Group 1: Fund Management Changes - Bank of China Fund announced the resignation of Chairman Zhang Yan due to work adjustments, effective June 16, with Executive President Zhang Jiawen acting as interim chairman [1] - Xinyuan Fund appointed two new deputy general managers: Zhang Pengfei and Yang Xiaoyu, both of whom joined the company in December 2022 [2] - Wu Huijuan resigned from all managed products at Green Fund due to personal reasons, with her management tenure being less than four months [4] Group 2: Fundraising and Market Activity - Dongfanghong Yingfeng Stable 6-Month Holding FOF announced an early end to its fundraising on June 17, with a maximum fundraising scale of 8 billion RMB [3] - The stock market showed a slight recovery with the Shanghai Composite Index up 0.04% and the Shenzhen Component Index up 0.24%, with total trading volume at 1.19 trillion RMB, a decrease of 161 billion RMB from the previous trading day [5] Group 3: ETF Market Insights - Energy Chemical ETF led the market with a gain of 2.82%, while several 5G and communication-related ETFs also performed well [5][6] - The aerospace sector is highlighted as a key beneficiary due to increasing military expenditures globally, with Chinese military enterprises gaining recognition in international arms trade [8] Group 4: Upcoming Fund Launches - Huatai Baoxing plans to launch a new index fund focused on the STAR Market, managed by Shang Shuo Hui, with a performance benchmark linked to the STAR Market Composite Index [9]

Quantitative Models and Construction Methods Hidden Risk Model - **Model Name**: Hidden Risk Model - **Construction Idea**: Funds with high correlation in net asset value (NAV) trends are likely exposed to similar risks (explicit or hidden). By regressing fund factors weighted by correlation with similar funds, hidden risks can be stripped away [3][68][69] - **Construction Process**: 1. Identify funds with high NAV correlation over the past year (top N funds, N=20) [70] 2. Calculate weighted daily returns of similar funds based on correlation [70] 3. Perform time-series regression of the target fund's daily returns against the weighted returns of similar funds. The intercept term represents the adjusted alpha factor [70] - Formula: $$R_{p}=\alpha+\beta\cdot SimiRet+\varepsilon_{p}$$ [90] - **Evaluation**: Provides higher explanatory power for fund returns compared to traditional models like Fama-Five-Factor [3][86][90] Hidden Risk-Adjusted Comprehensive Selection Factor - **Factor Name**: Hidden Risk-Adjusted Comprehensive Selection Factor - **Construction Idea**: Combine factors improved by hidden risk adjustments (e.g., Sharpe ratio, hidden trading ability) with original factors that do not require adjustment (e.g., reverse investment ability) [115][118] - **Construction Process**: 1. Adjust factors like Sharpe ratio and hidden trading ability using hidden risk regression [94][101] 2. Combine adjusted factors with original factors using equal weighting [115] - **Evaluation**: Improves stability and predictive power of selection factors, with RankICIR increasing significantly [107][118] Sharpe Ratio Factor Adjustment - **Factor Name**: Hidden Risk-Adjusted Sharpe Ratio Factor - **Construction Idea**: Adjust the original Sharpe ratio factor by regressing it against the weighted Sharpe ratios of similar funds [94] - **Construction Process**: 1. Calculate weighted Sharpe ratios of similar funds based on correlation [94] 2. Perform cross-sectional regression of the original Sharpe ratio against the weighted Sharpe ratios [94] - Formula: $$Sharpe=\alpha+\beta\cdot SimiSharpe+\varepsilon$$ [94] - **Evaluation**: Stability significantly improved, with RankICIR increasing from 0.77 to 1.99 [96][98] Hidden Trading Ability Factor Adjustment - **Factor Name**: Hidden Risk-Adjusted Hidden Trading Ability Factor - **Construction Idea**: Adjust the original hidden trading ability factor by regressing it against the weighted hidden trading ability of similar funds [101] - **Construction Process**: 1. Calculate weighted hidden trading ability of similar funds based on correlation [101] 2. Perform cross-sectional regression of the original hidden trading ability against the weighted hidden trading ability [101] - **Evaluation**: Stability significantly improved, with RankICIR increasing from 1.68 to 2.23 [102][106] --- Model Backtesting Results Hidden Risk Model - **RankIC Mean**: 92.32% [86] - **RankICIR**: Not explicitly mentioned - **RankIC Win Rate**: Not explicitly mentioned Hidden Risk-Adjusted Comprehensive Selection Factor - **RankIC Mean**: 13.99% [118][121] - **RankICIR**: 3.18 [118][121] - **RankIC Win Rate**: 93.01% [118][121] - **Annualized Excess Information Ratio**: 2.4 [3] Sharpe Ratio Factor Adjustment - **RankIC Mean**: 7.70% [98] - **RankICIR**: 1.99 [98] - **RankIC Win Rate**: 87.41% [98] Hidden Trading Ability Factor Adjustment - **RankIC Mean**: 7.21% [102] - **RankICIR**: 2.23 [102] - **RankIC Win Rate**: 90.21% [102] --- Factor Backtesting Results Hidden Risk-Adjusted Comprehensive Selection Factor - **RankIC Mean**: 13.99% [118][121] - **RankICIR**: 3.18 [118][121] - **RankIC Win Rate**: 93.01% [118][121] - **Quarterly Excess Return (Top Decile)**: 1.46% [118][121] - **Quarterly Long-Short Return**: 2.74% [118][121] Sharpe Ratio Factor Adjustment - **RankIC Mean**: 7.70% [98] - **RankICIR**: 1.99 [98] - **RankIC Win Rate**: 87.41% [98] - **Quarterly Excess Return (Top Decile)**: 0.86% [98] - **Quarterly Long-Short Return**: Not explicitly mentioned Hidden Trading Ability Factor Adjustment - **RankIC Mean**: 7.21% [102] - **RankICIR**: 2.23 [102] - **RankIC Win Rate**: 90.21% [102] - **Quarterly Excess Return (Top Decile)**: 0.92% [102] - **Quarterly Long-Short Return**: Not explicitly mentioned --- FOF Portfolio Construction Results Hidden Risk-Adjusted Comprehensive Selection Factor-Based Portfolio - **Annualized Excess Return**: 8.86% [147] - **Annualized Tracking Error**: 3.52% [147] - **Excess Information Ratio**: 2.31 [147] - **Maximum Relative Drawdown**: 3.40% [147] - **Relative Return-to-Drawdown Ratio**: 2.61 [147] - **Monthly Win Rate**: 75.91% [147]

Group 1 - The FOF (Fund of Funds) market has seen a significant surge in new issuances this year, with a 166.67% increase in the number of new FOFs and a staggering 1346.12% increase in fundraising scale compared to the previous quarter, indicating growing investor recognition [1] - FOFs originated in the late 1980s in the United States and have played a crucial role in the U.S. pension system through their dual advantages of "fund selection + asset allocation" over the past 50 years [1] - In China, FOFs are still in the development stage but are gaining popularity among investors due to their convenience, reasonable asset allocation, and suitability for long-term, stable pension investment needs [1] Group 2 - By 2035, China is expected to enter a phase of severe aging, leading to an increasing demand for pension investments, with FOFs emerging as a preferred choice for investors due to their multiple advantages [1] - FOFs primarily invest in a basket of funds and utilize a large asset allocation strategy for diversification, allowing for dynamic adjustments based on market conditions to navigate complex and changing market environments [1] Group 3 - Since 2020, the FOF fund index has increased by 17.44%, outperforming the Wande Balanced Mixed Fund Index and the CSI 300 Index, while maintaining a lower annualized volatility of 7.31% compared to the same indices, demonstrating a strong ability to balance returns and risks [2] - The Minsheng Jianyin's Kangning Stable Pension Target One-Year Holding Period FOF is designed for investors with pension needs or those seeking long-term stable investments, with over 80% of its assets invested in funds and a maximum equity asset allocation of 30% [2] - This fund employs a target risk strategy to optimize the investment process, limiting expected volatility and downside risk, which has resulted in a notable performance, ranking 8th among 77 similar funds in terms of net asset value growth rate over the past year [2]

Key Insights - The report highlights a significant increase in China's inverter exports in April 2025, with an export value of 5.82 billion, representing a month-on-month growth of 28.0% and a quantity of 4.5292 million units, up 21.4% from the previous month [3] - The average export price of inverters rose to 1,284 yuan per unit, marking a month-on-month increase of 5.5% [3] - The demand growth is attributed to the onset of the summer peak season for household storage and accelerated overseas large-scale storage construction [3] - The report emphasizes the long-term drivers of demand, including power outages, rising electricity prices, and significant growth in wind and solar installations, indicating a shift in inverter demand towards emerging markets [3] - The report recommends companies such as Deye Technology, Sungrow Power Supply, and Sunshine Power for their potential in the inverter market [3] Industry Overview - The macroeconomic context suggests that the Chinese yuan may have upward momentum against the US dollar, driven by structural rebalancing in global asset allocation and diminishing depreciation expectations for the yuan [2] - The report notes that the US has adjusted tariffs on imports, which has implications for global trade dynamics and may affect the competitiveness of Chinese exports [2] - The report also discusses the ongoing trends in the logistics and e-commerce sectors, highlighting a continued focus on price competition and volume growth in the express delivery market, with a year-on-year increase in express delivery volume of 19.1% in April [7] - In the consumer electronics sector, the report indicates strong sales growth for robotic vacuum cleaners and floor washers, driven by promotional activities and consumer demand, with sales volume for robotic vacuum cleaners increasing by 74.49% year-on-year in April [8]

Group 1 - The market is unpredictable, with fluctuations in gold prices and stock indices, indicating a shift in investment dynamics [1] - Current gold prices are approximately $3100+ per ounce, while the stock index is around $3300+, leading to speculation about future price adjustments [1] - Institutional investors are considered "smart money," and their holdings in funds are a key metric for selection [2] Group 2 - A total of 34 actively managed equity funds were filtered based on criteria such as size (between 500 million to 5 billion), performance (over 20% in the last three years and over 5% this year), and institutional ownership (over 60%) [2] - The list of selected funds includes various performance metrics, with some funds showing significant returns over the past year and three years [3][4] Group 3 - The analysis includes a focus on industry-specific funds, with a notable presence of sectors like electronics, pharmaceuticals, finance, and internet [5] - Fund managers with a longer management tenure are preferred, as they provide stability and consistent performance [6] - Attention is drawn to funds with high single investor ownership, as this can indicate potential risks related to liquidity and fund management [7]

Core Viewpoint - The FOF (Fund of Funds) portfolios reveal a preference for low-cost, clearly defined index funds and strong-performing active equity funds, indicating a trend in investment strategies among FOFs [1] Group 1: FOF Holdings Overview - As of the end of 2024, there are 1,257 active equity funds held by FOFs, with 121 funds having holdings exceeding 50 million shares [1] - In terms of market value, 100 funds have a holding value exceeding 100 million yuan [1] - Large fund companies with comprehensive product offerings, such as GF Fund, have multiple funds in FOF portfolios, indicating recognition of their active stock investment capabilities [1] Group 2: GF Fund Performance - GF Fund has five funds that meet both the criteria of FOF holdings exceeding 50 million shares and a market value exceeding 100 million yuan [2] - The top fund, GF Industry Leading A, has the highest FOF holdings and market value, with 180 million shares held by 15 FOFs, totaling a market value of 310 million yuan [2] - The fund manager, Cheng Kun, has achieved a return of 51.49% since taking over the fund on May 31, 2019, with a benchmark outperformance of 38.59% [2] Group 3: Investment Style of Cheng Kun - Cheng Kun is characterized as a classic value investor, focusing on undervalued assets with a long-term perspective [3] - His investment strategy includes a high stock position, typically maintaining over 90%, with a balanced industry allocation across various sectors [3] - As of the end of 2024, nine of his top holdings have been held for at least four quarters, with four held for over two and a half years, reflecting a preference for long-term investments [3]