Core Viewpoint - The article highlights the strong performance of the GF Quantitative Multi-Factor Mixed Fund (005225), which has achieved a cumulative return of 109.93% since its inception on March 21, 2018, significantly outperforming its benchmark across various time frames [1]. Group 1: Fund Performance - The GF Quantitative Multi-Factor Fund has a high equity position of 91.75%, with a diversified portfolio that includes six stocks with a total market capitalization below 10 billion, accounting for 8.35% of the fund's net asset value [2]. - Over the past year, the GF Quantitative Multi-Factor Fund has outperformed the National Securities 2000 Index by 30 percentage points, achieving a return of 54.33% compared to the index's performance [2]. - The fund's monthly win rate against the National Securities 2000 Index is 81%, with an average monthly excess return of 1.20% since the current fund managers took over [3]. Group 2: Investment Strategy - The fund employs a dual-engine model combining traditional quantitative multi-factor models with advanced machine learning techniques to enhance factor discovery and integration [4][5]. - The fund managers utilize AI tools to identify hidden pricing patterns and improve the efficiency of alpha factor extraction, addressing the limitations of traditional models [5]. Group 3: Other Quantitative Funds - The article also discusses other quantitative funds under GF, such as the GF Multi-Factor Mixed Fund (002943), which has consistently outperformed major indices over the past seven years [6][7]. - GF has a diverse range of quantitative products, including Smart Beta strategies, which focus on small-cap style enhancement [7]. Group 4: Dividend and Value Strategies - The GF Stable Strategy Fund (006780) employs a combination of subjective and quantitative approaches to capture dividend opportunities, achieving a return of 25.93% in 2024, outperforming the benchmark by 7.17% [10]. - The GF High Dividend Preferred Fund (008704) focuses on high-dividend, low-valuation stocks, achieving a year-to-date return of 12.10%, significantly surpassing the performance of the benchmark indices [14][15].

Core Viewpoint - The article emphasizes the successful integration of quantitative and active investment strategies by Ye Shuai at GF Fund, highlighting the dual benefits of capturing beta and alpha returns through a systematic approach to investment [1][2]. Group 1: Investment Strategy - Ye Shuai combines scientific quantitative methods with deep research to form a core investment philosophy centered on "scientific quantitative active investment" [2]. - The "active + quantitative" approach leverages subjective investment advantages in deep research and forward-looking predictions while utilizing quantitative models for initial stock screening [2][4]. - The investment strategy focuses on small-cap stocks with a strong beta return potential, aiming to select stocks with a solid valuation margin and predictable growth [4][5]. Group 2: Performance Metrics - The GF Big Data Strategy Growth Fund, managed by Ye Shuai, has shown impressive performance, with the A share rising 29.97% over the past year, ranking in the top 20 among similar flexible allocation funds [3]. - Over three years, the fund ranks in the top 10 of its category and has received a three-year five-star rating from Galaxy Securities [3]. Group 3: Research and Analysis Framework - The investment process involves a combination of top-down active research and bottom-up quantitative output, focusing on macroeconomic variables to guide overall portfolio management [4][6]. - The team employs a multi-layered stock selection strategy, emphasizing safety margins and growth potential through a systematic evaluation of candidates based on historical valuation and expected changes [5][6]. Group 4: Platform and Team Support - GF Fund's robust platform and team collaboration are crucial for the sustained excess returns of Ye Shuai's products, supported by a comprehensive research resource network [6]. - The stable strategy department consists of nine research members dedicated to systematic investment methods, ensuring disciplined execution of investment decisions based on objective data [6]. Group 5: AI Integration - The stable strategy team has incorporated AI stock selection technology, utilizing self-developed neural network architectures to extract potential alpha information from structured and unstructured data [7].