FINQ DOLLAR NEUTRAL U.S. Large Cap AI-Managed Equity ETF
Key Statistics
Historical Performance
Total return including reinvested distributions, from adjusted closing prices.
Price History
Price history is being compiled for this fund.
Top Holdings
Top 23 holdings as of Mar 31, 2026 · source: SEC N-PORT. Full holdings & prospectus →
About AINT
TheFund, an exchange-traded fund (“ETF”), is actively-managed and seeks to achieve its investment objective by generatingabsolute returns through a dollar-neutral investing approach based on the results of a proprietary adaptive artificial intelligence(“AI”) framework (the “Model). This approach involves taking long (buy) and short (sell) positions in equitysecurities of U.S. large-cap companies. The Fund defines a large-cap company to be any company that is included in the S&P500® Index (the “Index”). The Index is a market capitalization weighted index representing the 500largest public companies in the United States. A dollar-neutral portfolio aims to profit from the relative performance of assets,not overall market direction (as described more fully below). The Fund’s long and short dollar-neutral investments are determinedby the stock rankings and weightings as generated by the Model, which was developed and is maintained by FINQ AI, LLC (the “Sub-Adviser”)and its affiliates. An “adaptive” AI framework is one designed to learn, evolve, and dynamically adjust its behaviorand decision-making based on real-time data and market changes, rather than relying on static, predefined rules. AI-ManagedRanking System TheFund’s investments are determined by the Model which, at its core, is an adaptive relative ranking system that, on a dailybasis, continuously ranks all 500 stocks comprising the Index from “1” (being most relatively attractive as determinedby the Model) to “500” (being the least relatively attractive). The Model does not predict future performance, butrather, drawing from factors described below, establishes a dynamic and evolving “view” of each stock as to its relativepositioning or attractiveness, based solely on how each stock ranks compared to its peers in the Index. Stocks ranked at the topmay not necessarily “outperform” and those ranked at the bottom may not necessarily decline in value, but the Model’srelative positioning of each stock as compared to its peers dictates the investment selection process (as discussed below). Informulating its view and rankings of relative attractiveness, the Model compares and processes a wide range of financial newsand other data relevant to each company represented in the Index, gathered from public media sources, industry and institutionaldata providers and financial and regulatory filings databases. The Model processes these data inputs through its adaptive AI frameworkto arrive at relative attractiveness rankings and positioning. The Model: ● Draws from “common wisdom” (i.e., widely accepted beliefs and conventional advice regarding investment and market trends that drive public market behavior), “professional wisdom” (i.e., institutional data, insights and expertise from financial professionals, market analysts and asset managers), “fundamental signals” (i.e., indicators of companies’ financial health, performance, and future growth potential, such as key financial and valuation metrics, economic and industry trends, qualitative factors and market sentiment), and “regulatory interpretations” (i.e., relevant regulatory requirements and limitations). ● Ingests third-party natural language processing (NLP) data. The data is derived from written text or recorded speech, such as financial analyst reports, news sources, social media and blog posts NLP techniques are then used to extract relevant financial information, which informs the system’s analysis of market and company fundamentals, events, trends, themes, sentiments, and structures; and ● Applies advanced machine learning techniques allowing the system to evolve and self-correct over time. These include: ● genetic algorithms (i.e., search techniques designed to find optimal solutions to complex problems); ● reinforcement learning (i.e., trial-and-error processes used to train AI systems to learn optimal outcomes); and ● adaptive signal optimization (i.e., techniques to adjust or respond to constantly changing signals or inputs). TheModel is an AI-managed system that operates fully autonomously, without human intervention or interference. It functions end-to-endbased on AI logic and the learned elements of the system described above. The system methodology results in a daily, model-generatedranking of all 500 stocks in the Index and the Fund’s portfolio is constructed directly based on this ranking process asdescribed below. AI-ManagedInvestment Selection, Weighting and Rebalancing TheFund’s portfolio is selected based wholly on the rankings generated by the Model, which will reflect long positions in thetop 10 to 14 ranked stocks, and short positions in the bottom 10 to 14 ranked stocks. A short sale is a transaction in which the Fund sellsa security it does not own, typically in anticipation of a decline in the market price of that security. To effect a short sale,the Fund arranges through a broker to borrow the security it does not own to be delivered to a buyer of such security. In borrowingthe security to be delivered to the buyer, the Fund will become obligated to replace the security borrowed at the time of replacement,regardless of the market price at that time. The Fund will hold a portfolio of cash or cash equivalents, such as short-term U.S.treasury obligations and other money market instruments, as collateral for the Fund’s short positions. Usingthe same adaptive AI framework to draw, ingest and apply data inputs as described above, the Model determines, on a daily basis,the specific 10 to 14 long (i.e., highest ranked) and 10 to 14 short (i.e., lowest ranked) stock positions invested in by theFund (which vary as the Model’s rankings change), with weightings assigned by the Model to each of the long andshort positions in the Fund’s portfolio. In making such determinations, the Model takes into account regulatory investmentrestrictions and limitations applicable to the Fund, including complying with tax diversification requirements applicable to registeredinvestment companies under the Internal Revenue Code of 1986, as amended (the “Code”). TheModel seeks to maintain a dollar-neutral Fund portfolio with the aim of balancing long and short investment exposuresas a means to reduce market direction dependency and instead, focus on relative price performance among the long and short positions.For example, assume the Fund has a $75 cash position to be invested on a dollar-neutral basis to achieve a $100 long positionin ABC stock and a $100 short position in XYZ stock. The Fund in this example could sell short $100 of XYZ stock, withthe short sale proceeds increasing its cash position to $175, and then buy $100 of ABC stock. This results in a $100 XYZ shortposition, a $100 ABC long position and $75 in remaining cash (available for collateral on the short sale). In this example, theFund’s net exposure is $0 ($100 long ABC stock - $100 short XYZ stock = $0), while its gross exposure – the sum ofthe absolute value of both long and short positions – is $200. The use of leverage in this example (where gross exposureexceeds the capital invested) can amplify both potential gains and losses. See “Principal Investment Risks – “LeverageRisk” and “Short Sales Risk” below. The strategy aims to profit from the relative performance betweensuch long and short positions, not from overall market direction. In other words, the strategy can potentially gain if the longpositions outperform while the short positions underperform, while the dollar amounts invested in long andshort positions act as a potential hedge against market-wide fluctuations. There is no guarantee, however, that a dollar-neutralinvestment strategy will be successful, achieve profits or avoid losses. The Sub-Adviser supervises and monitors the Model to detect and address any potential malfunctions or technology issues impacting theModel’s performance, which includes reviewing the Model’s outputs to ensure they adhere to the built-in rules and parameters.The Sub-Adviser will not alter the Model’s programming, and will not intervene or override the Model’s ranked selection, weightingand rebalancing outputs other than to ensure the Fund remains in compliance with applicable regulatory requirements. Actual performanceof the Fund may differ from the performance of the Model’s selected and weighted stock positions due to factors including (i) timingdifferences between when the Model generates its outputs and when corresponding portfolio securities transactions are executed and settled,(ii) brokerage commissions, transactions costs and other fees and expenses of the Fund; and (iii) any Fund holdings of cash or cash equivalentsfor operational or liquidity purposes. Accordingly, there can be no assurance that the Fund’s performance will fully reflect theperformance of the Model’s selected and weighted stock positions. PortfolioCharacteristics TheFund invests, under normal circumstances, at least 80% of its net assets, plus the amount of any borrowings for investment purposes,in long and short positions in equity securities of U.S. large-cap companies. TheFund is deemed to be non-diversified under the 1940 Act, which means that it may invest a greater percentage of its assets inthe securities of a single issuer or a smaller number of issuers than if it was a diversified fund. The Fund’s portfolioof long and short positions in Index stocks, selected and weighted as dictated by the Model’s outputs, will be focused incertain sectors from time to time to the extent that stocks represented in the Index are focused in those sectors. As of the dateof this Prospectus, the most prevalent sectors in the Index, based on market capitalization, included the communications, consumerdiscretionary, finance, healthcare and technology sectors. The Fund’s exposure to one or more market sectors is subjectto change over time. TheFund is expected to have a moderate to high portfolio turnover rate on an annual basis.
AINT News
Data for AINT is aggregated from third-party providers (Tiingo, Nasdaq, Finnhub) and SEC filings, may be delayed at least 20 minutes, and may be incomplete or contain errors. Nothing here is investment advice. Verify with the official prospectus before investing.