量化模型持续进化，他是指数增强的“超级黑马”

Core Viewpoint - The article discusses the revolutionary breakthroughs in artificial intelligence over the past year, particularly in machine learning, which has evolved from a powerful data processing tool to a self-thinking capability, enabling significant advancements in fields like autonomous driving and robotics. The focus is on the performance of the Anxin Quantitative Selected CSI 300 Enhanced Fund managed by Shi Rongsheng, which utilizes a "white-box" machine learning approach to capture alpha and achieve superior performance in index enhancement strategies [1][4]. Group 1: Performance Metrics - The Anxin Quantitative Selected CSI 300 Enhanced Fund achieved a cumulative return of 36.53% and an annualized return of 16.62% since Shi Rongsheng took over management on August 24, 2023, outperforming the CSI 300 index by 14.89% [4]. - The fund's performance metrics include a maximum drawdown of -10.90%, indicating a robust risk management strategy [4]. - In comparison, other CSI 300 enhanced funds showed lower returns and higher drawdowns, highlighting the superior performance of Shi Rongsheng's fund [2][4]. Group 2: Machine Learning Approach - Shi Rongsheng transitioned from traditional multi-factor models to machine learning methods around 2020, recognizing the limitations of linear relationships in complex financial markets [8][9]. - The "white-box" approach allows for active factor selection and model transparency, enabling better understanding and optimization of the model's performance [10][13]. - The unified index enhancement framework employed by Shi Rongsheng allows for consistent application across various indices, reducing the risk of overfitting common in multi-factor models [14][15]. Group 3: Data Utilization and Model Evolution - The model's effectiveness is enhanced by feeding it high-quality data and allowing it to learn from a comprehensive dataset that spans complete economic cycles [16][19]. - Shi Rongsheng's strategy includes dynamic optimization of the model based on real-time market performance, ensuring adaptability to changing market conditions [19]. - The modular approach in building the machine learning model facilitates continuous improvement and scalability, akin to a standardized production line in the restaurant industry [21]. Group 4: Innovation and Future Outlook - Shi Rongsheng's exploration of large language models and other advanced technologies positions his strategies at the forefront of quantitative investment, combining the strengths of both public and private fund methodologies [22][24]. - The article emphasizes the potential for stable and continuous excess returns from the Anxin Quantitative Selected CSI 300 Enhanced Fund and the upcoming Anxin ChiNext Index Enhanced Fund, driven by ongoing innovations in machine learning [24].