Key Takeaways
- • CPAI traded at $51.01 as of July 2, 2026, with a YTD return of 27.21% and a near-zero premium/discount to NAV ($51.00), indicating efficient pricing and strong year-to-date performance.
- • The fund employs a proprietary quantitative framework combining 30+ empirical variables across value, momentum, reversal, profitability, sentiment, and price stability themes to dynamically rank and select equity positions.
- • Dynamic machine learning models adjust factor exposure in real-time based on market conditions, differentiating CPAI from static factor-based strategies and positioning it for adaptive outperformance relative to benchmarks.
- • 52-week range spans $35.06 to $52.13, with volatility of 1.78%, reflecting moderate drawdown risk and relatively stable execution within the quantitative equity space.
- • Third-party price targets average $60.05 with a range of $46.85 to $75.68, suggesting 18.60% upside from July 2 pricing, though forecasts reflect significant analyst uncertainty spanning 62% of the spread.
Summary
Counterpoint Quantitative Equity ETF (CPAI) is a systematic, AI-driven equity fund that seeks long-term capital appreciation through dynamic factor-based stock selection. As of July 2, 2026, the ETF traded at $51.01 per share with a year-to-date return of 27.21%, outpacing broad equity benchmarks and demonstrating the efficacy of its quantitative methodology during the first half of 2026. The fund's NAV stood at $51.00, with negligible premium/discount of $0.01, indicating tight tracking and efficient market pricing typical of well-managed ETF structures.
The fund's core differentiator lies in its proprietary quantitative research infrastructure. Counterpoint's investment team has distilled empirical analysis into a 30+ variable framework spanning established factor themes: value, long-term reversal, momentum, profitability, investor sentiment, and price stability. Rather than applying static factor weights, the strategy employs multiple machine learning models that dynamically adjust exposure across these dimensions in response to shifting market regimes. This adaptive approach represents a departure from traditional factor-based investing, where exposures remain relatively fixed regardless of market conditions.
The portfolio construction process leverages artificial intelligence algorithms to rank individual stocks and determine optimal inclusion and weighting. The methodology prioritizes stocks exhibiting multi-factor exposure that rank highest within Counterpoint's quantitative scoring systems. Management notes that the investment team combines published academic research with proprietary insights to inform model development and refinement.
Risk metrics reveal moderate volatility at 1.78% annualized, with a 52-week range of $35.06 to $52.13. The fund experienced a 45.49% gain from its 52-week low, though this recovery masks significant intra-period volatility during market stress. Third-party analyst price targets average $60.05, with a high forecast of $75.68 and a low of $46.85, implying 18.60% upside from July 2 pricing. However, the wide forecast spread—approximately 62% between high and low estimates—reflects substantial uncertainty regarding sustained outperformance and the robustness of the quantitative models through different market cycles.
The fund's core differentiator lies in its proprietary quantitative research infrastructure. Counterpoint's investment team has distilled empirical analysis into a 30+ variable framework spanning established factor themes: value, long-term reversal, momentum, profitability, investor sentiment, and price stability. Rather than applying static factor weights, the strategy employs multiple machine learning models that dynamically adjust exposure across these dimensions in response to shifting market regimes. This adaptive approach represents a departure from traditional factor-based investing, where exposures remain relatively fixed regardless of market conditions.
The portfolio construction process leverages artificial intelligence algorithms to rank individual stocks and determine optimal inclusion and weighting. The methodology prioritizes stocks exhibiting multi-factor exposure that rank highest within Counterpoint's quantitative scoring systems. Management notes that the investment team combines published academic research with proprietary insights to inform model development and refinement.
Risk metrics reveal moderate volatility at 1.78% annualized, with a 52-week range of $35.06 to $52.13. The fund experienced a 45.49% gain from its 52-week low, though this recovery masks significant intra-period volatility during market stress. Third-party analyst price targets average $60.05, with a high forecast of $75.68 and a low of $46.85, implying 18.60% upside from July 2 pricing. However, the wide forecast spread—approximately 62% between high and low estimates—reflects substantial uncertainty regarding sustained outperformance and the robustness of the quantitative models through different market cycles.
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