Strategy. The Fund is an actively managed exchange-traded fund (ETF) that seeks to achieve its investment objective by investing in U.S. equity securities that the Funds sub-adviser, Euclidean Technologies Management, LLC, (the Sub-Adviser) believes are under-valued and under-appreciated by the market. The Fund employs a quantitative and systematic approach to long-term investing. It is expected that the Fund will generally hold 60 to 70 stocks that are selected from a universe that includes all publicly traded stocks listed on the New York Stock Exchange (NYSE) and the Nasdaq Market (Nasdaq). To identify portfolio investments, the Sub-Advisers has developed the following multi-step investment process: Step 1: First, the Sub-Adviser screens the list of all publicly traded stocks on the NYSE and Nasdaq … Show more… The Fund is an actively managed exchange-traded fund (ETF) that seeks to achieve its investment objective by investing in U.S. equity securities that the Funds sub-adviser, Euclidean Technologies Management, LLC, (the Sub-Adviser) believes are under-valued and under-appreciated by the market. The Fund employs a quantitative and systematic approach to long-term investing. It is expected that the Fund will generally hold 60 to 70 stocks that are selected from a universe that includes all publicly traded stocks listed on the New York Stock Exchange (NYSE) and the Nasdaq Market (Nasdaq). To identify portfolio investments, the Sub-Advisers has developed the following multi-step investment process: Step 1: First, the Sub-Adviser screens the list of all publicly traded stocks on the NYSE and Nasdaq to exclude non-U.S. based companies, financial sector companies and any company with a market capitalization of less than $1 billion at the timing of screening (the Investment Universe). After the completion of the screening process, the Investment Universe will consist of approximately 1,600 companies. Step 2: In this Step, the Sub-Adviser applies its machine learning based model to generate a discounted forecast of next years earnings for each company within the Investment Universe. This discounted earnings forecast is then used as input for Step 3. The model is implemented as a neural network and was developed using standard supervised machine learning techniques. The model was trained to implement an input-output mapping which maps historical company data to future (forecast) company earnings. The model was also trained to estimate the uncertainty in its forecasts and this uncertainty is used to discount the earnings forecast such that the final product of the model in Step 2 is a discounted earnings forecast. The historical company data used as input to the model in Step 2 includes financials such as income and cashflow statements, balance sheet data, short interest, historical stock price change data, industry classification and market size category. The scope and types of input data used in Step 2 may change over time when it is found that additional types of data improve the accuracy of the discounted earnings forecast generated in Step 2. Step 3: All securities in the Investment Universe are then ranked by how inexpensive they are relative to the models discounted forecast earnings. That is, each companys earning yield is calculated by dividing the models discounted forecast earnings by the respective companys total enterprise value. Each company in the Investment Universe is then ranked by the calculated earnings yield such that less expensive companies rank higher than more expensive companies. Step 4: Next, the model is used to identify and screen out potential value-traps. A potential value trap is defined as a company that is considered inexpensive based on current valuation multiples but is likely to have bottom decile price performance over the subsequent year. As in Step 2, the model takes as input historical company data, however, in Step 4 the models output is the probability of a company not being a value-trap. This probability is then multiplied by the earnings yield value from Step 3 and the Investment Universe is re-sorted such that, companies with a high probability of being a value-trap rank lower than companies with a low probability of being a value-trap, all else being equal. This final ranking is then used as input for Step 5. The model is implemented as a neural network and was developed using standard supervised machine learning techniques. The historical company data used as input to the model in Step 4 includes financials such as income and cashflow statements, balance sheet data, short interest, historical stock price change data, industry classification and market size category. The scope and types of input data used in Step 4 may change over time when it is found that additional types of data improve the accuracy of the models ability to predict whether a company is or is not a value-trap. Step 5: With the final ranking from Step 4, the top 60-70 companies are evaluated for potential investment. Prior to purchase, the Sub-Adviser performs a final review to ensure the model is not receiving erroneous data and/or there exists highly relevant negative information about a company that the model does not have access to (e.g., announced bankruptcy filing, company officers being indicted of a financial crime, restatement of financials due to material oversight or misrepresentation and/or loss of a substantial client). In these situations, the Sub-Adviser puts a hold on considering the particular company for investment or removes it from consideration altogether and, then moves to the next highest ranked company for further consideration. This step provides the Sub-Adviser with discretion to override the model. Step 6: With the final list of 60-70 stocks, the Fund will equally weight to those positions at the time of initial investment and intends to rebalance the portfolio on a quarterly basis. The Sub-Adviser expects each portfolio position to represent 1.5 to 2% of the Funds overall portfolio and can grow to a cap of approximately 10%. All stocks in the portfolio are continually monitored, with the portfolio being systematically rebalanced on a quarterly basis. Portfolio changes may occur more frequently when major events such as public health crises, geopolitical events such as war or terrorism, trade disputes, economic shocks, amongst others are believed by the Sub-Adviser to likely have an impact on the Funds portfolio. In addition, in between quarterly rebalances, the Sub-Adviser will monitor the Funds portfolio for certain types of corporate actions that may impact the investment thesis for a company. The Sub-Advisers sell discipline is guided by the same process used to originally screen the Investment Universe. The Sub-Adviser will generally sell an investment if it no longer falls within the top 60 -70 stocks from its Investment Universe based on the results of the models quantitative screening process or if the Sub-Advisers manual stock analysis identifies a catalyst for selling. The Sub-Adviser does not anticipate high portfolio turnover as it seeks to invest in these companies for the long term. While it is anticipated that the Fund will invest across a range of industries, certain sectors may be overweighted compared to others because the Sub-Adviser seeks best investment opportunities regardless of sector. The sectors in which the Fund may be overweighted will vary at different points in the economic cycle. As noted above, the Sub-Adviser utilizes a multi-step process that is based on a quantitative screening process which involves the application of its proprietary models. The qualitative human oversight performed by the Sub-Adviser is a risk mitigation technique that acknowledges the fact that the models only have access to backward-looking quantitative information on company fundamentals and market data. From time-to-time, companies and the media will announce, e.g., through earning calls or SEC filings, relevant information that is not captured in a companys historical financials. Examples include, but are not limited to, an upcoming merger, the need to restate historical financials, or significant future economic headwinds for the company such as a loss of a major source of revenue. Show less