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].

雷德玮（David Wright），现任瑞士百达资产管理量化投资主管，负责Quest（量化股票和解决方案）以 及指数股票业务。他于2022年加入瑞士百达资产管理。此前，他曾担任量化业务策略Quest联席主管。 在加入瑞士百达之前，雷德玮在贝莱德（2009年之前为巴克莱全球投资）工作了22年，后来担任系统化 主动股票（SAE）业务的欧洲、中东和非洲地区（EMEA）产品策略主管以及SAE ESG产品的首席产品 策略师。 "当一名分析师上调股票评级时，是否真正预示股价即将上涨？对此，AI量化模型可以更精准地判 断。"瑞士百达资管量化投资主管雷德玮近期在接受中国证券报记者专访时表示，AI通过算力提升和开 源工具普及，正在推动量化投资进入2.0时代。传统量化局限于价值、动量等少量因子进行分析，AI量 化可以识别数百个高频信号，挖掘数据中的非线性关系。 瑞士百达资管为瑞士百达集团旗下的机构资产管理和基金管理公司，瑞士百达集团拥有220年历史，截 至2025年6月30日，集团资产管理规模达7110亿瑞士法郎。目前，雷德玮带领的瑞士百达资管量化股票 及解决方案投资团队的管理规模为250亿美元，旗下AI量化策略产品已在发达市场 ...

Core Insights - The article discusses the advancements in AI-driven quantitative investment strategies led by David Wright at Swiss Bank Asset Management, highlighting the transition to a 2.0 era of quantitative investing through enhanced computational power and open-source tools [1][2]. Group 1: AI Quantitative Strategies - AI quantitative models can identify hundreds of high-frequency signals and uncover non-linear relationships in data, surpassing traditional quantitative methods that rely on a limited number of factors [1][5]. - The AI quantitative strategy team at Swiss Bank Asset Management manages $25 billion, with plans to expand into emerging markets, including A-shares in China [2][3]. - The interest of global capital in the Chinese market is on the rise, with potential AI quantitative strategies targeting A-shares expected to launch next year [2][3]. Group 2: Market Adaptation and Performance - AI models have shown that the signal relationships identified can be generalized across countries, indicating that these models can be adapted to the Chinese market [3][4]. - Emerging markets may offer higher potential excess returns compared to developed markets, although trading costs are also higher, leading to similar overall excess returns relative to benchmarks [3][4]. - The integration of fundamental signals alongside emotional and price signals in emerging markets has been found to enhance model performance [3][4]. Group 3: Differentiation and Risk Management - Swiss Bank Asset Management's AI quantitative strategy focuses on a holding period of approximately 20 days, contrasting with many competitors that prefer shorter holding periods [5][6]. - The firm emphasizes the use of traditional data for model training, covering longer historical periods, and maintaining factor neutrality across various investment styles [5][6]. - To mitigate overfitting risks, the company employs economically sound signals, integrates numerous simple models for training, and utilizes a cross-validation method with 15 years of data [6]. Group 4: Evolving Role of Fund Managers - The role of fund managers is evolving with the rise of AI in quantitative investing, shifting from model building to training AI models and validating outputs [6]. - Fund managers will continue to conduct factor research and oversee investment portfolio construction, maintaining the same number of personnel despite changes in responsibilities [6].