Sparkline Intangible Value ETF (ITAN) Exchange: NYSE ARCA

The Fund is an actively-managed exchange-traded fund (“ETF”). The Fund will invest in U.S.-listed equity securities that Sparkline Capital LP (the “Sub-Adviser”) believes are attractive relative to its proprietary measure of “intangible-augmented intrinsic value.”   Unlike most traditional quantitative value strategies, the Sub-Adviser’s definition of intrinsic value includes an assessment of both tangible assets and intangible value. Intangible value is growing increasingly important as the economy shifts from industrial to information-based. The Sub-Adviser focuses on four pillars of intangible value: (1) human capital, (2) brand equity, (3) intellectual property, and (4) network effects, each of which are described more below.   1.       Human capital: Human capital is the value embodied by human beings. In the modern economy, the ability to attract and retain top talent can be an important source of competitive advantage, as are company cultures that motivate and nurture workers.   2.       Brand equity: Well-known brand names are often able to generate sales simply due to strong consumer recognition and loyalty. Companies may invest considerable resources in building their brands, which can constitute a large component of their market value.   3.       Intellectual property: Intellectual property encompasses creations of the human intellect. It includes both legally-protected patents and proprietary trade secrets. As science and technology plays a larger role in human society, intellectual property has increasingly become the primary source of value for many companies.   4.       Network effects: Network effects are a phenomenon by which users of a product or service derive incremental value from the addition of other users to the network. This can make it challenging for new entrants to unseat firms with dominant market positions. As globalization and the internet increase the potential scale of networks, network effects are becoming an important type of “moat.”   The Sub-Adviser employs a proprietary quantitative methodology to determine an estimated value of the foregoing four pillars for each company as well as to determine an estimated value of each company’s tangible assets – the fifth pillar.   The Sub-Adviser uses, among other sources, companies’ public accounting disclosures to analyze tangible assets. However, the Sub-Adviser has concluded that most companies’ accounting disclosures omit or give only cursory mention to their intangible value. The technical accounting definition of “intangible assets” is quite specific and captures only a narrow subset of the Sub-Adviser’s broader concept of intangible value. As a result, a key component of the Sub-Adviser’s process is its use of non-accounting data (or “alternative data”) to measure intangible value. In general, such metrics are quite varied because each intangible pillar must be measured differently.   Because alternative data is often unstructured (e.g., text) and very large, the Sub-Advisor uses natural language processing (NLP) (a form of machine learning) in addition to traditional quantitative investment techniques to incorporate the data into its investment process. NLP is specifically designed to deal with unstructured text. The Sub-Adviser generally uses open-source NLP frameworks, which are widely used and vetted, and adapts them to the unique use case of investing.   This investment process is applied to a starting investment universe of the approximately largest 1,000 publicly listed U.S. securities (by market capitalization). The Sub-Advisor may remove companies from the universe if the Sub-Adviser determines they do not have a meaningful quantity of intangible value. For each company in the investment universe, the Sub-Adviser considers multiple metrics for the company’s attractiveness according to each of the five pillars, and then averages those metrics to produce a score for each of the five pillars. This is because the Sub-Adviser believes that no one data source or metric is infallible and that by combining many metrics, a better result can be obtained. Finally, the composite score is created by summing across the five pillars. The Fund will then generally seek to hold the securities of the companies with the highest total scores.   The Sub-Adviser is not constrained by the number of portfolio holdings, except that the Fund will generally hold at least 50 securities. The Fund’s investments may include common stocks and Real Estate Investment Trusts (REITs). Although the Fund will not concentrate its investments in a particular industry, the Sub-Adviser anticipates that Fund will hold a meaningful amount of stocks in the technology, communications, healthcare, and consumer discretionary sectors.   The Sub-Advisor will seek to continually improve its valuation models used for the Fund as new datasets, methodologies and research become available. The Sub-Adviser will also employ active risk management techniques. As a result and because the Fund seeks to be fully invested at all times, the Sub-Adviser may recommend changes to the Fund’s individual positions during dynamic market conditions.