The Fund seeks to track the performance, before fees and expenses, of the Index. The Index, developed by Wilshire Indexes (the “Index Provider”) in partnership with the Adviser, tracks the performance of stocks of large capitalization companies domiciled in the United States. As of September 30, 2025, the companies included in the Index had a market capitalization ranging from $13.2 billion to $4.5 trillion. The Index include common stocks and real estate investment trusts (“REITs”). The Index Provider designs, maintains, and calculates the Index using a transparent, ruled-based methodology. This methodology incorporates return forecasts from quantitative machine learning models developed by the Adviser, guiding stock selection and weighting within the Index.

The Index construction process begins with the universe of companies that are current members of the FT Wilshire US Large Cap Index (the “Underlying Benchmark”). The Underlying Benchmark is a float-adjusted, market capitalization-weighted index that tracks the performance of large-capitalization companies domiciled in the United States. With 433

constituents as of June 30, 2025, the Underlying Benchmark covers up to 85% of the U.S. market capitalization.

The construction of the Index is then divided into three steps: (1) risk-adjusted returns estimation via machine learning; (2) covariance matrix estimation and (3) mean-tracking error optimization, as described in greater detail below.

1. Risk-Adjusted Returns Estimation via Machine Learning

The first step of the Index construction process involves forecasting the expected risk-adjusted return of each component of the Underlying Benchmark using machine learning models. These models analyze over 100 stock-level market and fundamental characteristics from well-established academic research, drawn from 12 different categories such as default risk, growth, momentum, productivity, profitability, size and value. The models are based on historical data and aim to identify patterns that help predict which stocks that comprise the Underlying Benchmark are likely to outperform.

2. Covariance Matrix Estimation

The second step of the Index construction process utilizes a covariance matrix to estimate risk and correlations between stocks of the Underlying Benchmark. The covariance matrix uses a statistical factor model to identify how the daily returns of stocks comprising the Underlying Benchmark move in relation to each other, and allows the models to understand how combining different stocks will affect the total risk of the portfolio.

3. Mean-Tracking Error Optimization

In the final step of the Index construction process, a mean-tracking error optimization is performed using the expected returns determined in Step 1 above and the covariance matrix estimated in Step 2. This mathematical process selects stock weights that aim to maximize return while minimizing tracking error from the Underlying Benchmark in terms of risk and exposure. The optimization imposes certain constraints to ensure that the Index remains aligned with the Underlying Benchmark, including industry exposure limits and individual stock weight caps. Stocks with trivial target weights, as defined in the Index methodology, are removed from the Index. The optimization process is repeated periodically to adapt to changing market conditions.

The Index is reconstituted on a quarterly basis. A constituent will be removed from the Index if it is removed from the Underlying Benchmark.

The Adviser employs a passive indexing investment approach. Under normal circumstances, the Fund invests at least 80% of its net assets, plus any borrowings for investment purposes, in component securities of the Index. This investment policy may be changed by the Fund upon 60 days’ prior written notice to shareholders. The Fund generally uses a “replication” strategy to achieve its investment objective, meaning that it will invest in all of the securities included in the Index. The Fund may, however, use a representative sampling approach to achieve its investment objective when the Adviser believes it is in the best interest of the Fund. For example, among other reasons, the Fund may use a representative sampling approach when there are practical difficulties or substantial costs involved in replicating the Index or when an Index constituent becomes temporarily illiquid, unavailable or less liquid. When the Fund uses a representative sampling approach, the Fund may invest in a subset, or “sample,” of the securities included in the Index and whose risk, return and performance characteristics generally match the risk, return and performance characteristics of the Index as a whole. The Fund may also invest in total return swaps that are not components of the Index that the Adviser believes will help the Fund track the Index.

The Fund may concentrate its investments (i.e., invest more than 25% of its total assets) in a particular industry or group of industries to approximately the same extent that the Index concentrates in an industry or group of industries. As of September 30, 2025, the Index was not concentrated in any particular industry. In addition, in replicating the Index, the Fund may from time to time invest a significant portion of its assets in the securities of companies in one or more sectors. As of September 30, 2025, a significant portion of the Index consisted of companies in the Digital Information and Services sector and the Technology sector, as each such sector is defined by the Wilshire Global Assets Taxonomy System (“GATS”), as set forth in the Global Assets Taxonomy System Principles and Methodology (June 2025) published by Wilshire Indexes. The sectors in which the Index components, and thus the Fund’s investments, may be focused will vary as the composition of the Index changes over time.