Counterpoint Funds has introduced the Counterpoint Quantitative Equity ETF (NYSE Arca: CPAI) aimed at achieving long-term capital growth through investments in individual stocks with exposure to various factors. These selected stocks, identified through quantitative models and artificial intelligence (AI), rank high in desirability.
Joseph Engelberg, Counterpoint’s Chief Research Officer, highlighted that CPAI utilizes AI to adapt factor exposure dynamically in response to market fluctuations. Unlike traditional investment approaches, the fund is not tied to a fixed single or multifactor strategy, allowing it to navigate diverse market conditions effectively.
The fund, managed actively, employs multiple machine learning models in the stock selection process. By adjusting factor exposure dynamically, it aims to outperform the market, resulting in a portfolio typically consisting of around 50 stocks. While CPAI encompasses stocks across various market capitalizations, it places particular emphasis on small- and midcap equities.
Counterpoint Funds, known for its defensive, systematic, and research-oriented investment strategies, manages assets exceeding $1.5 billion. The firm specializes in defensive fixed income and equity diversifier strategies tailored to enhance long-term portfolio performance.
Rise of AI-Driven ETFs
CPAI is part of the expanding landscape of ETFs leveraging AI in portfolio construction. The pioneering AI Powered Equity ETF (AIEQ) fully integrates AI for stock selection. Other AI-powered broad equity ETFs include the BTD Capital Fund (DIP), AdvisorShares Let Bob AI Powered Momentum ETF (LETB), and WisdomTree International AI Enhanced Value Fund (AIVI).
Recently, Qraft Technologies and LG AI Research collaborated to launch the LG Qraft AI-Powered U.S. Large-Cap Core ETF (LQAI), focusing on U.S. large-cap core stocks through AI-driven strategies.
Todd Rosenbluth, Head of Research at VettaFi, noted that AI-driven funds eliminate human biases in stock selection, offering a more dynamic approach compared to rule-based smart beta strategies. He emphasized that AI-based ETFs can operate continuously, adapting and learning at a pace beyond human capabilities.
