Interview with David Wu: How GaoYing Quant is Reshaping Quantitative Investment with AI

As global financial markets evolve, competition in quantitative investment intensifies, requiring investors to continuously adapt to new environments. GaoYing Quant's performance in this field is noteworthy, particularly its second fund, which won the Relative Value Strategy Award at the 2026 HFM Asia Pacific Hedge Fund Performance Awards, highlighting its leading position in quantitative investment. To gain deeper insights into GaoYing Quant's future strategies and how it leverages AI technology to tackle market challenges, the Techritual editorial team interviewed David Wu, Chairman of GaoYing Quant.

Long-term Vision of GaoYing Quant

David Wu stated that GaoYing Quant's long-term vision is to establish an AI-native quantitative asset management institution based in Hong Kong and serving globally, with the core philosophy of "Technology Empowering Long-term Asset Appreciation." He emphasized that this vision aims to move away from traditional quantitative methods that rely on human experience and static factors, establishing a modern quantitative investment ecosystem characterized by full-chain intelligence, internationalization, and high compliance standards.

He pointed out that the company's overall strategy will focus on three core directions: first, adhering to full-stack technology self-research and continuously iterating the AI end-to-end intelligent investment research and trading framework; second, deepening its positioning as an international financial hub in Hong Kong and expanding its global multi-market layout; and finally, ensuring extreme intelligent risk control as a baseline, balancing technological innovation with capital safety, and providing high-resilience, sustainable quantitative allocation solutions for institutions and high-net-worth investors.

Application Scenarios of AI Technology

Regarding the specific applications of AI in strategy research and quantitative trading, David Wu emphasized that GaoYing Quant has taken the lead in completing the paradigm shift from "manual factor mining" to "large model intelligent decision-making," achieving a dominant position for AI throughout the investment research and trading process. He introduced several core application scenarios:

• Global Data Intelligent Extraction: Utilizing a self-built AI data center to integrate multidimensional data, enhancing the breadth and accuracy of information capture.
• Autonomous Strategy Mining and Evolution: AI can autonomously explore latent effective market factors without human intervention and conduct real-time market reviews.
• Full-chain Intelligent Trading Decision-making: Building a self-researched AI trading framework to achieve fully automated closed-loop solutions, addressing traditional quantitative pain points.
• Global Dynamic Intelligent Risk Control: AI models monitor market fluctuations in real-time, achieving proactive, real-time, and intelligent risk control.

Strategies for Market Volatility

In the current uncertain global market, David Wu pointed out that GaoYing Quant relies on AI's adaptive, self-iterative, and forward-looking capabilities to maintain stable performance. He explained that AI's real-time dynamic understanding of the market allows for rapid identification of dominant trading logic and automatic adjustments of factor weights, strategy styles, and trading frequencies, thereby avoiding disconnection between strategies and the market.

Additionally, David Wu emphasized that the reinforcement learning self-iteration capability enables AI models to autonomously learn the profit and hedging logic of various market scenarios, enhancing survival and profitability in complex markets.

Technical Advantages of GaoYing Quant's Second Fund

David Wu further introduced the technical advantages of GaoYing Quant's second fund, which serves as the core flagship product of the company's technology implementation. He noted that the underlying support for this fund is an AI-driven computing platform. He explained that the platform operates on a five-layer closed-loop architecture that includes data input, AI intelligent decision-making, low-latency execution, real-time risk control, and iterative review, providing GaoYing Quant with a unique competitive advantage in the industry.

He emphasized that the core competitive advantage of this platform lies in its fully self-researched, full-stack capabilities, with no reliance on external technologies, and its high adaptability to market conditions, enhancing execution efficiency and accuracy.

Future Development Directions

Looking ahead, David Wu stated that GaoYing Quant will continue to focus on four major directions: intelligence, proactivity, globalization, and refinement, iterating a new risk control system. He mentioned plans to upgrade the AI extreme market stress testing system, establish a full-chain real-time intelligent risk control hub, and develop liquidity intelligent prediction models to address extreme market conditions.

He concluded by emphasizing that GaoYing Quant will continue to empower risk control through technology, upgrading from "passively responding to risks" to "actively preventing and intelligently mitigating risks."