For decades, active investment managers have competed on their ability to identify promising companies, anticipate market changes, and allocate capital more effectively than a benchmark index. FINQ is testing whether an artificial intelligence system can take on that responsibility autonomously.
The company’s two AI-managed exchange-traded funds delivered returns substantially above the S&P 500 from their February 5, 2026 inception through September 30, 2026, according to FINQ’s reported figures. The FINQ FIRST U.S. Large Cap AI-Managed U.S. Equity ETF (AIUP) returned 32.66%, while the FINQ Dollar Neutral U.S. Large Cap AI-Managed U.S. Equity ETF (AINT) returned 41.56%. The S&P 500 returned 10.96% over the same period.
The results offer an early indication of how FINQ’s investment framework has performed since its launch on NYSE Arca. They also raise a broader question for asset managers: Can autonomous systems consistently translate large volumes of financial information into investment decisions that outperform conventional benchmarks?
Replacing the Traditional Investment Workflow
FINQ’s approach starts with the idea that investment opportunities change continuously. A company that ranks highly at one point may become less attractive as new financial information emerges, market sentiment shifts, or competing businesses strengthen their positions.
Its proprietary AI framework is designed to evaluate financial and market data across index constituents in real time. It then ranks companies, selects holdings, and determines portfolio weights, allowing the funds to adjust their positions as the system’s assessments change.
This differs from an investment process in which human portfolio managers make the final decisions after reviewing research, forecasts, and quantitative signals. FINQ says its ETFs are fully managed by artificial intelligence, without human intervention in the investment-management process.
The company describes AIUP and AINT as the first SEC-registered ETFs in the United States to be fully managed by AI. That positioning places FINQ in a specialized part of the investment industry, where the objective is not simply to use machine learning to inform human judgment but to automate the portfolio-management process itself.
Still, autonomy does not eliminate the challenges of investing. Models depend on their design, data, assumptions, and ability to respond to conditions that may differ from those encountered previously.
The Numbers Behind FINQ’s Performance
The two funds reported different returns despite sharing the same underlying AI framework.
AIUP follows a long-only strategy focused on U.S. large-cap equities. It seeks to gain exposure to companies that the model ranks most favorably, with holdings and weights adjusted according to its assessments. FINQ reported that AIUP outperformed the S&P 500 at every month-end since inception.
AINT uses a dollar-neutral strategy, buying highly ranked companies and selling short those with the lowest rankings. The approach is intended to capitalize on relative differences between securities rather than depending exclusively on broad market appreciation. FINQ said AINT outperformed the S&P 500 at every month-end except its first month of trading.
As of September 30, 2026, AINT’s 41.56% return exceeded the S&P 500’s 10.96% by 30.60 percentage points. AIUP’s 32.66% return exceeded the same benchmark by 21.70 percentage points.
These comparisons demonstrate the funds’ reported performance during the period, but they do not establish that the AI framework will outperform under all market conditions. Nor do the two funds’ different returns, by themselves, establish which strategy will prove more effective over a longer investment horizon.
Why the Results Matter for the Industry
The asset management industry has spent years incorporating quantitative models, algorithmic trading, and machine-learning tools into investment decisions. FINQ’s proposition goes further by making AI the central decision-making mechanism for its ETFs.
The potential appeal is scale. An autonomous system can evaluate many securities and update its assessments continuously, potentially identifying changes that would be difficult for a traditional research team to monitor manually. Whether that capability produces lasting investment advantages depends on the quality of its decisions and how effectively it manages risk.
“These results demonstrate the strength and consistency of our AI framework during dynamic market environments,” said Eldad Tamir, founder and CEO of FINQ. “I believe autonomous investing will continue to reshape asset management, and the performance of AIUP and AINT reflects the growing ability of AI to adapt, identify opportunities, and respond to market changes at scale.”
For FINQ, the opportunity is to establish that autonomous management can deliver more than a compelling technology story. Sustained performance, transparent disclosures, and a clear understanding of risk will matter as much as the underlying system.
The Longer-Term Challenge
The figures reported through September 30 represent less than eight months of performance since the ETFs launched in February. That is a limited window for evaluating an investment model intended to adapt to different market environments.
The strategies also carry different risks. AIUP remains exposed to equity-market declines, while AINT’s short positions introduce the possibility of losses when securities rise against the model’s expectations. Strong initial returns do not remove either concern.
Investors should review the funds’ official standardized performance, prospectuses, expenses, and risk disclosures before making investment decisions. Past performance is not a guarantee of future results, and current performance may differ from the figures reported here. Investment returns and principal values fluctuate, so shares may be worth more or less than their original cost when sold.
FINQ has presented an early case for autonomous investing through two funds that have outperformed their benchmark since inception. The more consequential test will be whether that advantage survives changing market conditions and a substantially longer track record.
