Woori Asset Management Launches 'WON Physical AI TOP2 Plus Active ETF'

On August 4, Woori Asset Management announced the launch of the 'WON Physical AI TOP2 Plus Active' Exchange Traded Fund (ETF), which invests in Samsung Electronics, Hyundai Motor, and key companies in the physical artificial intelligence (AI) value chain, aiming to lead in the era of physical AI.

The WON Physical AI TOP2 Plus Active ETF focuses its investments on two key companies: it allocates an equal 25% weight each to Samsung Electronics, which serves as the “brain” of physical AI, and Hyundai Motor, which acts as the “body,” for a total of 50% of the portfolio. Samsung Electronics is establishing an intelligent platform for the robotics industry based on its AI and semiconductor capabilities, while Hyundai Motor, which owns humanoid robotics company Boston Dynamics, is recognized as a central figure in the physical AI ecosystem.

The remaining portfolio consists of companies from four core value chains that make up the physical AI ecosystem: robotics, core components, intelligence, and energy. Key holdings strategically include Robotis (robotics), Hyundai Mobis (core components), SK hynix and Samsung Electro-Mechanics (intelligence), and HD Hyundai Electric, Hyosung Heavy Industries, and LS ELECTRIC (energy). The portfolio also includes companies with stable earnings from existing AI, semiconductor, and power businesses to help buffer the volatility of early-stage robotics companies’ stock prices. The ETF tracks the FnGuide Physical AI TOP2 Plus Index and carries a total expense ratio of 0.49% per annum.

 

An active management strategy that enables flexible response to changing market conditions is also a key feature. Junggyu Yoo, Manager of Equity Investment Team 1 at Woori Asset Management, stated, "The robotics industry is in the early stages, with growth rates differing by value chain—such as AI semiconductors, actuators, reducers, and sensors," adding, "Rather than simply tracking a single index, actively adjusting the holdings and weights based on technological competence, order intake, and mass production potential is a more effective management approach."

This content was produced with the assistance of AI translation services.