Core Viewpoint - The article discusses the development and capabilities of the GF Quantitative Alpha Factor Database, which supports various investment strategies through a comprehensive factor library built on extensive research and data accumulation by the GF Quantitative team [1]. Group 1: Database Overview - The GF Quantitative Alpha Factor Database is established on MySQL 8.0 and encompasses over a decade of research experience, integrating fundamental factors, Level-1 and Level-2 high-frequency factors, machine learning factors, and alternative data factors [1]. - The database supports strategies such as long-short strategies, index enhancement, ETF rotation, asset allocation, and derivatives [1]. - The GF Quantitative team possesses a data storage capacity of over 100TB and high-performance CPU/GPU computing servers, collaborating with reliable data providers like Wind, Tianruan, and Tonglian for efficient factor development and dynamic updates [1]. Group 2: Factor Types and Performance - The article lists various factors categorized by type, including deep learning factors, trading volume factors, and market order ratios, each with specific definitions and performance metrics [3]. - For instance, the "agr_dailyquote" factor has a historical average of 14.22% and a historical win rate of 91.97% [3]. - The "bigbuy" factor shows a historical average of 7.85% with a win rate of 66.74% [3]. Group 3: Research Reports - A series of research reports are available for download, covering topics such as style factor-driven quantitative stock selection, industry selection, and macroeconomic observations related to Alpha factor trends [4][5]. - The reports include analyses on the application of factors in the CSI 300 index and various strategies for capturing industry alpha drivers [4].

Quantitative Models and Construction Methods Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection of stocks from the broker gold stock pool to outperform the median of active equity funds[39][43]. - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark. - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool. - The industry distribution of all public funds is used as the industry allocation benchmark. - The model's detailed construction method can be found in the report "Broker Gold Stock Full Analysis - Data, Modeling, and Practice" published on February 18, 2022[39][43]. - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the active equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][39]. Model Backtesting Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: 15.49%[5][42] - **Excess Return Relative to Active Equity Fund Index (Monthly)**: 3.59%[5][42] - **Absolute Return (Year-to-Date)**: 34.01%[5][42] - **Excess Return Relative to Active Equity Fund Index (Year-to-Date)**: 5.72%[5][42] - **Ranking in Active Equity Funds (Year-to-Date)**: 30.38% percentile (1054/3469)[5][42] Quantitative Factors and Construction Methods Factor Name: Single Quarter Net Profit Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of net profit in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net profit for the current quarter. - Compare it to the net profit of the same quarter in the previous year. - The formula is: $ \text{Net Profit Growth Rate} = \frac{\text{Current Quarter Net Profit} - \text{Previous Year Same Quarter Net Profit}}{\text{Previous Year Same Quarter Net Profit}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter ROE - **Factor Construction Idea**: This factor measures the return on equity for a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net income for the quarter. - Divide it by the average shareholders' equity for the quarter. - The formula is: $ \text{ROE} = \frac{\text{Net Income}}{\text{Average Shareholders' Equity}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of revenue in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the revenue for the current quarter. - Compare it to the revenue of the same quarter in the previous year. - The formula is: $ \text{Revenue Growth Rate} = \frac{\text{Current Quarter Revenue} - \text{Previous Year Same Quarter Revenue}}{\text{Previous Year Same Quarter Revenue}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Backtesting Results Single Quarter Net Profit Growth Rate - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter ROE - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter Revenue Growth Rate - **Performance**: This factor has shown good performance recently[3][28].

Core Insights - The article discusses the use of a multi-factor model suitable for the CSI 300 index constituents, combined with a small-cap high-growth satellite strategy, to enhance the performance of the CSI 300 enhanced strategy [1][2] - Since 2016, the CSI 300 enhanced strategy has achieved an annualized excess return of 12.6% with a tracking error of 5.2% under a satellite allocation of 30% domestic and 10% foreign [1][2] Summary by Sections - **Performance Analysis**: The CSI 300 enhanced strategy has shown an annualized excess return of at least 10% since 2016, with an information ratio exceeding 2.0. The internal component of the strategy has lower tracking error and relative drawdown, while the external component offers greater return elasticity but with higher tracking error and drawdown [1][2] - **Model Construction**: The multi-factor model is constructed based on fundamental and momentum indicators, which has demonstrated better stock selection robustness compared to the all-A multi-factor model [1] - **Satellite Strategy**: The external component can be replaced with small-cap high-growth or GARP strategies. The optimal satellite allocation depends on the risk-return preference, with the most extreme case showing an annualized excess return of 17.5% when fully utilizing satellite strategies [2]

- The report discusses a multi-factor model suitable for the constituents of the CSI 300 Index, combined with a small-cap high-growth portfolio as an external satellite strategy to improve the performance of the CSI 300 enhanced strategy[1][3][5] - The internal part of the enhanced strategy uses a multi-factor model based on fundamental and momentum indicators, including factors such as ROE, ROE YoY, SUE, expected net profit adjustment, accelerated growth, cash flow ratio, value (dividend yield and BP equal weight composite), momentum, buy-in strength after opening, and large order-driven rise[16][17] - The external part of the enhanced strategy uses a small-cap high-growth portfolio, constructed using factors such as SUE, EAV, expected net profit adjustment, cumulative R&D investment, PB_INT, small-cap, late trading volume ratio, and large order net buy-in ratio after opening[35][36] - The internal multi-factor model shows more stable stock selection performance within the CSI 300 Index constituents compared to the all-A multi-factor model, with higher IC and RankIC information ratios[16][17] - The small-cap high-growth portfolio has an annualized return of 25.0% since 2016, with an annualized excess return of 24.4% relative to the CSI 300 Index, but also higher tracking error[35][36] - The GARP strategy, which balances growth potential and reasonable pricing, is also considered as an external satellite strategy, showing an annualized return of 20.9% for the GARP 20 portfolio and 17.4% for the GARP 50 portfolio since 2016[39][40][42] - Combining the internal multi-factor model and external satellite strategies (small-cap high-growth or GARP) can significantly improve the performance of the CSI 300 enhanced strategy, with annualized excess returns not less than 10% and information ratios above 2.0 since 2016[29][45][55] Model and Factor Construction Process - **Internal Multi-Factor Model**: Constructed using fundamental and momentum indicators, including ROE, ROE YoY, SUE, expected net profit adjustment, accelerated growth, cash flow ratio, value (dividend yield and BP equal weight composite), momentum, buy-in strength after opening, and large order-driven rise[16][17] - **Small-Cap High-Growth Portfolio**: Constructed using factors such as SUE, EAV, expected net profit adjustment, cumulative R&D investment, PB_INT, small-cap, late trading volume ratio, and large order net buy-in ratio after opening[35][36] - **GARP Strategy**: Constructed by excluding high-risk stocks, using PB and dividend yield as value factors, and ROE, SUE, EAV, expected net profit adjustment, and two-year compound growth rate as growth factors, selecting the top 20 or 50 stocks based on composite scores[41][42] Model and Factor Performance Metrics - **Internal Multi-Factor Model**: IC monthly average 6.36%, IC monthly win rate 67.0%, annualized ICIR 1.67; RankIC monthly average 7.53%, RankIC monthly win rate 72.2%, annualized ICIR 2.00[17] - **Small-Cap High-Growth Portfolio**: Annualized return 25.0%, annualized excess return 24.4%, tracking error 20.3%, information ratio 1.21, relative drawdown 39.6%, monthly win rate 61.4%[36] - **GARP 20 Portfolio**: Annualized return 20.9%, annualized excess return 20.3%, tracking error 15.8%, information ratio 1.26, relative drawdown 36.0%[42] - **GARP 50 Portfolio**: Annualized return 17.4%, annualized excess return 16.8%, tracking error 14.6%, information ratio 1.14, relative drawdown 37.2%[42] Combined Strategy Performance - **Internal 20% + External 10% (Small-Cap High-Growth)**: Annualized excess return 11.7%, information ratio 2.35, tracking error 5.2%, relative drawdown 21.9%[45][48] - **Internal 20% + External 10% (GARP)**: Annualized excess return 11.3%, information ratio 2.41, tracking error 4.3%, relative drawdown 5.8%[50][53]

Quantitative Models and Construction Methods Model Name: Hong Kong Stock Selection 20 Portfolio - **Model Construction Idea**: The model aims to track the monthly performance of a long portfolio by selecting the top 20 stocks with the highest scores based on multiple factors. The benchmark is the Hong Kong Composite Index (HKD) (930930.CSI) [40][42] - **Model Construction Process**: - The model uses four types of factors: technical, capital, fundamental, and analyst expectations [40][41] - At the end of each month, the top 20 stocks with the highest scores are selected and equally weighted to construct the Hong Kong Stock Selection 20 Portfolio [42] - **Model Evaluation**: The model has shown excellent performance in the Hong Kong Stock Connect sample stocks [40] Model Backtesting Results - **Hong Kong Stock Selection 20 Portfolio**: - **July 2025**: Portfolio return 11.6%, benchmark index return 4.8%, excess return 6.8% [42] - **Overall Period (2015.1~2025.7)**: Excess annualized return 13.9%, excess return volatility ratio 1.0 [42][44] Quantitative Factors and Construction Methods Factor Name: Technical, Capital, Fundamental, Analyst Expectations - **Factor Construction Idea**: These factors are used to evaluate the performance of Hong Kong Stock Connect sample stocks [40] - **Factor Construction Process**: - **Technical**: Based on stock price movements and technical indicators - **Capital**: Based on capital flow and trading volume - **Fundamental**: Based on financial metrics such as PE ratio, ROE, etc. - **Analyst Expectations**: Based on analyst ratings and forecasts [40][41] - **Factor Evaluation**: These factors have shown excellent performance in the Hong Kong Stock Connect sample stocks [40] Factor Backtesting Results - **Technical, Capital, Fundamental, Analyst Expectations**: - **July 2025**: Excess annualized return 13.9%, excess return volatility ratio 1.0 [42][44]

Group 1: Report Industry Investment Rating - No information provided in the content Group 2: Core Viewpoints of the Report - Optimizing the position strategy can effectively enhance the real - world performance of the quantitative framework, which is a multiplier method for increasing returns, especially in volatile markets [2][6][111] - Binary full - position strategies can capture returns efficiently in obvious trends but come with high volatility, drawdown risks, and high turnover and commission costs; threshold step - by - step addition strategies have low trading frequency but limited ability to capture returns in volatile markets (except for the LG model) [2][111] - Single continuous strategies perform well in volatile markets. Linear and normal strategies show high return stability, while Sigmoid, Atanh, and Atanh - Sigmoid strategies have significant advantages in volatility control, suitable for risk - averse investors. The GRU model shows stable performance in improving odds, while the strategy advantages of LG, SVM and other models are environment - dependent [2][111] - In terms of turnover and commission consumption, single continuous strategies such as Sigmoid and Atanh can reduce turnover and commission consumption in volatile markets, and investors should focus more on returns rather than commission costs [2][111] Group 3: Summary According to the Table of Contents 3.1 Multi - factor Model's Position Strategy Introduction - **Multi - long and short full - position strategy**: It is a binary extreme position management mode, which can be used as a performance benchmark and a reference for other strategies. It performs poorly in bull markets and better in volatile markets, and is more suitable for non - linear models in volatile markets [12][32][33] - **Threshold multi - long and short full - position strategy and step - by - step addition strategy**: The threshold full - position strategy introduces a fuzzy interval filtering mechanism to reduce misjudgment risks and improve the overall risk - return ratio. The step - by - step addition strategy can reduce turnover and trading costs but may sacrifice some returns, except for the LG model in volatile markets [13][14][53] - **Continuous strategies based on different risk preferences and mapping functions**: Continuous strategies can convert binary probability signals into position adjustment signals, which can be divided into risk - seeking, risk - averse, and risk - neutral types according to risk preferences. Different mapping functions such as linear, Sigmoid, normal, Atanh, and Atanh - Sigmoid are used [18] 3.2 Strategy Back - testing - **Back - testing sample interval and key parameters**: The trading target is the Treasury bond futures T contract. The period from January 1, 2024, to December 31, 2024, is regarded as a bull market, and the period from January 1, 2025, to May 9, 2025, is regarded as a volatile market [31] - **Benchmark results of multi - long and short full - position strategy**: It has little effect on increasing returns in bull markets and performs better in volatile markets. Non - linear models such as RF and SVM can better handle the problem of return increase in volatile markets [33][34] - **Threshold full - position strategy and step - by - step position adjustment strategy**: The threshold strategy can optimize the odds of investment strategies in both bull and volatile markets, but the application effect depends on the model type and market environment. The step - by - step position adjustment strategy can significantly reduce turnover and trading costs but usually sacrifices some returns, except for the LG model in volatile markets [37][40][53] - **Analysis of the effect of single continuous strategies**: In volatile markets, continuous position strategies can significantly improve the odds of strategies without increasing the prediction win rate. Different strategies such as Atanh and Sigmoid have different risk - return characteristics, and their turnover is related to the model and market environment [58][73][74] - **Rediscussion of the impact of trading commissions**: The key is to increase returns rather than reduce costs. Although different models and strategies have different commission consumption, the impact of commissions on returns is relatively small, and investors should focus on return enhancement [94][97][110] 3.3 Summary and Strategy Recommendations - Different position management strategies play an important role in return acquisition and risk control. Investors should choose appropriate models and strategies according to their risk preferences and market conditions [111]

Group 1 - The report emphasizes the importance of optimizing position strategies to enhance the performance of quantitative frameworks in the bond market [1][4][12] - It highlights that the choice of position strategy can significantly impact the overall model's performance, especially in volatile market conditions [4][19][50] - The report discusses various position strategies, including full long/short strategies, threshold-based strategies, and gradual accumulation strategies, each with distinct advantages and disadvantages [20][24][25][26] Group 2 - The report presents a detailed analysis of the backtesting results for different strategies, indicating that the full long/short strategy performs well in trending markets but may incur high transaction costs [47][50][51] - It notes that threshold strategies can filter out low-confidence signals, improving the risk-reward ratio in both bull and volatile markets [55][56] - Gradual adjustment strategies are shown to reduce turnover and trading costs, although they may sacrifice some potential returns, particularly in volatile markets [57][58] Group 3 - The report categorizes continuous strategies based on risk preferences, utilizing different mapping functions to adjust positions according to the strength of the signals [32][34][39] - It discusses the effectiveness of various mapping functions, such as linear, Sigmoid, normal, Atanh, and Atanh-Sigmoid strategies, in managing positions based on market signals [33][36][38][39] - The analysis indicates that non-linear models, particularly in volatile markets, can enhance performance and manage risks more effectively than linear models [51][52]

Core Insights - The article highlights the strong performance of quantitative private equity funds in the first half of the year, driven by factors such as technological iteration, market liquidity, and a strong small-cap style, leading to outperformance compared to actively managed equity funds [1][9][24] - Longqi Technology stands out among billion-yuan quantitative private equity firms, ranking 6th in average returns across its 16 products, showcasing a "long-distance running" spirit and consistent performance over the past year and three years [1][9][24] Performance Overview - In the first half of the year, Longqi Technology achieved impressive results, attributed to the iterative development of multi-factor models, which allowed for stable excess returns amid market volatility [9][14] - The firm employs a diversified strategy with a factor weight structure of 70% for price-volume factors, 20% for fundamental factors, and 10% for alternative factors, balancing short-term explosive potential with long-term value [9][14][24] Company Strategy - Longqi Technology emphasizes a long-term investment philosophy, focusing on effective strategies rather than short-term gains, which has helped them navigate various market cycles successfully [9][14][18] - The company fosters a collaborative research environment, enhancing the efficiency of its investment research team and enabling rapid transformation of research outcomes into actionable strategies [9][14][18] Market Position - Longqi Technology has been recognized for its consistent performance, winning the "Golden Bull Award" in the private equity sector for three consecutive years, reflecting its strong market position and reputation [9][24] - The firm has adapted its strategies in response to market changes, such as shifting from fundamental quantification to price-volume strategies and incorporating alternative factors to enhance risk management [9][17][24] Future Outlook - Longqi Technology plans to continue developing a diverse range of products tailored to investor risk preferences and market conditions, ensuring a comprehensive asset allocation strategy [19][24] - The company maintains a cautious yet innovative approach to AI integration in its investment strategies, focusing on relevant applications rather than purely relying on computational power [23][24]

Core Insights - The performance of index-enhanced private equity products was outstanding in the first half of 2025, with an average return of 17.32% and an average excess return of 14.17% across 705 products, indicating that nearly all products outperformed their benchmark indices [1][2]. Group 1: Performance by Scale - Large-scale private equity (over 5 billion) showed a significant advantage, with 267 products achieving an average return of 18.3% and an average excess return of 14.51%, where 265 products had positive excess returns, accounting for 99.25% [2][3]. - Medium-scale private equity (20 to 50 billion) had 152 products with an average return of 17.3% and an average excess return of 14.37%, with 96.71% of products showing positive excess returns [3]. - Small-scale private equity (0 to 10 billion) had 286 products with an average return of 16.41% and an average excess return of 13.75%, with 89.51% of products achieving positive excess returns [3]. Group 2: Market Trends and Strategies - The market exhibited a significant small-cap style dominance, which positively impacted the performance of index-enhanced products linked to small-cap indices, with 76 other index-enhanced products and 258 air index-enhanced products achieving average returns of 20.84% and 17.88%, respectively [3]. - The performance of CSI 300 index-enhanced products lagged, with an average return of 6.31% and an average excess return of 6.28%, reflecting the overall weak performance of the CSI 300 index [4]. - Factors contributing to the strong performance of index-enhanced private equity products included structural characteristics of the A-share market, high individual stock volatility, and favorable trading conditions due to high average daily trading volume [4][5]. Group 3: Regulatory Environment - The relaxation of merger and acquisition policies by regulatory authorities led to an increase in significant asset restructuring cases, boosting market confidence and improving liquidity, which provided favorable conditions for the implementation of quantitative strategies [5].

1. Report Industry Investment Rating No relevant content provided. 2. Core Views of the Report - The report comprehensively analyzes public and private quantitative funds, covering aspects such as the basic characteristics of quantitative strategies, the development history of domestic quantitative investment, the current development status of the industry, the operational characteristics and performance of quantitative funds, the differences in investment operations between public and private quantitative funds, and how to select quantitative products [1][2][3]. - Quantitative strategies are based on historical data, using methods such as data mining and mathematical modeling to discover investment opportunities, with strong systematic and disciplined features. They focus on research breadth to achieve probability - based wins, different from subjective strategies that rely on research depth [10][11][12]. - Public and private quantitative funds have different development paths and characteristics. Public quantitative funds have experienced stages of growth, slowdown, and strategy diversification, while private quantitative funds have gone through explosive growth, stable development, and challenges [5][16][19]. - There are significant differences in regulatory requirements, management behaviors, investment strategies, and fee terms between public and private quantitative funds, which lead to differences in their risk - return characteristics [6]. - When selecting quantitative products, investors should use a four - dimensional evaluation system of "strategy deconstruction - positioning matching - indicator verification - ability evaluation" to consider factors such as strategy environment adaptability, risk - return characteristic persistence, and management team moat depth [6][90]. 3. Summary According to the Directory 3.1 Quantitative Strategy Basic Characteristics - Quantitative strategies use historical data to discover price change patterns and formulate investment strategies. The most widely used quantitative stock - selection model is the multi - factor model, including price - volume factors, fundamental factors, and alternative factors. Some funds also introduce machine learning factors [10]. - Quantitative strategies have strong strategy discipline, systematically mining investment opportunities and avoiding the influence of subjective emotions. Their risk - control systems are embedded in strategies, with different constraints for different types of products [11]. - Compared with subjective investment, quantitative investment focuses on research breadth and probability - based wins, with lower marginal costs and a wider range of tracked investment opportunities [12]. 3.2 Domestic Quantitative Investment Development History 3.2.1 Public Fund Quantitative Investment Development History - **Germination Period (2004 - 2014)**: From the exploration of "subjective + quantitative" to the initial application of the multi - factor model. The first index - enhanced fund and active quantitative stock - selection fund were established, and with the return of talents, the multi - factor stock - selection model was gradually applied [12][13][15]. - **Accelerated Growth Period (2015 - 2021)**: The multi - factor model became popular, and the scale of quantitative funds expanded rapidly. The scale of index - enhanced strategies increased significantly, while the scale of hedge strategies grew rapidly from 2020 and then declined [16]. - **Steady Development Period (2022 - present)**: The growth rate of the overall scale of public quantitative funds has slowed down, but strategies have become more diversified. Different product lines complement each other, and some managers introduce AI algorithms to iterate strategies [19]. 3.2.2 Private Fund Quantitative Investment Development History - Private quantitative funds have experienced three rounds of growth. From 2019 to 2021, there was explosive growth, with the scale reaching 1.08 trillion yuan at the end of 2021, accounting for 17.1% of the total scale of private securities investment funds. From 2021 to 2023, there was steady development, and in 2024, the industry faced challenges due to market fluctuations and stricter regulations. In 2025, private fund filings recovered [5][22][25]. 3.3 Public and Private Quantitative Fund Industry Development Status 3.3.1 Public Fund Quantitative Strategy and Pattern Distribution - **Strategy Classification**: Public quantitative strategies mainly include active quantitative strategies, index - enhanced strategies, and quantitative hedge strategies. Some equity parts of fixed - income + funds also use quantitative management methods [31]. - **Scale Distribution**: As of 2025Q1, the number of public quantitative equity funds reached 654, with a scale of 3025.88 billion yuan. Index - enhanced products had the largest scale, and the management scale concentration of the top ten managers was relatively high [32][37]. 3.3.2 Private Fund Quantitative Strategy and Manager Situation - **Strategy Classification**: Private quantitative investment strategies are more diverse, including quantitative long - only, stock neutral, convertible bond strategies, CTA strategies, other derivative strategies, arbitrage strategies, and composite strategies [38]. - **Hundred - Billion Private Quantitative Managers**: As of the end of June 2025, there were 39 hundred - billion private quantitative investment fund managers, accounting for nearly half of the total number of hundred - billion private funds [5]. 3.4 Operational Characteristics and Performance of Public and Private Stock Quantitative Funds 3.4.1 Operational Characteristics - **High Turnover**: Quantitative funds have a relatively high turnover rate, which helps capture short - term trading opportunities. Public quantitative funds' annual bilateral turnover is mainly between 2 - 20 times, and private quantitative funds' turnover is generally above 30 times [47][48]. - **Large Number of Holdings**: Quantitative funds usually hold a large number of stocks, with a high degree of diversification in stocks and industries. Public quantitative funds' holding numbers are mainly between 50 - 600, and some exceed 2000. They can reduce non - systematic risks [53][54]. 3.4.2 Performance - **Index - Enhanced Products**: The absolute and excess returns of index - enhanced products vary from year to year, with the overall excess - acquisition ability of CSI 1000 index - enhanced > CSI 500 index - enhanced > SSE 500 index - enhanced. Private index - enhanced funds generally have better excess returns than public ones, but with greater differentiation [57][58]. - **Active Quantitative Funds**: The performance of public and private active quantitative funds varies by year. In 2019 - 2020, public active quantitative funds performed better, while in 2018, 2021 - 2023, private ones performed better. Private funds have greater performance and drawdown differentiation [66]. - **Quantitative Hedge Funds**: Private quantitative hedge funds generally outperform public ones in terms of annual returns, but their performance and drawdown differentiation are also greater [70]. 3.5 Differences in Investment Operations between Public and Private Quantitative Funds - **Regulatory Requirements and Contracts**: Public quantitative funds are regulated by the "Securities Investment Fund Law", with high regulatory intensity and high information transparency. Private quantitative funds are regulated by the "Regulations on the Supervision and Administration of Private Investment Funds", with more customized contracts and higher risk levels [79]. - **Management Behaviors**: Public quantitative managers rely on institutionalized teams and standardized IT infrastructure, with a focus on systematic risk control and compliance. Private managers use an elite - based organizational structure, with higher hardware investment and employee incentives, and their product strategies may be more differentiated [81]. - **Investment Strategies and Restrictions**: Public quantitative funds have stricter constraints on investment scope, proportion, and tracking error, with lower turnover. Private quantitative funds have more flexible mechanisms, with higher turnover and greater elasticity in excess returns [6][84]. - **Fee Terms**: Private quantitative product fee terms are more complex, usually including management fees and performance rewards, while public quantitative products mainly charge fixed management fees and custody fees [6][87]. 3.6 How to Select Quantitative Products - When selecting quantitative products, investors should use a four - dimensional evaluation system of "strategy deconstruction - positioning matching - indicator verification - ability evaluation" to consider factors such as strategy environment adaptability, risk - return characteristic persistence, and management team moat depth [6][90].