Investment Strategy	Mar. 10, 2026
Pacer S&P 500 3AI Top 100 ETF
Prospectus [Line Items]
Strategy [Heading]	Principal Investment Strategies of the Fund
Strategy Narrative [Text Block]	The Fund employs a “passive management” (or indexing) investment approach designed to track the total return performance, before fees and expenses, of the Index. The Index The Index uses an objective, rules-based methodology to provide exposure to 100 stocks within the S&P 500 Index® (the “S&P 500”) with the highest 3AI Alpha Intelligence Scores. 3AI refers to the machine learning technology firm, 3AI, that provides the 3AI Alpha Intelligence Scores used by the Index Provider in the construction of the Index. 3AI leverages artificial intelligence (“AI”) and machine learning to generate the 3AI Alpha Intelligence Scores. S&P Dow Jones Indices LLC (the “Index Provider”) compiles, maintains and calculates the Index. The Index’s initial universe is derived from the component companies of the S&P 500. The S&P 500 is comprised of common stocks of approximately 500 large-capitalization companies that generally represent the large-cap segment of the U.S. equity market. 3AI Alpha Intelligence Score The 3AI Alpha Intelligence Scores represent the 12-month excess return forecast of a stock relative to a global universe of approximately 20,000 equity securities. This universe includes all stocks with a market capitalization above $50 million. The 3AI Alpha Intelligence Score is determined using 3AI’s proprietary forecasting models through the application of machine learning techniques, by analyzing company data and business-cycle data. 3AI’s Alpha Forecast Models The 3AI Alpha Intelligence Score is generated using 3AI’s proprietary forecasting systems, utilizing an end-to-end machine learning production process that begins with raw data and culminates in the 12-month excess return forecasts. Machine learning is a subset of artificial intelligence (“AI”) that enables the development of models that learn patterns from large datasets, which can subsequently be used to make predictions on new data. 3AI’s models analyze hundreds of proprietary 3AI data signals (also known as factors) sourced from financial statements, analyst forecasts, market data, and macroeconomics. This breadth of data signals enables 3AI’s models to conduct deep due diligence assessment of every stock covered—encompassing fundamentals, technical analysis, future expectation, institutional sentiment, valuation and the U.S. business cycle. Additionally, 3AI’s model incorporates a Bayesian Believability Layer that seeks to enhance forecasting accuracy by continually observing and learning from its own forecasting performance. 3AI seeks to source its data exclusively from reputable mainstream providers, including financial statements and accounting disclosures, corporate earnings reports, market trading and liquidity data, analyst consensus forecasts, corporate actions, macroeconomic indicators, economic think tanks, surveys, and research institutions. Examples of reputable mainstream data providers include regulatory filings of public companies and standardized financial statement databases (e.g., Worldscope); market pricing and trading data from regulated securities exchanges and trading venues; aggregated analyst estimates derived from institutional broker estimate systems; and corporate action information disseminated by issuers and exchanges. 3AI Alpha Intelligence Score Calculation 3AI conducts the following process to calculate a stock’s 3AI Alpha Intelligence Score: 1.Data Signal Generation: Raw data is collected and used to generate data signals (also known as factors). 3AI’s proprietary process transforms the data to make the data robust for machine learning. Imputation algorithms are used to generate missing data inputs where data is insufficient or non-existent to result in a feature library for the AI learning system. 2.Single Stock Alpha Forecast Generation: 3AI generates a Single Stock Alpha forecast for each company using company-level stock data to forecast relative stock performance attributable to company and stock-specific information using advanced deep factor-based AI models. To produce the Single Stock Forecasts, 3AI’s models use hundreds of single stock factors, grouped into the following categories: accounting forensics; forecast, sentiment and surprise; clarity of business model; company physics models; credit risk models; financial change; shareholder treatment; technical indicators; valuation models; and other quantitative approaches (including correlation and beta analysis). 3.Business Cycle Alpha Forecasts Generation: 3AI generates Business Cycle Alpha forecasts by using macroeconomic data to forecast relative sector performance attributable to the Business Cycle using advanced deep factor-based AI models. To produce the Business Cycle Alpha Stock Forecasts, 3AI’s models use proprietary macroeconomic factors from the following categories: U.S. Government bond yields; commodity and market indicators; economic, business and market confidence surveys; economic cycle indicators; manufacturing and supply chain indicators; and inflationary indicators. 4.Final 3AI Alpha Intelligence Score Generation: Single Stock Alpha and Business Cycle Alpha forecasts are combined and passed through a Bayesian Believability Layer to generate final 3AI Alpha Intelligence Scores. The Bayesian Believability Layer continuously monitors and learns from the model’s prior forecasts—identifying the conditions, stock types, and environments where the models’ signals have historically been most effective. This layer serves as an essential safeguard, helping to enhance the accuracy of the 3AI Alpha Intelligence Scores. Model Training 3AI’s model training process involves sequentially training the models using only data available up to a specified historical point and subsequently validating their predictive accuracy on unseen future periods. This method helps ensure genuine out-of-sample validity, prevent look-ahead bias and overfitting, and confirm that forecasts are robust under real-world market conditions. Additionally, 3AI’s dataset includes an extensively deep history of global equities spanning multiple economic cycles, including delisted and failed companies. This breadth enables the models to learn from a full range of outcomes, thereby helping to mitigate survivorship bias and enhance the models’ statistical reliability. Score Validation and Human Oversight 3AI provides ongoing oversight through score validation procedures and human oversight. The score validation process includes monitoring universe stability, performing raw data completeness and accuracy audits of raw data, performing data signal correlation and stability checks against historical norms, performing regular forecast stability verification, and verifying final pre-delivery data completeness. Additionally, 3AI integrates human oversight at multiple levels, including data signal development and refinement; model interpretation through explainability; and ongoing data and model quality monitoring. Index Construction At each quarterly rebalancing, the Index Provider calculates a z-score for each company in the S&P 500 using the raw 3AI Alpha Intelligence Score. A z-score is a way to standardize data by measuring how far a value lies from the mean in units of standard deviation. The z-scores are winsorized at +3 and -3 (i.e., winsorization is applied to z-scores to limit extreme outliers by capping at 3 and flooring at -3), then ranked in descending order. The top 100 ranked companies are selected for inclusion in the Index, subject to the following rules designed to reduce turnover: 1.The Index Provider ranks all eligible securities in the S&P 500 by their 3AI Alpha Intelligence Score. 2.Any current constituent of the Index that is ranked within the top 120 will be eligible for inclusion in the Index. 3.If the target count of 100 securities is not reached after selecting from eligible current constituents of the Index, the Index Provider will select from the eligible universe in descending rank order of the winsorized z-scores until the target count is reached. Index components are weighted based on their winsorized z-scores. The maximum weight of each constituent is capped at 4.5%. The aggregate weight of constituents within each Global Industry Classification Standard (GICS®) sector is capped at 40%. Weight above individual company and sector limitations are typically redistributed among the other Index constituents in proportion to their weights. As of January 30, 2026, the companies included in the Index had a market capitalization of $7.4 billion to $4.6 trillion. Also as of January 30, 2026, the Index had significant exposure to the information technology and consumer discretionary sectors. The Index is typically reconstituted and rebalanced quarterly as of the close of business on the third Friday of March, June, September, and December based on data as of the last business days of February, May, August, and November, respectively. The Fund’s Investment Strategy The Fund attempts to invest all, or substantially all, of its assets in the component securities that make up the Index. The Adviser expects that, over time, the correlation between the Fund’s performance and that of the Index, before fees and expenses, will be 95% or better. The Fund will generally use a “replication” strategy to achieve its investment objective, meaning it will invest in all component securities of the Index in the same approximate proportion as in the Index. The Fund is non-diversified and therefore may invest a larger percentage of its assets in the securities of a single company than diversified funds. To the extent the Index concentrates (i.e., holds more than 25% of its total assets) in the securities of a particular industry or group of related industries, the Fund will concentrate its investments to approximately the same extent as the Index. As of January 30, 2026, the Index was not concentrated in any industry or group of industries.
Strategy Portfolio Concentration [Text]	To the extent the Index concentrates (i.e., holds more than 25% of its total assets) in the securities of a particular industry or group of related industries, the Fund will concentrate its investments to approximately the same extent as the Index. As of January 30, 2026, the Index was not concentrated in any industry or group of industries.
Pacer S&P World 3AI Top 300 ETF
Prospectus [Line Items]
Strategy [Heading]	Principal Investment Strategies of the Fund
Strategy Narrative [Text Block]	The Fund employs a “passive management” (or indexing) investment approach designed to track the total return performance, before fees and expenses, of the Index. The Index The Index uses an objective, rules-based methodology to provide exposure to 300 stocks within the S&P World Index® (the “S&P World Index”) with the highest 3AI Alpha Intelligence Scores. 3AI refers to the machine learning technology firm, 3AI, that provides the 3AI Alpha Intelligence Scores used by the Index Provider in the construction of the Index. 3AI leverages artificial intelligence (“AI”) and machine learning to generate the 3AI Alpha Intelligence Scores. S&P Dow Jones Indices LLC (the “Index Provider”) compiles, maintains and calculates the Index. The Index’s initial universe is derived from the component companies of the S&P World Index. The S&P World Index is comprised of large- and mid-cap stocks from 24 developed markets. 3AI Alpha Intelligence Score The 3AI Alpha Intelligence Scores represent the 12-month excess return forecast of a stock relative to a global universe of approximately 20,000 equity securities. This universe includes all stocks with a market capitalization above $50 million. The 3AI Alpha Intelligence Score is determined using 3AI’s proprietary forecasting models through the application of machine learning techniques, by analyzing company data and business-cycle data. 3AI’s Alpha Forecast Models The 3AI Alpha Intelligence Score is generated using 3AI’s proprietary forecasting systems, utilizing an end-to-end machine learning production process that begins with raw data and culminates in the 12-month excess return forecasts. Machine learning is a subset of artificial intelligence (“AI”) that enables the development of models that learn patterns from large datasets, which can subsequently be used to make predictions on new data. 3AI’s models analyze hundreds of proprietary 3AI data signals (also known as factors) sourced from financial statements, analyst forecasts, market data, and macroeconomics. This breadth of data signals enables 3AI’s models to conduct deep due diligence assessment of every stock covered—encompassing fundamentals, technical analysis, future expectation, institutional sentiment, valuation and the U.S. business cycle. Additionally, 3AI’s model incorporates a Bayesian Believability Layer that seeks to enhance forecasting accuracy by continually observing and learning from its own forecasting performance. 3AI seeks to source its data exclusively from reputable mainstream providers, including financial statements and accounting disclosures, corporate earnings reports, market trading and liquidity data, analyst consensus forecasts, corporate actions, macroeconomic indicators, economic think tanks, surveys, and research institutions. Examples of reputable mainstream data providers include regulatory filings of public companies and standardized financial statement databases (e.g., Worldscope); market pricing and trading data from regulated securities exchanges and trading venues; aggregated analyst estimates derived from institutional broker estimate systems; and corporate action information disseminated by issuers and exchanges. 3AI Alpha Intelligence Score Calculation 3AI conducts the following process to calculate a stock’s 3AI Alpha Intelligence Score: 1.Data Signal Generation: Raw data is collected and used to generate data signals (also known as factors). 3AI’s proprietary process transforms the data to make the data robust for machine learning. Imputation algorithms are used to generate missing data inputs where data is insufficient or non-existent to result in a feature library for the AI learning system. 2.Single Stock Alpha Forecast Generation: 3AI generates a Single Stock Alpha forecast for each company using company-level stock data to forecast relative stock performance attributable to company and stock-specific information using advanced deep factor-based AI models. To produce the Single Stock Forecasts, 3AI’s models use hundreds of single stock factors, grouped into the following categories: accounting forensics; forecast, sentiment and surprise; clarity of business model; company physics models; credit risk models; financial change; shareholder treatment; technical indicators; valuation models; and other quantitative approaches (including correlation and beta analysis). 3.Business Cycle Alpha Forecasts Generation: 3AI generates Business Cycle Alpha forecasts by using macroeconomic data to forecast relative sector performance attributable to the Business Cycle using advanced deep factor-based AI models. To produce the Business Cycle Alpha Stock Forecasts, 3AI’s models use proprietary macroeconomic factors from the following categories: U.S. Government bond yields; commodity and market indicators; economic, business and market confidence surveys; economic cycle indicators; manufacturing and supply chain indicators; and inflationary indicators. 4.Final 3AI Alpha Intelligence Score Generation: Single Stock Alpha and Business Cycle Alpha forecasts are combined and passed through a Bayesian Believability Layer to generate final 3AI Alpha Intelligence Scores. The Bayesian Believability Layer continuously monitors and learns from the model’s prior forecasts—identifying the conditions, stock types, and environments where the models’ signals have historically been most effective. This layer serves as an essential safeguard, helping to enhance the accuracy of the 3AI Alpha Intelligence Scores. Model Training 3AI’s model training process involves sequentially training the models using only data available up to a specified historical point and subsequently validating their predictive accuracy on unseen future periods. This method helps ensure genuine out-of-sample validity, prevent look-ahead bias and overfitting, and confirm that forecasts are robust under real-world market conditions. Additionally, 3AI’s dataset includes an extensively deep history of global equities spanning multiple economic cycles, including delisted and failed companies. This breadth enables the models to learn from a full range of outcomes, thereby helping to mitigate survivorship bias and enhance the models’ statistical reliability. Score Validation and Human Oversight 3AI provides ongoing oversight through score validation procedures and human oversight. The score validation process includes monitoring universe stability, performing raw data completeness and accuracy audits of raw data, performing data signal correlation and stability checks against historical norms, performing regular forecast stability verification, and verifying final pre-delivery data completeness. Additionally, 3AI integrates human oversight at multiple levels, including data signal development and refinement; model interpretation through explainability; and ongoing data and model quality monitoring. Index Construction At each quarterly rebalancing, the Index Provider calculates a z-score for each company in the S&P World Index using the raw 3AI Alpha Intelligence Score. A z-score is a way to standardize data by measuring how far a value lies from the mean in units of standard deviation. The z-scores are winsorized at +3 and -3 (i.e., winsorization is applied to z-scores to limit extreme outliers by capping at 3 and flooring at -3), then ranked in descending order. The top 300 ranked companies are selected for inclusion in the Index, subject to the following rules designed to reduce turnover: 1.The Index Provider ranks all eligible securities in the S&P World Index by their 3AI Alpha Intelligence Score. 2.Any current constituent of the Index that is ranked within the top 360 will be eligible for inclusion in the Index. 3.If the target count of 300 securities is not reached after selecting from eligible current constituents of the Index, the Index Provider will select from the eligible universe in descending rank order of the winsorized z-scores until the target count is reached. Index components are weighted based on their winsorized z-scores. The maximum weight of each constituent is capped at 4.5%. The aggregate weight of constituents within each Global Industry Classification Standard (GICS®) sector is capped at 40%. Exposure to the United States is capped at the maximum of 60% and the weight of the United States in the S&P World Index. Exposure for every other country is capped at the weight of such country in the S&P World Index plus 10%. Weight above individual company, sector, and country limitations are typically redistributed among the other Index constituents in proportion to their weights. As of January 30, 2026, the companies included in the Index had a market capitalization of $2.7 billion to $4.6 trillion. Also as of January 30, 2026, the Index had significant exposure to the information technology sector. The Index is typically reconstituted and rebalanced quarterly as of the close of business on the third Friday of March, June, September, and December based on data as of the last business days of February, May, August, and November, respectively. The Fund’s Investment Strategy The Fund attempts to invest all, or substantially all, of its assets in the component securities that make up the Index. The Adviser expects that, over time, the correlation between the Fund’s performance and that of the Index, before fees and expenses, will be 95% or better. The Fund will generally use a “replication” strategy to achieve its investment objective, meaning it will invest in all component securities of the Index in the same approximate proportion as in the Index. The Fund is non-diversified and therefore may invest a larger percentage of its assets in the securities of a single company than diversified funds. To the extent the Index concentrates (i.e., holds more than 25% of its total assets) in the securities of a particular industry or group of related industries, the Fund will concentrate its investments to approximately the same extent as the Index. As of January 30, 2026, the Index was not concentrated in any industry or group of industries.
Strategy Portfolio Concentration [Text]	To the extent the Index concentrates (i.e., holds more than 25% of its total assets) in the securities of a particular industry or group of related industries, the Fund will concentrate its investments to approximately the same extent as the Index. As of January 30, 2026, the Index was not concentrated in any industry or group of industries.

Investment Strategy

The Fund employs a “passive management” (or indexing) investment approach designed to track the total return performance, before fees and expenses, of the Index.

The Index uses an objective, rules-based methodology to provide exposure to 100 stocks within the S&P 500 Index® (the “S&P 500”) with the highest 3AI Alpha Intelligence Scores. 3AI refers to the machine learning technology firm, 3AI, that provides the 3AI Alpha Intelligence Scores used by the Index Provider in the construction of the Index. 3AI leverages artificial intelligence (“AI”) and machine learning to generate the 3AI Alpha Intelligence Scores. S&P Dow Jones Indices LLC (the “Index Provider”) compiles, maintains and calculates the Index.

The Index’s initial universe is derived from the component companies of the S&P 500. The S&P 500 is comprised of common stocks of approximately 500 large-capitalization companies that generally represent the large-cap segment of the U.S. equity market.

3AI Alpha Intelligence Score

The 3AI Alpha Intelligence Scores represent the 12-month excess return forecast of a stock relative to a global universe of approximately 20,000 equity securities. This universe includes all stocks with a market capitalization above $50 million. The 3AI Alpha Intelligence Score is determined using 3AI’s proprietary forecasting models through the application of machine learning techniques, by analyzing company data and business-cycle data.

3AI’s Alpha Forecast Models

The 3AI Alpha Intelligence Score is generated using 3AI’s proprietary forecasting systems, utilizing an end-to-end machine learning production process that begins with raw data and culminates in the 12-month excess return forecasts. Machine learning is a subset of artificial intelligence (“AI”) that enables the development of models that learn patterns from large datasets, which can subsequently be used to make predictions on new data.

3AI’s models analyze hundreds of proprietary 3AI data signals (also known as factors) sourced from financial statements, analyst forecasts, market data, and macroeconomics. This breadth of data signals enables 3AI’s models to conduct deep due diligence assessment of every stock covered—encompassing fundamentals, technical analysis, future expectation, institutional sentiment, valuation and the U.S. business cycle.

Additionally, 3AI’s model incorporates a Bayesian Believability Layer that seeks to enhance forecasting accuracy by continually observing and learning from its own forecasting performance.

3AI seeks to source its data exclusively from reputable mainstream providers, including financial statements and accounting disclosures, corporate earnings reports, market trading and liquidity data, analyst consensus forecasts, corporate actions, macroeconomic indicators, economic think tanks, surveys, and research institutions. Examples of reputable mainstream data providers include regulatory filings of public companies and standardized financial statement databases (e.g., Worldscope); market pricing and trading data from regulated securities exchanges and trading venues; aggregated analyst estimates derived from institutional broker estimate systems; and corporate action information disseminated by issuers and exchanges.

3AI Alpha Intelligence Score Calculation

3AI conducts the following process to calculate a stock’s 3AI Alpha Intelligence Score:

1.Data Signal Generation: Raw data is collected and used to generate data signals (also known as factors). 3AI’s proprietary process transforms the data to make the data robust for machine learning. Imputation algorithms are used to generate missing data inputs where data is insufficient or non-existent to result in a feature library for the AI learning system.

2.Single Stock Alpha Forecast Generation: 3AI generates a Single Stock Alpha forecast for each company using company-level stock data to forecast relative stock performance attributable to company and stock-specific information using advanced deep factor-based AI models.

To produce the Single Stock Forecasts, 3AI’s models use hundreds of single stock factors, grouped into the following categories: accounting forensics; forecast, sentiment and surprise; clarity of business model; company physics models; credit risk models; financial change; shareholder treatment; technical indicators; valuation models; and other quantitative approaches (including correlation and beta analysis).

3.Business Cycle Alpha Forecasts Generation: 3AI generates Business Cycle Alpha forecasts by using macroeconomic data to forecast relative sector performance attributable to the Business Cycle using advanced deep factor-based AI models.

To produce the Business Cycle Alpha Stock Forecasts, 3AI’s models use proprietary macroeconomic factors from the following categories: U.S. Government bond yields; commodity and market indicators; economic, business and market confidence surveys; economic cycle indicators; manufacturing and supply chain indicators; and inflationary indicators.

4.Final 3AI Alpha Intelligence Score Generation: Single Stock Alpha and Business Cycle Alpha forecasts are combined and passed through a Bayesian Believability Layer to generate final 3AI Alpha Intelligence Scores.

The Bayesian Believability Layer continuously monitors and learns from the model’s prior forecasts—identifying the conditions, stock types, and environments where the models’ signals have historically been most effective. This layer serves as an essential safeguard, helping to enhance the accuracy of the 3AI Alpha Intelligence Scores.

3AI’s model training process involves sequentially training the models using only data available up to a specified historical point and subsequently validating their predictive accuracy on unseen future periods. This method helps ensure genuine out-of-sample validity, prevent look-ahead bias and overfitting, and confirm that forecasts are robust under real-world market conditions.

Additionally, 3AI’s dataset includes an extensively deep history of global equities spanning multiple economic cycles, including delisted and failed companies. This breadth enables the models to learn from a full range of outcomes, thereby helping to mitigate survivorship bias and enhance the models’ statistical reliability.

Score Validation and Human Oversight

3AI provides ongoing oversight through score validation procedures and human oversight. The score validation process includes monitoring universe stability, performing raw data completeness and accuracy audits of raw data, performing data signal correlation and stability checks against historical norms, performing regular forecast stability verification, and verifying final pre-delivery data completeness.

Additionally, 3AI integrates human oversight at multiple levels, including data signal development and refinement; model interpretation through explainability; and ongoing data and model quality monitoring.

At each quarterly rebalancing, the Index Provider calculates a z-score for each company in the S&P 500 using the raw 3AI Alpha Intelligence Score. A z-score is a way to standardize data by measuring how far a value lies from the mean in units of standard deviation. The z-scores are winsorized at +3 and -3 (i.e., winsorization is applied to z-scores to limit extreme outliers by capping at 3 and flooring at -3), then ranked in descending order. The top 100 ranked companies are selected for inclusion in the Index, subject to the following rules designed to reduce turnover:

1.The Index Provider ranks all eligible securities in the S&P 500 by their 3AI Alpha Intelligence Score.

2.Any current constituent of the Index that is ranked within the top 120 will be eligible for inclusion in the Index.

3.If the target count of 100 securities is not reached after selecting from eligible current constituents of the Index, the Index Provider will select from the eligible universe in descending rank order of the winsorized z-scores until the target count is reached.

Index components are weighted based on their winsorized z-scores. The maximum weight of each constituent is capped at 4.5%. The aggregate weight of constituents within each Global Industry Classification Standard (GICS®) sector is capped at 40%. Weight above individual company and sector limitations are typically redistributed among the other Index constituents in proportion to their weights.

As of January 30, 2026, the companies included in the Index had a market capitalization of $7.4 billion to $4.6 trillion. Also as of January 30, 2026, the Index had significant exposure to the information technology and consumer discretionary sectors.

The Index is typically reconstituted and rebalanced quarterly as of the close of business on the third Friday of March, June, September, and December based on data as of the last business days of February, May, August, and November, respectively.

The Fund’s Investment Strategy

The Fund attempts to invest all, or substantially all, of its assets in the component securities that make up the Index. The Adviser expects that, over time, the correlation between the Fund’s performance and that of the Index, before fees and expenses, will be 95% or better.

The Fund will generally use a “replication” strategy to achieve its investment objective, meaning it will invest in all component securities of the Index in the same approximate proportion as in the Index.

The Fund is non-diversified and therefore may invest a larger percentage of its assets in the securities of a single company than diversified funds.

To the extent the Index concentrates (i.e., holds more than 25% of its total assets) in the securities of a particular industry or group of related industries, the Fund will concentrate its investments to approximately the same extent as the Index. As of January 30, 2026, the Index was not concentrated in any industry or group of industries.

The Fund employs a “passive management” (or indexing) investment approach designed to track the total return performance, before fees and expenses, of the Index.

The Index uses an objective, rules-based methodology to provide exposure to 300 stocks within the S&P World Index® (the “S&P World Index”) with the highest 3AI Alpha Intelligence Scores. 3AI refers to the machine learning technology firm, 3AI, that provides the 3AI Alpha Intelligence Scores used by the Index Provider in the construction of the Index. 3AI leverages artificial intelligence (“AI”) and machine learning to generate the 3AI Alpha Intelligence Scores. S&P Dow Jones Indices LLC (the “Index Provider”) compiles, maintains and calculates the Index.

The Index’s initial universe is derived from the component companies of the S&P World Index. The S&P World Index is comprised of large- and mid-cap stocks from 24 developed markets.

3AI Alpha Intelligence Score

3AI’s Alpha Forecast Models

artificial intelligence (“AI”) that enables the development of models that learn patterns from large datasets, which can subsequently be used to make predictions on new data.

Additionally, 3AI’s model incorporates a Bayesian Believability Layer that seeks to enhance forecasting accuracy by continually observing and learning from its own forecasting performance.

3AI Alpha Intelligence Score Calculation

3AI conducts the following process to calculate a stock’s 3AI Alpha Intelligence Score:

Score Validation and Human Oversight

At each quarterly rebalancing, the Index Provider calculates a z-score for each company in the S&P World Index using the raw 3AI Alpha Intelligence Score. A z-score is a way to standardize data by measuring how far a value lies from the mean in units of standard deviation. The z-scores are winsorized at +3 and -3 (i.e., winsorization is applied to z-scores to limit extreme outliers by capping at 3 and flooring at -3), then ranked in descending order. The top 300 ranked companies are selected for inclusion in the Index, subject to the following rules designed to reduce turnover:

1.The Index Provider ranks all eligible securities in the S&P World Index by their 3AI Alpha Intelligence Score.

2.Any current constituent of the Index that is ranked within the top 360 will be eligible for inclusion in the Index.

3.If the target count of 300 securities is not reached after selecting from eligible current constituents of the Index, the Index Provider will select from the eligible universe in descending rank order of the winsorized z-scores until the target count is reached.

Index components are weighted based on their winsorized z-scores. The maximum weight of each constituent is capped at 4.5%. The aggregate weight of constituents within each Global Industry Classification Standard (GICS®) sector is capped at 40%. Exposure to the United States is capped at the maximum of 60% and the weight of the United States in the S&P World Index. Exposure for every other country is capped at the weight of such country in the S&P World Index plus 10%. Weight above individual company, sector, and country limitations are typically redistributed among the other Index constituents in proportion to their weights.

As of January 30, 2026, the companies included in the Index had a market capitalization of $2.7 billion to $4.6 trillion. Also as of January 30, 2026, the Index had significant exposure to the information technology sector.

The Fund’s Investment Strategy

The Fund is non-diversified and therefore may invest a larger percentage of its assets in the securities of a single company than diversified funds.