瑞士百达资管雷德玮：AI驱动量化投资进入2.0时代

Core Viewpoint - The rise of AI-driven quantitative strategies is transforming investment approaches, allowing for the identification of complex relationships in data that traditional methods cannot capture [1][4]. Group 1: AI Quantitative Strategies - AI quantitative models can analyze hundreds of high-frequency signals, uncovering non-linear relationships in data, which enhances predictive accuracy compared to traditional models that rely on a limited number of factors [1][7]. - The AI quantitative strategy developed by Swiss Bank Asset Management focuses on around 400 high-frequency signals, contrasting with the typical 20 signals used in traditional quantitative strategies [7]. - The AI model's ability to learn complex relationships allows it to improve the prediction of stock price movements based on analyst ratings and other signals [6][8]. Group 2: Market Expansion and Interest - Global capital interest in the Chinese market is on the rise, with plans to include A-shares in AI quantitative strategies as they expand into emerging markets [4][5]. - The firm is currently exploring the potential of AI-driven strategies for domestic investors in China, contingent on obtaining additional QDLP quotas [5][6]. Group 3: Investment Strategy and Risk Management - The investment horizon for Swiss Bank Asset Management's AI strategies is approximately 20 days, differing from many competitors that focus on ultra-short holding periods [8]. - To mitigate overfitting risks, the firm employs methods such as using economically sound signals, integrating numerous simple models, and utilizing extensive historical data for training [8]. Group 4: Role of Fund Managers - The role of fund managers is evolving with the integration of AI, shifting from model building to training AI models and validating their outputs while still conducting factor research [8].