500's 10.07%, and AINT returned 27.13% versus the same benchmark. The Next Web reports NAV-to-market pricing remained tight: AIUP closed at $28.00 NAV $27.93 and AINT at $31.78 NAV $31.74 . How the funds work Per The Next Web, both ETFs are driven by FINQ's proprietary AI framework that continuously ranks, selects, and weights S&P 500 constituents using real-time financial, market, news, sentiment, and institutional data. AIUP is a long-only large-cap vehicle gross expense ratio 0.70% , while AINT is a dollar-neutral long/short vehicle that shorts lowest-ranked names to isolate relative performance gross expense ratio 1.25% . Per FINQ's official filings and May 2026 performance report, both funds are fully autonomous - no human portfolio manager intervenes in selection, weighting, or rebalancing decisions. Industry context Systematic AI models running end-to-end portfolio construction represent a growing trend in quantitative finance. For AI and ML practitioners, the FINQ architecture illustrates one production path for model-driven portfolio management: daily AI rankings feed directly into ETF construction without a human override layer. The AINT dollar-neutral structure - which uses relative rankings to hold simultaneous long and short positions - shows how AI ranking signals can be monetized independently of broader market direction. The AINT strategy's strong since-inception return should be interpreted cautiously: dollar-neutral funds can show amplified short-window returns when cross-sectional stock dispersion is high, and a four-month window does not establish durable alpha. What to watch Meaningful assessment of persistent alpha will require multi-quarter performance across different market regimes, along with published disclosures on turnover, transaction costs, and AUM growth. Regulatory filings and third-party governance audits would provide additional signal on operational robustness. Eldad Tamir, FINQ founder and CEO, has signaled plans to expand beyond these two funds toward a fuller AI-based wealth management platform. Scoring Rationale FINQ's AI-managed ETFs represent a genuinely novel deployment - the first SEC-registered fully-autonomous AI portfolio managers - making this relevant to AI practitioners in quantitative finance. However, this is a promotional performance update driven by a single tech-outlet source TNW with May 31 figures that lack independent corroboration; the story is narrow in audience and reflects FINQ's own PR cycle rather than independent analysis. Practice with real FinTech & Trading data 90 SQL & Python problems · 15 industry datasets Active Verified Users by Income TierEasy /problems/sql/active-verified-users-by-income Technology Stocks with High BetaMedium /problems/sql/technology-stocks-with-high-beta Portfolio Performance ScorecardHard /problems/sql/portfolio-performance-scorecard 250 free problems · No credit card See all FinTech & Trading problems /problems/datasets/fintech