Core Viewpoint - Mingxi Capital aims to become a top global hedge fund by providing long-term stable Alpha asset allocation tools, emphasizing talent development and a long-term investment philosophy [3]. Company Overview - Mingxi Capital, established in 2014, has a registered capital of 300 million and manages assets between 5-10 billion [2][6]. - The team consists of over 40 members, with more than 80% in research and IT, featuring core members from renowned hedge funds like Point72, Citadel, and Millennium [2][6]. - As of July 2025, the average return of products under Mingxi Capital is ranked 2nd in the quantitative private equity performance list for the first seven months [2]. Investment Philosophy - The investment philosophy is based on "investment logic," utilizing innovative "composite logic" and self-developed machine learning algorithms, with a strong focus on tail risk management [2][17]. Core Advantages - The core team has an average of over 10 years of experience, with 100% of the research team holding master's or doctoral degrees from prestigious universities [14]. - The strategy framework is derived from leading firms like D.E. Shaw and Point72, covering various strategies including stock index enhancement, market neutrality, and high-frequency CTA [14][15]. Product Lines - The index enhancement strategy aims to outperform benchmark indices by selecting a basket of stocks in the A-share market, with daily optimization [17]. - The quantitative stock selection strategy does not benchmark against specific indices, focusing on high-quality stock selection across the entire market [20]. - The market-neutral strategy combines long and short positions to maximize capital utilization while minimizing risk [20]. Achievements and Recognition - Mingxi Capital has received several accolades, including top rankings in various private equity competitions and performance metrics [22][23].

Group 1 - The consumer electronics sector has recently surged, particularly driven by Huawei-related stocks, indicating a potential underlying trend rather than just news-driven excitement [1][4] - The performance of Huawei-related stocks, such as Junyi Digital and Aerospace Hongtu, suggests that their rise is not solely due to upcoming product launches but reflects deeper market dynamics [4][6] - Institutional investors have been quietly accumulating these stocks during periods of consolidation, which often leads to ordinary investors missing out on the subsequent rallies [6][10] Group 2 - The key to profiting in a bull market lies in the efficient use of capital, as some investors significantly outperform the market while others struggle to keep pace [7][9] - Many investors mistakenly believe that simply holding stocks during a bull market is sufficient, but this can lead to idle capital during long periods of price stagnation [9][10] - Recognizing institutional trading behaviors and timing entry points can enhance capital utilization and improve investment outcomes [10][12] Group 3 - Quantitative data analysis can reveal patterns in institutional trading, providing insights into when institutions are likely to start accumulating positions [10][12] - For instance, the "institutional inventory" data indicates the level of institutional activity, and a rise in this data often signals significant institutional buying [12][14] - Stocks like Silicon Treasure and Changchun Yidong have shown similar patterns of institutional accumulation prior to price increases, highlighting a consistent trend across different sectors [14][16][18] Group 4 - The recent activity in the consumer electronics sector is likely a result of prior institutional positioning, suggesting that the current market movements may be part of a longer-term trend rather than short-lived reactions to news [18]

Group 1 - The core viewpoint of the news is that Lichong Group is leveraging its industrial chain synergy to achieve steady revenue growth and expand its market presence in various sectors, including new energy vehicles and robotics [1] - In the first half of 2025, Lichong Group is expected to report a revenue of 1,444,339 million yuan, representing a year-on-year increase of 15.41%, and a net profit of 40,129 million yuan, with a growth of 4.97% [1] - The company is focusing on the application of aluminum alloy new materials in emerging industries and has established production bases in Thailand and Mexico, enhancing its global supply capabilities [1] Group 2 - Lichong Group has successfully implemented standardized circulation of recycled aluminum products through the futures market, which has improved market recognition [1] - The company is expanding its overseas production capacity, particularly in high-end forging, casting, and low-carbon aluminum alloy wheels [1]

Quantitative Models and Construction Methods 1. Model Name: Multi-dimensional Quantitative Rotation Model: Growth-Dividend Balanced Allocation - **Model Construction Idea**: This model aims to rotate between high-growth and dividend strategies based on effective single-factor signals, providing balanced allocation between growth and dividend styles[9] - **Model Construction Process**: - At the end of each month, the model selects effective signals from single-factor tests, including term spread, social financing growth, CPI and PPI quadrants, US Treasury yields, and capital flow dynamics (ETF, insurance funds, foreign capital)[9] - Each factor provides a buy signal for either high-growth or dividend strategies, and the average score across all factors is used as the final allocation score[9] - **Model Evaluation**: The model effectively captures market rotation opportunities, with strong performance in high-growth scenarios supported by improving macroeconomic indicators[9] 2. Model Name: Large-Cap vs. Small-Cap Rotation Model - **Model Construction Idea**: This model rotates between large-cap and small-cap styles based on macroeconomic and monetary indicators, aiming to exploit relative strength and momentum effects[24][29][35] - **Model Construction Process**: - **Monetary Cycle**: - Use short-term interest rates (Shibor3M and 1-year government bond yields) to classify monetary conditions as tight or loose[29] - Buy small-cap stocks during loose monetary conditions and large-cap stocks during tight conditions[29] - **Modified Monetary Activation Index**: - Use M1 and M2 growth rates and their scissors difference to classify market conditions into four quadrants[32] - Allocate between large-cap and small-cap stocks based on the quadrant classification[32] - **Relative Strength**: - Use moving averages to capture momentum; when the small-cap relative strength index crosses above its 9-month moving average, allocate to small-cap stocks, otherwise allocate to large-cap stocks[35] - **Model Evaluation**: The model demonstrates significant outperformance in capturing large-cap and small-cap rotation opportunities, with strong sensitivity to monetary conditions and momentum effects[29][32][35] --- Model Backtesting Results 1. Multi-dimensional Quantitative Rotation Model: Growth-Dividend Balanced Allocation - **Cumulative Return**: 348.20%[6] - **Annualized Return**: 17.35%[6] - **Maximum Drawdown**: 27.08%[6] - **Annualized Volatility**: 23.14%[6] - **Annualized Sharpe Ratio**: 0.75[6] - **Calmar Ratio**: 0.64[6] 2. Large-Cap vs. Small-Cap Rotation Model - **Cumulative Return**: 158.61%[22] - **Annualized Return**: 10.67%[22] - **Maximum Drawdown**: 32.46%[22] - **Annualized Volatility**: 21.01%[22] - **Annualized Sharpe Ratio**: 0.51[22] - **Calmar Ratio**: 0.33[22] --- Quantitative Factors and Construction Methods 1. Factor Name: Term Spread - **Factor Construction Idea**: Reflects fixed-income market expectations of future economic growth; widening spreads favor high-growth styles[13] - **Factor Construction Process**: - Calculate the spread between 10-year and 1-year government bond yields[13] - Use the monthly change in the spread as a signal for growth or dividend allocation[13] 2. Factor Name: Social Financing Growth - **Factor Construction Idea**: Serves as a leading macroeconomic indicator; higher growth supports high-growth styles[13] - **Factor Construction Process**: - Measure the year-over-year growth rate of total social financing stock[13] - Use the monthly change in growth rate as a signal for allocation[13] 3. Factor Name: CPI and PPI Quadrants - **Factor Construction Idea**: Captures inflation dynamics; CPI rising faster than PPI indicates strong downstream demand, favoring high-growth styles[17] - **Factor Construction Process**: - Classify market conditions into quadrants based on the year-over-year changes in CPI and PPI[17] - Allocate based on the quadrant classification[17] 4. Factor Name: US Treasury Yields - **Factor Construction Idea**: Reflects global risk appetite; higher yields negatively impact high-growth styles[17] - **Factor Construction Process**: - Use the level and trend of US 10-year Treasury yields as a signal for allocation[17] 5. Factor Name: Capital Flow Dynamics - **Factor Construction Idea**: Measures foreign capital inflows and domestic ETF flows; higher inflows support high-growth styles[18] - **Factor Construction Process**: - Construct a composite index using the USD index, RMB offshore exchange rate, and CDS spreads[18] - Use the index trend as a signal for allocation[18] --- Factor Backtesting Results 1. Term Spread - **Latest Value**: 0.40 (up from 0.32 last month)[13] 2. Social Financing Growth - **Latest Value**: 9% YoY (up from 8.9% last month)[13] 3. CPI and PPI Quadrants - **CPI**: 0% YoY (down from 0.1% last month)[17] - **PPI**: -3.6% YoY (unchanged from last month)[17] 4. US Treasury Yields - **Latest Value**: 4.26% (high-level oscillation)[17] 5. Capital Flow Dynamics - **Foreign Capital Inflow Index**: Strengthened due to RMB depreciation and CDS spread widening[18]

Group 1 - The recent pressure from President Trump on the Federal Reserve to lower interest rates echoes historical interventions, particularly during Nixon's presidency, which led to unexpected outcomes in monetary policy [3][4] - The current global monetary system differs from Nixon's era, but historical experiences can still provide insights into market trends [3] - The actions of Trump, including the dismissal of Federal Reserve officials, raise concerns about the independence of the central bank and the potential for overly accommodative monetary policy, which could increase long-term inflation expectations [4] Group 2 - The concept of "institutional clustering" in the A-share market is often misunderstood; it is not merely about the number of institutions buying but rather about the operational model of trading [4] - The performance of stocks like "Shutai Shen" and "Kunyuan Group" illustrates the impact of institutional support, with "Shutai Shen" showing significant institutional backing while "Kunyuan Group" lacks sustained support [7][10] - Quantitative data analysis reveals that institutional trading behaviors can be identified and leveraged, allowing for better investment decisions based on the activity levels of institutional investors [8][10] Group 3 - Historical lessons suggest that if the independence of the Federal Reserve is compromised, it may lead to short-term benefits but could ultimately result in uncontrolled inflation and rising interest rates, similar to the Nixon era [13] - The current market dynamics, influenced by expectations of interest rate cuts, have led to a nearly 10% decline in the dollar index this year, while the yield curve for U.S. Treasuries has steepened, indicating potential increases in long-term yields [13][14] - The essence of market behavior remains unchanged despite evolving circumstances; understanding human nature and capital dynamics is crucial for long-term investment success [14]

Company Overview - Zhicheng Zhuoyuan was established on June 19, 2017, and focuses on quantitative investment, with a current active management scale exceeding 16 billion yuan [4] - The company aims to create a top-tier domestic private equity fund management company that benchmarks against overseas quantitative hedge funds [4][5] - The company has a clear and stable equity structure, with the actual controller holding over 80% of the shares, ensuring efficient decision-making [6] Development History - The company was founded in 2014, with its first quantitative hedge strategy achieving real performance in 2014 [4] - By 2022, the management scale exceeded 10 billion yuan, and as of now, it has surpassed 16 billion yuan [4] Core Investment Philosophy - The investment philosophy is based on statistical arbitrage, assuming that future market behavior will resemble past patterns, allowing for the estimation of future price expectations [5] - The strategy involves ranking stocks based on expected returns and adjusting positions to maintain a portfolio with a higher expected return than the market index [5] Core Team and Advantages - The core team, led by investment director Shi Fan, consists of members with strong backgrounds in finance and quantitative analysis, primarily graduates from Peking University [7] - The investment team has over 10 years of localized quantitative management experience, with a low correlation between their models and market trends, allowing for stable alpha generation [15][37] Investment Strategy and Product Line - The company offers two main types of quantitative products: market-neutral and quantitative long strategies [4] - The investment strategy is characterized by a focus on short-cycle, low-frequency trading, aiming for stable returns while managing risk effectively [41] Risk Control - The company has established a comprehensive risk management system that includes pre-trade, intra-trade, and post-trade risk controls [31][32][33] - The risk management framework is integrated into the investment strategy, allowing for dynamic adjustments based on market conditions [32][33] Core Advantages - The company emphasizes a cautious approach to scaling, prioritizing returns and volatility over aggressive growth [41] - The unique investment research system integrates traditional fund management processes with modern factor-based research, enhancing efficiency and innovation [42] Awards and Recognition - Zhicheng Zhuoyuan has received multiple awards, including the 2023 China Private Equity Golden Bull Award for "Best Quantitative Multi-Strategy Private Fund Manager" [44][45]

融通中证 A500 交易型开放式指数证券投资基金 基金管理人：融通基金管理有限公司 基金托管人：上海银行股份有限公司 送出日期：2025 年 8 月 28 日 融通中证 A500 交易型开放式指数证券投资基金 2025 年中期报告 基金管理人的董事会、董事保证本报告所载资料不存在虚假记载、误导性陈述或重大遗漏， 并对其内容的真实性、准确性和完整性承担个别及连带的法律责任。本中期报告已经三分之二以 上独立董事签字同意，并由董事长签发。 基金托管人上海银行股份有限公司根据融通中证 A500 交易型开放式指数证券投资基金（以下 简称"本基金"）基金合同规定，于 2025 年 8 月 26 日复核了本报告中的财务指标、净值表现、利 润分配情况、财务会计报告、投资组合报告等内容，保证复核内容不存在虚假记载、误导性陈述 或者重大遗漏。 基金管理人承诺以诚实信用、勤勉尽责的原则管理和运用基金资产，但不保证基金一定盈利。 基金的过往业绩并不代表其未来表现。投资有风险，投资者在作出投资决策前应仔细阅读本 基金的招募说明书。 本报告中财务资料未经审计。 本报告期自 2025 年 1 月 9 日（基金合同生效日）起至 6 月 30 ...

Core Viewpoint - The report provides a comprehensive overview of the performance and management of the Rongtong Juchao 100 Index Securities Investment Fund (LOF) for the first half of 2025, highlighting its investment strategy, financial performance, and market outlook. Fund Overview - Fund Name: Rongtong Juchao 100 Index Securities Investment Fund (LOF) [3] - Fund Manager: Rongtong Fund Management Co., Ltd. [3] - Fund Custodian: Industrial and Commercial Bank of China [3] - Total Fund Shares at Period End: 473,548,612.29 shares [3] - Fund's Investment Objective: To achieve capital appreciation by outperforming the Juchao 100 Index while controlling tracking error [3][5]. Financial Performance - Realized Income for the Period: CNY 9,750,489.37 for Class A/B and CNY 65,662.43 for Class C [5] - Profit for the Period: CNY 9,201,615.02 for Class A/B and CNY 53,937.28 for Class C [5] - Net Asset Value at Period End: CNY 477,515,976.79 for Class A/B and CNY 3,362,471.63 for Class C [5] - Net Asset Value Growth Rate: 2.01% for Class A/B and 1.84% for Class C [13] Investment Strategy - The fund employs an enhanced index strategy, aiming to control tracking error to the Juchao 100 Index within 0.5% while seeking to achieve excess returns [5][12]. - The fund utilizes a multi-factor quantitative selection model to enhance investment performance and manage risks [12]. Market Overview - The Chinese economy showed stability with a GDP growth rate of 5.3% in the first half of 2025, driven by consumption and high-end manufacturing [14]. - The market experienced a structural rally, with small-cap stocks outperforming larger ones, and liquidity conditions improving [14][15]. - The outlook for the second half of 2025 suggests a potential shift from liquidity-driven market dynamics to fundamentals-driven performance, with expectations of larger-cap stocks gaining favor [15][16]. Risk Management - The fund's risk management framework includes multi-dimensional constraints and a focus on maintaining a balanced exposure to various investment styles [12][16]. - The fund has not encountered any significant trading anomalies during the reporting period [12].

Core Viewpoint - The article discusses the rise of active quantitative funds in the A-share market, highlighting their ability to combine the advantages of both active and passive investment strategies, thus providing investors with opportunities for excess returns while maintaining a clear investment style [4][5][20]. Group 1: Active Quantitative Funds - Active quantitative funds have gained attention for their unique approach, which integrates mathematical models and vast data analysis, avoiding emotional trading and ensuring strict adherence to investment discipline [6][7]. - These funds offer a broader investment perspective, allowing for efficient stock selection across a larger pool, thus adapting better to rapid market changes [7][8]. - The article emphasizes the collaborative efforts of teams at Guangfa Fund, which have developed a multi-strategy investment system that combines subjective research with quantitative methods to enhance long-term excess returns [9][10]. Group 2: Guangfa Fund's Quantitative Strategy - Guangfa Fund has established a "multi-asset, multi-strategy, multi-team" framework for its quantitative business, with three core teams focusing on different aspects of quantitative investment [9][10]. - The Quantitative Investment Department, led by Zhao Jie, focuses on pure quantitative strategies, utilizing a factor library of approximately 600 effective factors to drive stable excess returns [10][12]. - The Active Quantitative Team, led by Yang Dong, bridges subjective and quantitative approaches, employing a human-machine collaboration model to capture stock alpha across various styles and industries [10][11]. - The Stable Strategy Department, led by Lin Yingrui, emphasizes risk control and value discovery, integrating active fundamental research with a scientific quantitative framework [11][12]. Group 3: Product Offerings and Performance - Guangfa Fund has launched a series of active quantitative products that cater to diverse investor preferences, focusing on style enhancement, industry themes, and risk management [13][17]. - The Small Cap Enhancement product, managed by Li Yuxin and Yi Wei, has achieved a return of 99.98% over the past year, showcasing a strong risk-return profile [14][16]. - The Growth Style Enhancement product, Guangfa Dongcai Big Data Selected, has delivered a return of 73.61%, leveraging internet big data to capture growth alpha [15][16]. - The Value/Dividend Style Enhancement products, such as Guangfa Stable Strategy and Guangfa High Dividend Preferred, have also shown significant returns, with the former achieving a 45.97% return over the past year [15][16]. Group 4: Future Outlook - The article posits that active quantitative investment will evolve from being a supplementary option in asset management to a primary strategy for navigating complex market trends [20][21]. - The integration of human insights with machine precision is expected to create a more robust investment approach, providing investors with a stable and predictable long-term investment path [21].

Quantitative Models and Construction Methods Model Name: Quantitative Alpha Model - **Model Construction Idea**: The model uses price and volume, high-frequency alpha factors as signals to calculate the overall score of each index[13] - **Model Construction Process**: - The model calculates the scores based on factors such as incremental funds entering the market (amt_mustd3m/turn_utd), intraday trading (tcv_intra), and interday stability - trading depth improvement (mom_mdr3m)[17] - Formula: $ \text{Score} = \text{amt_mustd3m/turn_utd} + \text{tcv_intra} + \text{mom_mdr3m} $ - The parameters represent the following: - amt_mustd3m/turn_utd: Incremental funds entering the market - tcv_intra: Intraday trading - mom_mdr3m: Interday stability - trading depth improvement - **Model Evaluation**: The model's internal selection has been pointing towards micro-cap stocks since September 2024, indicating a preference for micro-cap stocks[13][17] Model Name: Non-linear Market Cap Model - **Model Construction Idea**: The model evaluates the impact of non-linear market cap fluctuations on manager excess returns[20] - **Model Construction Process**: - The model calculates the average distance of scatter points from the origin during significant non-linear market cap fluctuations[20] - Formula: $ \text{Average Distance} = \frac{\sum \text{Distance from Origin}}{\text{Number of Points}} $ - The parameters represent the following: - Distance from Origin: The distance of each scatter point from the origin during non-linear market cap fluctuations - **Model Evaluation**: The model shows that managers' excess returns are significantly influenced by non-linear market cap fluctuations, with the average distance from the origin increasing from 0.84 in 2023 to 1.49 in 2024 and 1.19 in 2025[20][30] Model Name: Linear Market Cap Model - **Model Construction Idea**: The model evaluates the impact of linear market cap fluctuations on manager excess returns[26] - **Model Construction Process**: - The model calculates the average distance of scatter points from the origin during significant linear market cap fluctuations[26] - Formula: $ \text{Average Distance} = \frac{\sum \text{Distance from Origin}}{\text{Number of Points}} $ - The parameters represent the following: - Distance from Origin: The distance of each scatter point from the origin during linear market cap fluctuations - **Model Evaluation**: The model shows that managers' excess returns are influenced by linear market cap fluctuations, with the average distance from the origin increasing from 0.69 in 2023 to 1.05 in 2024 and 0.96 in 2025[26][32] Model Backtest Results Quantitative Alpha Model - **IR**: 0.55[17] - **Excess Return**: 1.9%[28] Non-linear Market Cap Model - **IR**: 1.19[30] - **Excess Return**: 1.9%[28] Linear Market Cap Model - **IR**: 0.96[32] - **Excess Return**: 0.08%[28] Quantitative Factors and Construction Methods Factor Name: Incremental Funds Entering the Market (amt_mustd3m/turn_utd) - **Factor Construction Idea**: Measures the amount of new funds entering the market[17] - **Factor Construction Process**: - Formula: $ \text{amt_mustd3m/turn_utd} $ - The parameters represent the following: - amt_mustd3m: Amount of new funds entering the market - turn_utd: Market turnover - **Factor Evaluation**: Indicates the improvement in the trading environment for micro-cap stocks[17] Factor Name: Intraday Trading (tcv_intra) - **Factor Construction Idea**: Measures the intensity of intraday trading[17] - **Factor Construction Process**: - Formula: $ \text{tcv_intra} $ - The parameters represent the following: - tcv_intra: Intraday trading volume - **Factor Evaluation**: Indicates the improvement in the trading environment for micro-cap stocks[17] Factor Name: Interday Stability - Trading Depth Improvement (mom_mdr3m) - **Factor Construction Idea**: Measures the stability and depth of trading over multiple days[17] - **Factor Construction Process**: - Formula: $ \text{mom_mdr3m} $ - The parameters represent the following: - mom_mdr3m: Momentum and trading depth over three months - **Factor Evaluation**: Indicates the improvement in the trading environment for micro-cap stocks[17] Factor Backtest Results Incremental Funds Entering the Market (amt_mustd3m/turn_utd) - **IR**: 0.55[17] - **Excess Return**: 1.9%[28] Intraday Trading (tcv_intra) - **IR**: 0.55[17] - **Excess Return**: 1.9%[28] Interday Stability - Trading Depth Improvement (mom_mdr3m) - **IR**: 0.55[17] - **Excess Return**: 1.9%[28]