Strategy. The Fund, an exchange-traded fund (ETF), is actively managed and seeks to achieve its investment objective primarily by taking long (buy) positions in equity securities of U.S. large-cap companies based on the results of a proprietary adaptive artificial intelligence (AI) framework (the Model). The Fund defines a large-cap company to be any company that is included in the S&P 500 Index (the Index). The Index is a market capitalization weighted index representing the 500 largest public companies in the United States. The Funds investments are determined by 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 … Show more… The Fund, an exchange-traded fund (ETF), is actively managed and seeks to achieve its investment objective primarily by taking long (buy) positions in equity securities of U.S. large-cap companies based on the results of a proprietary adaptive artificial intelligence (AI) framework (the Model). The Fund defines a large-cap company to be any company that is included in the S&P 500 Index (the Index). The Index is a market capitalization weighted index representing the 500 largest public companies in the United States. The Funds investments are determined by 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 behavior and decision-making based on real-time data and market changes, rather than relying on static, predefined rules. AI-Managed Ranking System The Funds investments are determined by the Model which, at its core, is an adaptive relative ranking system that, on a daily basis, continuously ranks all 500 stocks comprising the Index from 1 (being most relatively attractive as determined by the Model) to 500 (being the least relatively attractive). The Model does not predict future performance, but rather, drawing from factors described below, establishes a dynamic and evolving view of each stock as to its relative positioning or attractiveness, based solely on how each stock ranks compared to its peers in the Index. Stocks ranked at the top may not necessarily outperform and those ranked at the bottom may not necessarily decline in value, but the Models relative positioning of each stock as compared to its peers dictates the investment selection process (as discussed below). In formulating its view and rankings of relative attractiveness, the Model compares and processes a wide range of financial news and other data relevant to each company represented in the Index, gathered from public media sources, industry and institutional data providers and financial and regulatory filings databases. The Model processes these data inputs through its adaptive AI framework to 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 systems 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). The Model is an AI-managed system that operates fully autonomously, without human intervention or interference. It functions end-to-end based on AI logic and the learned elements of the system described above. The system methodology results in a daily, model-generated ranking of all 500 stocks in the Index and the Funds portfolio is constructed directly based on this ranking process as described below. AI-Managed Investment Selection, Weighting and Rebalancing The Funds portfolio is selected based wholly on the rankings generated by the Model, which typically includes the Models top 14 to 20 ranked stocks. Using the same adaptive AI framework to draw, ingest and apply data inputs as described above, the Model determines, on a daily basis, the specific stock positions invested in by the Fund (which vary as the Models rankings change), and the optimal number of stock positions ( i.e. , typically between typically 14 to 20) based upon weightings assigned by the Model to each ranked position in the Funds portfolio. In making a daily determination of the stocks to be held by the Fund and individual position weightings, the Model takes into account regulatory investment restrictions and limitations applicable to the Fund, including compliance with tax diversification requirements applicable to registered investment companies under the Internal Revenue Code of 1986, as amended (the Code). The Sub-Adviser supervises and monitors the Model to detect and address any potential malfunctions or technology issues impacting the Models performance, which includes reviewing the Models outputs to ensure they adhere to the built-in rules and parameters. The Sub-Adviser will not alter the Models programming, and will not intervene or override the Models ranked selection, weighting and rebalancing outputs other than to ensure the Fund remains in compliance with applicable regulatory requirements. Actual performance of the Fund may differ from the performance of the Models selected and weighted stock positions due to factors including (i) timing differences 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 equivalents for operational or liquidity purposes. Accordingly, there can be no assurance that the Funds performance will fully reflect the performance of the Models selected and weighted stock positions. Portfolio Characteristics The Fund invests, under normal circumstances, at least 80% of its net assets, plus the amount of any borrowings for investment purposes, in equity securities of U.S. large-cap companies. The Fund is deemed to be non-diversified under the 1940 Act, which means that it may invest a greater percentage of its assets in the securities of a single issuer or a smaller number of issuers than if it was a diversified fund. The Funds portfolio of Index stocks, selected and weighted as dictated by the Models outputs, will be focused in certain sectors from time to time to the extent that stocks represented in the Index are focused in those sectors. As of the date of this Prospectus, the most prevalent sectors in the Index, based on market capitalization, included the communications, consumer discretionary, finance, healthcare and technology sectors. The Funds exposure to one or more market sectors is subject to change over time. The Fund is expected to have a moderate to high portfolio turnover rate on an annual basis. Show less