FINQ’s AI-managed exchange-traded funds have recorded a strong start, outperforming the S&P 500 by substantial margins since launching in February 2026. The company’s reported figures show AIUP returning 32.66% and AINT returning 41.56% from February 5 through September 30, 2026, compared with a 10.96% return for the benchmark.
The performance gap is significant: AIUP exceeded the S&P 500 by 21.70 percentage points, while AINT outperformed by 30.60 percentage points. But the more interesting question is not simply how much the funds have gained. It is whether an investment process run entirely by artificial intelligence can sustain an advantage as markets change.
FINQ is betting that it can. Its proprietary system evaluates financial and market data, ranks index constituents, selects securities, and adjusts portfolio weights without human intervention in the investment-management process. The company says its two ETFs are the first SEC-registered funds in the United States to be fully managed by AI.
A System Built Around Relative Rankings
FINQ’s model does not treat every stock in a large-cap index as an equally attractive investment opportunity. Instead, it continually assesses the available data and ranks companies according to its investment framework.
That ranking process feeds into two distinct products.
AIUP, the FINQ FIRST U.S. Large Cap AI-Managed U.S. Equity ETF, follows a long-only strategy. It invests in selected U.S. large-cap equities and adjusts allocations according to the model’s rankings. FINQ reported that AIUP outperformed the S&P 500 at every month-end since its February 5 inception.
AINT, the FINQ Dollar Neutral U.S. Large Cap AI-Managed U.S. Equity ETF, applies the same broad concept through a different structure. It buys stocks the model ranks highly and sells short those ranked lowest. This approach aims to capture differences in relative stock performance, rather than depending solely on gains across the wider equity market.
According to FINQ, AINT outperformed the S&P 500 at every month-end except its first month of trading. Its 41.56% reported return through September 30 was higher than AIUP’s 32.66%, although the different strategies also carry different risk exposures.
Why Autonomous Management Is the Central Proposition
The distinction between AI-assisted investing and AI-managed investing matters to FINQ’s business model.
An AI-assisted process might use machine learning to screen stocks or help analysts evaluate information before a human manager makes the final decision. FINQ’s stated approach is more comprehensive: its framework is designed to conduct the ranking, selection, and portfolio-weighting process autonomously.
The potential advantage lies in the ability to evaluate large amounts of information continuously and update decisions as the investment environment changes. FINQ believes this can help the funds identify opportunities and respond to market developments with a degree of speed and scale that traditional human-managed processes may find difficult to replicate.
Founder and CEO Eldad Tamir framed the performance as evidence of the system’s consistency.
“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.”
The reported returns support the company’s early performance narrative. They do not, however, independently establish which elements of the model produced the gains or whether those elements will remain effective in future markets.
What Could Challenge the Model?
A short performance record can be influenced by the particular market conditions in which a strategy begins trading. FINQ’s ETFs had been operating for less than eight months as of September 30, 2026, leaving many potential market environments untested.
A model that successfully identifies leading stocks during one period may struggle when leadership rotates or when relationships between securities change. Short-selling strategies face additional challenges: positions can move against expectations, and losses on short positions can be substantial.
There is also a distinction between beating a benchmark and delivering an appropriate investment outcome for a particular investor. Fees, volatility, portfolio exposure, and an individual’s investment horizon all matter when comparing funds.
The reported returns should therefore be treated as historical results rather than forecasts. Past performance does not guarantee future results. Investment returns and principal values fluctuate, and investors may receive less than their original investment when selling shares. Current performance may be higher or lower than the figures presented here.
The Next Phase for FINQ
FINQ’s launch-period results give investors a starting point for assessing its autonomous investment model. The company has demonstrated that its two funds have outperformed the S&P 500 over the period reported, using both long-only and dollar-neutral approaches.
The next phase will require a longer record, more varied market conditions, and continued scrutiny of risk-adjusted performance. If the funds can sustain their results through different market cycles, FINQ may strengthen the case for AI as a direct participant in portfolio management.
For now, the September 30, 2026 figures offer an encouraging but preliminary snapshot of what autonomous investing can deliver. The durability of that performance remains the central question.
