Strategy. The Fund, an exchange-traded fund (ETF), is actively-managed and seeks to achieve its investment objective by generating absolute 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 equity securities of U.S. large-cap companies. 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. A dollar-neutral portfolio aims to profit from the relative performance of assets, not overall market direction (as described more fully below). The Funds long and short dollar-neutral … Show more… The Fund, an exchange-traded fund (ETF), is actively-managed and seeks to achieve its investment objective by generating absolute 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 equity securities of U.S. large-cap companies. 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. A dollar-neutral portfolio aims to profit from the relative performance of assets, not overall market direction (as described more fully below). The Funds long and short dollar-neutral 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 will reflect long positions in the top 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 sells a 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 borrowing the 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 Funds short positions. 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 10 to 14 long ( i.e. , highest ranked) and 10 to 14 short ( i.e. , lowest ranked) stock positions invested in by the Fund (which vary as the Models rankings change), with weightings assigned by the Model to each of the long and short positions in the Funds portfolio. In making such determinations, the Model takes into account regulatory investment restrictions and limitations applicable to the Fund, including complying with tax diversification requirements applicable to registered investment companies under the Internal Revenue Code of 1986, as amended (the Code). The Model seeks to maintain a dollar-neutral Fund portfolio with the aim of balancing long and short investment exposures as 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 position in ABC stock and a $100 short position in XYZ stock. The Fund in this example could sell short $100 of XYZ stock, with the short sale proceeds increasing its cash position to $175, and then buy $100 of ABC stock. This results in a $100 XYZ short position, a $100 ABC long position and $75 in remaining cash (available for collateral on the short sale). In this example, the Funds net exposure is $0 ($100 long ABC stock - $100 short XYZ stock = $0), while its gross exposure the sum of the absolute value of both long and short positions is $200. The use of leverage in this example (where gross exposure exceeds the capital invested) can amplify both potential gains and losses. See Principal Investment Risks Leverage Risk and Short Sales Risk below. The strategy aims to profit from the relative performance between such long and short positions, not from overall market direction. In other words, the strategy can potentially gain if the long positions outperform while the short positions underperform, while the dollar amounts invested in long and short positions act as a potential hedge against market-wide fluctuations. There is no guarantee, however, that a dollar-neutral investment 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 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 long and short positions 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 long and short positions in 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