鸣石基金：AI驱动+本土化创新！十五年持续迭代量化投研版图

Core Insights - The article highlights the growing popularity and impressive performance of quantitative private equity funds, particularly focusing on Ming Stone Fund, which has established itself as a leading player in the industry since its inception in 2010 [1][2]. Company Overview - Ming Stone Fund was founded in December 2010 and currently employs over 100 staff globally, with more than 80% of the research team holding advanced degrees from prestigious universities [2]. - The founder and general manager, Dr. Yuan Yu, has a strong academic background, having obtained a Ph.D. in Finance from the Wharton School and previously worked at the Federal Reserve Bank of the United States [4][2]. Investment Strategy - The fund has developed a unique Chinese-style three-factor model (CH-3) that adapts the popular Fama-French model to better fit the Chinese market, focusing on market, size, and value factors [12]. - Ming Stone Fund employs a proprietary "Five-Ring Multi-Core" quantitative research system, which includes five key research stages: factor, AI, optimization, risk control, and trading [9][13]. AI Integration - Since 2021, the fund has increasingly integrated AI into its quantitative research processes, establishing the G-Lab AI laboratory to enhance efficiency and adaptability of investment strategies [13][14]. - The fund's approach combines academic research with AI-driven factor selection, ensuring a robust theoretical foundation for its investment strategies [14]. Performance Metrics - Ming Stone Fund's quantitative stock selection product, "Ming Stone Spring 28," ranked third among top private equity quantitative stock selection products in terms of excess returns over the past three years [15]. - The fund attributes its strong performance to its efficient research system, adaptable strategies, and the favorable market environment characterized by increased liquidity and volatility [16]. Risk Management - The fund emphasizes risk control by utilizing a self-developed multi-factor risk control model, which enhances the predictive capability for volatility in high-frequency trading strategies [18].