Core Viewpoint - The article emphasizes that AI-driven quantitative investment is becoming essential for public funds, with Minsheng Jianyin Fund leading the way in this transformation through a comprehensive AI quantitative strategy development over the past four years [1][6][14]. Group 1: AI Quantitative Investment Strategy - Minsheng Jianyin Fund's quantitative investment director, He Jiang, initiated a focus on AI quantitative investment strategies in 2021, creating a "data-feature-strategy-portfolio" closed-loop system that is evolving into a unique competitive advantage [1][10]. - The core barrier of Minsheng Jianyin's AI quantitative strategy lies in effectively converting subjective insights into machine-learnable optimization mechanisms, continuously refining investment rules in high-dimensional space [10][11]. - The shift from traditional linear models to AI models allows for the capture of complex non-linear market relationships, significantly enhancing predictive capabilities and investment returns [9][11]. Group 2: Motivations for Focusing on AI - Traditional quantitative strategies face limitations, with the average excess return of the CSI 500 index-enhanced public funds dropping below 3% in 2022, indicating a need for innovation [6][14]. - The explosion of AI technology, driven by improved computing power and algorithm advancements, enables models to uncover complex market relationships that are difficult for the human brain to analyze [6][11]. - Minsheng Jianyin possesses unique internal research data, which has been integrated to create a proprietary fundamental feature database, enhancing their AI model's effectiveness [7][11]. Group 3: Performance and Future Outlook - The Minsheng Jianyin CSI 2000 index-enhanced fund, managed by He Jiang, has shown impressive returns, with a six-month return of 17.18% and a one-year return of 49.66%, significantly outperforming the benchmark [13]. - The CSI 2000 index is viewed as a valuable asset for long-term investment due to its structural opportunities in technology upgrades, including AI, semiconductor growth, and high-end manufacturing [13][14]. - The future of public funds is expected to evolve into an "AI-led quantitative + tool-based index product" ecosystem, with technology finance becoming a fundamental aspect of the industry [14].

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].

Core Insights - AI-driven quantitative investment strategies are evolving, moving from traditional models that rely on a limited number of factors to more advanced models that can identify hundreds of high-frequency signals and non-linear relationships in data [1][5]. Group 1: Company Overview - Swiss Bank Asset Management, part of the Swiss Bank Group with a 220-year history, has an asset management scale of 711 billion Swiss Francs as of June 30, 2025 [2]. - The quantitative investment team led by David Wright manages $25 billion, with plans to expand AI quantitative strategies into emerging markets, including A-shares in China [2][3]. Group 2: Market Interest and Strategy - Global capital interest in China is recovering, with plans to include A-shares in AI quantitative strategies as the team develops a version for emerging markets [2][3]. - The current AI quantitative strategy products are primarily focused on developed markets, tracking the MSCI World Index, but there is a push to include A-shares in the future [2][3]. Group 3: AI Model Adaptability - AI models can adapt to the Chinese market, with backtesting showing that identified signal relationships are transferable to emerging markets, including China [3]. - The potential for excess returns in emerging markets is higher than in developed markets, although trading costs are also higher [3]. Group 4: AI Application in Stock Ratings - AI models can enhance the predictive power of stock ratings by incorporating various signals, such as the timing of earnings reports, to improve decision-making [4][5]. - Traditional quantitative methods typically use around 20 company-level signals, while Swiss Bank's AI strategy utilizes approximately 400 high-frequency signals [5]. Group 5: Differentiation in AI Strategies - Swiss Bank's AI quantitative strategy focuses on a holding period of about 20 days, contrasting with many competitors that prefer shorter holding periods [5][6]. - The strategy emphasizes factor neutrality, maintaining balanced exposure across various investment styles without overexposing to any single factor [5][6]. Group 6: Mitigating Overfitting Risks - The company employs several methods to mitigate overfitting risks in AI models, including using economically sound signals, integrating numerous simple models, and applying cross-validation techniques [6]. - The role of fund managers is evolving, shifting from model building to training AI models and conducting factor research, while still maintaining oversight of model outputs and portfolio construction [6][7].

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].

Group 1 - The report highlights the significant role of quantitative stock selection funds in the public fund market, with a total of 277 funds managing a combined scale of 90.32 billion yuan as of the end of Q2 2025, offering broader investment scope and higher style exposure flexibility compared to standard index-enhanced funds [3][12][13] - The report focuses on five fund managers with distinctive investment frameworks in quantitative stock selection, including Feng Xixiang from Xinda Australia Fund, Gao Chongnan from Guotai Fund, Lin Jingyi from Xinda Australia Fund, Shi Yunchao from Penghua Fund, and Zhai Zijian from Western Li De Fund, providing insights into their strategies and product positioning [3][12][13] Group 2 - Feng Xixiang employs a unified framework emphasizing the effectiveness of factors and the universality of alpha models, integrating static multi-factor linear models with machine learning dynamic weighting models since 2023, achieving balanced allocation in his representative products [4][16][23] - Gao Chongnan focuses on the Calmar ratio, selecting high dividend, quality, and growth styles to enhance the stability of risk-return profiles, with a product positioning aimed at low volatility value style [4][35][36] - Lin Jingyi implements a "HI+AI" approach using an integrated research platform, employing a three-step method to replicate successful peer consensus and enhance index tracking through multiple alpha models [5][22] - Shi Yunchao's strategy combines multi-factor linear models with a higher proportion of non-linear models, focusing on short prediction cycles and higher turnover rates, while maintaining a diversified portfolio to mitigate risks [6][24] - Zhai Zijian adopts an AI quantitative investment strategy with a "core + satellite" multi-strategy balanced configuration, utilizing machine learning for long-term predictions and high-frequency data analysis [6][24] Group 3 - The report indicates that as of the end of Q2 2025, Feng Xixiang manages a total of 4.54 billion yuan across seven quantitative stock selection products, with representative products achieving cumulative returns of 40.66% and 74.91% since inception, significantly outperforming their benchmark indices [17][21] - Gao Chongnan's strategy iteration has led to improved performance, with the National Strategy Yield Fund achieving an annualized return of 28.72% in 2024, reflecting a notable enhancement in risk-adjusted returns [36][37] - The quantitative team at Xinda Australia Fund consists of experienced professionals, with a comprehensive product line that includes 11 quantitative stock selection products and 2 quantitative fixed income + strategy products, aiming to reduce volatility while seeking absolute returns [32][33]

1. Report Industry Investment Rating There is no information provided regarding the industry investment rating in the given content. 2. Core Viewpoints of the Report - The report focuses on the analysis of fund manager Yang Lin from Minsheng Jiachen Fund. Yang Lin has over 13 years of experience in the securities industry and about 1.6 years as an investment manager. His investment framework is a multi - dimensional AI quantitative investment framework that continuously iterates and evolves. He makes full use of various information, constructs factors through both manual and machine - learning methods, and applies AI technology in multiple dimensions [3][7][9]. - The representative product, Minsheng Jiachen Smart Selection Growth, has shown excellent performance in the active equity category. Since its establishment until June 27, 2025, it has achieved a return of 27.94%, ranking in the top 20% of active equity products. It also has a relatively low volatility and a high return - risk ratio [3][29]. - The product has distinct investment characteristics. Its overall position is similar to the combination of CSI 500 and CSI 1000, with low individual - stock concentration, small industry over - or under - weighting, and a focus on small - and medium - cap stocks without micro - cap stocks. Trading turnover may be the main source of its income [3][43][53]. 3. Summary Based on the Table of Contents 3.1 Fund Manager Overview - Yang Lin is a CFA, with a bachelor's degree in finance from Xi'an Jiaotong University and a master's degree in financial mathematics from the University of Warwick. He has held various positions in different financial institutions and joined Minsheng Jiachen Fund in October 2019. Currently, he manages one product. His fund manager index lagged slightly behind the CSI 300 before September 2024 due to the small - cap style of the products he managed but has significantly outperformed the CSI 300 since then [3][7]. 3.2 Investment Framework: A Continuously Iterating and Evolving Multi - dimensional AI Quantitative Investment Framework - Yang Lin uses multiple dimensions of information to create effective investment factors. He constructs factors through both manual and machine - learning methods. Manual factors include fundamental (about 20%) and price - volume factors (about 40%), mainly for longer - term factors, while machine - learning processes relatively short - term price - volume factors (about 40%). The ratio of long/medium/short/ultra - short - term factors is about 2:2:3:3 [9][11]. - AI technology is applied in multiple dimensions of his investment framework, such as in ultra - short - term factor construction, manual stock - selection, and comprehensive data processing [11]. - Yang Lin believes that continuous iteration is crucial for maintaining competitiveness. He iterates his models in three dimensions: data, model, and training. He has iterated his quantitative strategies multiple times in the past few years, with an optimization frequency of 1 - 2 times a year, and only implements new strategies in real - time after verifying their superiority [14][15]. 3.3 Representative Product: Minsheng Jiachen Smart Selection Growth - The product was established in June 2024, managed by Cai Xiao and Yang Lin. Its goal is to use a quantitative investment model to achieve long - term stable appreciation of fund assets while controlling risks. It has a management fee rate of 1.20% and a custody fee rate of 0.20% [18]. - In actual operation, it uses a stock strategy of "50% weight of CSI 500 - like all - market enhancement + 50% weight of CSI 1000 - like all - market enhancement". Its holdings are consistent with this positioning, with about half of the holdings being CSI 500 component stocks and about 40% being CSI 1000 component stocks [20]. - Due to the adoption of the high - order PB - ROE framework, the product shows a significant low - PB characteristic in its holdings. Its weighted average PB in 24H2 full - position and 25Q1 heavy - position stocks is in the lower 9.1% and 1.8% levels respectively among active equity products, while ROE is around the 20% quantile [21]. 3.4 Performance Analysis of Minsheng Jiachen Smart Selection Growth - **Performance in the Same Category**: The product's return since its establishment (as of June 27, 2025) is 27.94%, ranking in the top 20% of active equity products. Its annualized volatility is 24.80%, in the lower - middle level among active equity products. Its annualized Sharpe ratio is 1.18, and the Calmar ratio is 2.40, both ranking around the top 10% [29]. - **Relative Performance**: Compared with the composite index of "45% CSI 500 + 45% CSI 1000+10% ChinaBond New Composite Wealth (Total Value)", the product has significantly outperformed the composite index since November 2024. From October 2024 to June 2025, its monthly winning rate is 77.8%, and the average monthly excess return is 0.34% [33][37]. 3.5 Investment Characteristic Analysis of Minsheng Jiachen Smart Selection Growth - **Positioning Characteristics**: The product has a highly diversified individual - stock position. The proportion of the top ten stocks in 24H2 full - position is only about 6%, and the top thirty is about 17%. It focuses on small - and medium - cap stocks without micro - cap stocks, strictly controlling non - linear market - value exposure [43][48]. - **Industry Distribution**: Compared with the industry distribution of CSI 500 and CSI 1000, the product slightly over - weights industries such as basic chemicals, machinery, and environmental protection, and slightly under - weights industries such as electronics, power equipment, and media, with a small overall over - or under - weighting range [49]. - **Income Source**: Using the Brinson model to split the fund's income, trading has made a significant contribution to the product's excess return since its management, while other income sources contribute less. The product's relatively high turnover rate suggests that trading turnover may be the main reason for its leading performance. The product's absolute income comes from various sectors, with financial real - estate and technological innovation contributing more, and it also has a strong ability to obtain relative income in these sectors [51][53].

Group 1 - In 2025, AI quantitative investment and the Federal Reserve's interest rate hikes are key variables reshaping investment logic in the financial markets [1] - AI technology has rapidly advanced in quantitative investment, with firms like Luminus Fund utilizing deep neural networks to extract market features from vast datasets [3] - Luminus Fund's quantitative simulation shows that over 70% of excess returns come from stock selection, highlighting AI's potential in enhancing returns through individual stock analysis [3] Group 2 - The persistent inflation in 2025, with core PCE inflation nearing 3% and CPI inflation expected to rise to 5.4%, increases pressure on the Federal Reserve to consider interest rate hikes [4] - Wall Street's betting on the likelihood of rate hikes has surged from under 10% to 34.6%, with predictions of a potential increase of 75 basis points from major financial institutions [4] - The evolving investment logic indicates that while traditional AI models may struggle with market volatility due to reliance on historical data, models that can adapt quickly may seize more opportunities [5] Group 3 - Different asset classes are affected differently by AI quantitative investment and Federal Reserve rate hikes, with notable divergence in the tech stock market [6] - Stocks like Intel surged by 16% due to market sentiment and AI-driven funds, while growth stocks like Meta and Netflix face challenges from anticipated rate hikes [6] - In the bond market, rising rates lead to falling bond prices, but AI models can optimize bond allocations across various maturities and credit ratings [6] Group 4 - The gold market is also impacted, with short-term dollar strength from rate hikes suppressing gold price increases, while AI quantitative investment can analyze multidimensional data to capture short-term price fluctuations [6] - Investors in 2025 must reassess their strategies, recognizing both the advantages and limitations of AI quantitative investment while closely monitoring Federal Reserve rate hike developments [7] - Adjusting asset allocations, such as increasing cash reserves and focusing on stable, cash-rich companies less affected by rate hikes, is essential for navigating the complex market environment [7]

Core Insights - The rise of AI in the quantitative private equity sector is highlighted by the recent achievements of domestic firms like NianKong Technology, which has made significant strides in AI applications for finance [1][2] - As of May 23, 2023, there are 39 domestic quantitative private equity firms managing over 10 billion yuan, with 15 of them actively engaging in AI-related investments [1] Group 1: Company Developments - NianKong Technology has collaborated with Shanghai Jiao Tong University to submit a research paper on large model training methodologies to the international NIPS conference [1] - The establishment of Shanghai QuanPin Siwei Artificial Intelligence Technology Co., Ltd. by NianKong Technology focuses on cutting-edge AI research [1] - NianKong Technology, founded in 2015, operates two quantitative private equity firms, NianJue Assets and NianKong Data Technology, and is recognized as an early adopter of AI in the financial sector [1] Group 2: Performance Metrics - NianKong Technology's quantitative products have shown impressive performance, with an average return of 21.50% over the past year across four products [1] - Specific products managed by NianKong's founder, Wang Xiao, achieved returns of 33.96% and 23.24% over the past year [1] - Among the 15 billion-yuan AI quantitative private equity firms, 13 have reported products with more than three performance records, yielding an average return of 29.91% over the past year [2] Group 3: Industry Trends - The integration of AI in quantitative investment has become increasingly significant, with a noticeable divergence in returns between subjective long and quantitative long strategies since 2020 [2] - The technological advancements in AI are paving the way for quantitative investment, making "AI + Quantitative" a crucial consideration for investors in the future [2] - DeepSeek, backed by Liang Wenfeng's firms, has set a benchmark in the industry, with its affiliated firm, Huanfang Quantitative, leading in average returns over the past six months and year [2]