Machine Intelligence Opportunistic U.S. Equity

Principal Risk

The strategy employs a quantitative model to execute the strategy. Data imprecision, software or other technology malfunctions, programming inaccuracies and similar circumstances may impair the performance of these systems, which may negatively affect performance. Furthermore, there can be no assurance that the quantitative models used in managing the strategy will perform as anticipated or enable the strategy to achieve its objective.

The principal risks are generally those attributable to stock investing. Holdings are subject to market, issuer, and other risks, and their values may fluctuate. Market risk is the risk that securities may decline in value due to factors affecting the securities markets or particular industries. Issuer risk is the risk that the value of a security may decline for reasons specific to the issuer, such as changes in its financial condition. More particularly, the Strategy invests in mid to smaller companies, which may be more susceptible to price swings than larger companies because they have fewer resources and more limited products, and many are dependent on a few key managers. The manager's investment style may become out of favor and/or the manager's selection process may prove incorrect, which may have a negative impact on the Strategy performance. The Strategy invests primarily in U.S equity securities—regardless of capitalization. The Strategy uses a concentrated and high turnover investment approach, and generally seeks to invest in what the firm believes are high-quality growth-oriented companies trading at discounts to the manager's assessment of their intrinsic value. The Strategy has no appropriate benchmark for the composite because the Strategy has minimal exposure to a number of sectors and invests across the market capitalization spectrum. Investment Model: A manager's proprietary model may not adequately allow for existing or unforeseen market factors or the interplay between such factors, and even a model that performs in accordance with the manager’s intentions may underperform other investment strategies or result in greater losses than other strategies. The proprietary models used by a manager to evaluate securities or securities markets are based on the manager's understanding of the interplay of market factors and do not assure successful investment. The markets, or the prices of individual securities, may be affected by factors not foreseen in developing the models. Strategies that are actively managed, in whole or in part, according to a quantitative investment model, including models using artificial intelligence to select securities, can perform differently from the market as a whole based on the investment model and the factors used in the analysis, the weight placed on each factor, and changes from the factors' historical trends. Mistakes in the construction and implementation of the investment models (including, for example, data problems and/or software issues) may create errors or limitations that might go undetected or are discovered only after the errors or limitations have negatively impacted performance. There is no guarantee that the use of these investment models will result in effective investment decisions for the Strategy.

Artificial intelligence (AI) including natural language processing, machine learning, and other forms of AI may pose inherent risks, including but not limited to: issues with data privacy, intellectual property, consumer protection, and anti-discrimination laws; ethics and transparency concerns; information security issues; the potential for unfair bias and discrimination; quality and accuracy of inputs and outputs; technical failures and potential misuse. Reliance on information produced using AI-based technology and tools should factor in these risks.