Market Overview - The report indicates that major asset classes have shown positive performance, with Hong Kong stocks leading, followed by crude oil and A-shares. The A-share market saw the ChiNext Index increase by 3.17% [2][6] - The average stock position of all funds decreased to 78.89%, a drop of 1.38 percentage points from the previous week. However, the average position for ordinary equity funds rose to 83.57%, an increase of 0.90 percentage points [2][10] Equity Fund Performance - Passive index funds averaged an increase of 1.92%, while ordinary equity funds saw an average rise of 2.94%. Enhanced index funds increased by 1.49%, and flexible allocation funds rose by 2.19%. The best-performing fund in the passive category was the Invesco CSI Hong Kong Innovation Drug ETF, which surged by 13.98% [3][11] - The report highlights that the average performance of various equity fund types shows significant variability, with the top-performing funds in the ordinary equity category achieving a maximum increase of 17.61% [12][13] Fixed Income Fund Performance - Fixed income products also showed positive trends, with mixed bond funds averaging a 0.49% increase. The best performer in this category was the Huashang Dual-Wing Balanced Mixed A, which rose by 5.09% [14] - The report details that short-term pure bond funds had a minimal average increase of 0.05%, indicating a relatively stable performance in the fixed income market [14][15] Other Fund Categories - Commodity funds averaged a 0.51% increase, with the highest performer being the soybean meal ETF, which rose by 2.43%. International (QDII) funds performed well, averaging a 2.46% increase, with the top fund gaining 15.89% [15][16] - REITs funds remained stable, with the best performer showing a modest increase of 3.05% [16] Fund Market Dynamics - The report notes that 36 new funds were established this week, raising a total of 180.74 billion, which is a decrease compared to the previous week. Among these, 20 were equity funds, which raised the largest amount at 63.38 billion [18][19] - As of July 18, 2025, the total number of public funds reached 12,963, with a total net asset value of 33.78 trillion [21][22]

Core Viewpoint - Dividend strategies have gained market attention since last year, characterized by their defensive attributes and high dividend yields, making them attractive to investors. However, with the rise of technology and pharmaceutical sectors in 2025, growth stocks have overshadowed dividend assets, despite institutional investments still favoring dividend-related sectors, particularly in Hong Kong stocks [1]. Group 1: Market Performance and Trends - As of July 18, 2025, the total scale of listed dividend-themed ETFs has exceeded 150 billion, with 58 out of 61 ETFs achieving positive returns this year [1]. - The Hang Seng Hong Kong Stock Connect High Dividend Low Volatility Index has shown a year-to-date increase of 17.52%, benefiting the ETFs that track it [3]. - The largest ETF by scale, the E Fund Hang Seng Dividend Low Volatility ETF, has surpassed 30 billion in assets, demonstrating significant growth since its inception in April 2024 [3]. Group 2: ETF Characteristics and Performance - The top-performing ETFs are primarily those tracking the Hang Seng Hong Kong Stock Connect High Dividend Low Volatility Index, which selects stocks based on high dividend yields and low volatility [3][4]. - The China Securities Dividend Low Volatility Index, tracked by the largest dividend ETF, has maintained a consistent performance with a year-to-date return of 8.21% [9]. - The Morgan Hong Kong Dividend Index ETF, the first cross-border strategy ETF to exceed 10 billion in scale, has a year-to-date return of 17.79% [11]. Group 3: Sector Allocation and Composition - The financial sector accounts for over 30% of the index composition, followed by energy, real estate, and industrial sectors, each exceeding 10% [4]. - The index maintains a diversified approach, with no single stock exceeding 5% weight, ensuring a balanced exposure to various companies [4]. Group 4: Future Outlook and Valuation - Despite concerns over high relative valuations, the absolute valuations of major dividend low volatility indices remain around 7 times, indicating potential for long-term investment [14]. - The current market dynamics suggest a valuation recovery rather than a bubble, with stable dividend assets expected to retain their allocation value in the long term [14].

Group 1 - The core viewpoint of the news highlights the recent decline in the stock price of Jingbeifang Information Technology Co., Ltd., which has dropped by 2.60% over four consecutive trading days [1] - Jingbeifang is recognized as a leader in the fintech service sector, aiming to provide comprehensive and high-quality software and IT solutions to assist clients in their digital transformation [1] - In the second quarter of this year, the Invesco Great Wall Quality Evergreen Mixed A Fund entered the top ten shareholders of Jingbeifang, marking a new investment for the fund this year [1] Group 2 - The Invesco Great Wall Quality Evergreen Mixed A Fund has achieved a year-to-date return of 30.35%, ranking 356 out of 4,491 in its category [1] - The fund manager, Nong Bingli, has a strong background with experience in various financial institutions, including positions as a research analyst and fund manager [3][4] - Nong Bingli has been managing multiple funds, including the Invesco Great Wall Quality Evergreen Mixed A Fund since July 6, 2023, and has a cumulative management experience of over six years [4]

1. Report Industry Investment Rating No information provided in the content. 2. Core Viewpoints of the Report - In 2025, with policy focus, the "technology board" of the bond market has accelerated its progress. On July 17, the first batch of Sci - tech Bond ETFs were collectively listed. The Sci - tech Bond ETFs have investment value due to factors such as high - quality index components, better performance in bull and bear markets, spread compression potential, and suitability for certain types of investors [1][8][9]. - The Sci - tech Bond ETFs have differences from other mainstream ETFs in terms of sample range, sample rating, and sample remaining term, and the Shanghai AAA Sci - tech Bond Index has the characteristics of high return, low volatility, and low drawdown [4]. - The GF Shanghai AAA Sci - tech Innovation Corporate Bond ETF has features such as low credit risk, stable coupon income, and suitability for both stable - allocation investors and stock - bond portfolio rotation strategies [5]. 3. Summary According to the Catalog 3.1 How to Evaluate the Investment Value of Sci - tech Bond ETFs? 3.1.1 Index Component Structure - Among the 10 Sci - tech Bond ETF products, 6 track the China Securities AAA Sci - tech Bond Index, 3 track the Shanghai AAA Sci - tech Bond Index, and 1 tracks the Shenzhen AAA Sci - tech Bond Index. The Shanghai AAA Sci - tech Bond Index has better sample subject qualifications, a relatively larger sample size, and uses physical redemption [11]. - The industry distributions of the China Securities AAA Sci - tech Bond Index, Shanghai AAA Sci - tech Bond Index, and Shenzhen AAA Sci - tech Bond Index are similar and highly concentrated. The top four industries of the first two are industry, public utilities, energy, and materials, accounting for 90% of the total scale [13]. 3.1.2 Policy Attributes: Better Returns in Bull Markets and More Resilience in Bear Markets - Since 2025, in bear markets, the yield of sci - tech bonds has increased 3BP less than that of medium - term notes and short - term financing bills; in bull markets, it has decreased 6BP more. The policy attributes of sci - tech bonds provide strong support for their performance in bull - bear cycles [2][18]. 3.1.3 Allocation Demand: Room for Spread Compression - Currently, the 10 - year Treasury yield has been fluctuating narrowly in the range of 1.63% - 1.73% for over 2 months. The 3 - year and 5 - year AAA - rated sci - tech bonds still have an excess spread of 29BP and 5BP compared to medium - term notes of the same term and rating, and their yield levels are still attractive [2][22]. 3.1.4 High Correlation between Sci - tech Bond Index and Dividend Index: Suitable for Conservative Allocation - Oriented Investors - The Shanghai AAA Sci - tech Bond Index has a strong positive correlation with the Shanghai Dividend Index, with a correlation coefficient of 0.56. Sci - tech Bond ETFs are more suitable for conservative allocation - oriented investors and those who prefer stable high - coupon income [3][25]. 3.2 Comparison between Sci - tech Bond ETFs and Other ETFs 3.2.1 Comparison of Indexes Tracked by Mainstream ETFs - Sci - tech Bond ETFs, as a new listing category, differ from other mainstream ETFs in sample range, sample rating, and sample remaining term. For example, the ChinaBond 7 - 10 - year Policy Financial Bond Index and the Shanghai 10 - year Treasury Bond (Net) Index have no restrictions on sample ratings, while the Shanghai Market - Making Corporate Bond Index and the Shanghai AAA Sci - tech Bond Index require high - credit - rated bonds [30][31]. 3.2.2 The Index Tracked by Sci - tech Bond ETFs Has High Return, Low Volatility, and Low Drawdown - The Shanghai AAA Sci - tech Bond Index has the highest annualized return (5.41%), relatively low annualized volatility (1.02%), and relatively low maximum drawdown (- 1.42%) among the four indexes [38][39]. 3.3 Introduction to Sci - tech Bond ETF Products 3.3.1 Product Information of GF Sci - tech Bond ETF - The GF Shanghai AAA Sci - tech Innovation Corporate Bond ETF (fund code: 511120.SH) is a contract - type open - ended index fund tracking the Shanghai AAA Sci - tech Bond Index. As of July 17, 2025, its liquid scale is 5.172 billion yuan [5][43]. 3.3.2 Trading Mechanism: On - exchange Trading with "T + 0" Real - time Trading for Convenient Operation - The ETF supports on - exchange continuous trading, has no subscription and redemption limits, and allows "T + 0" real - time trading. It also has a cash dividend mechanism, providing flexibility and predictable cash flow for investors [46][47]. 3.3.3 The Index Tracked by the ETF Has Medium - to - Short Duration, High Credit Rating, High Industry Concentration, and Many Leading Enterprises - The Shanghai AAA Sci - tech Bond Index has a medium - to - short duration, with bonds with a maturity of less than 5 years accounting for 80% of the total scale and a weighted average duration of 4.40 years. The component bonds are mainly of high - credit rating, and the top four industries account for over 90% of the total scale [48][50][51]. 3.3.4 Good Liquidity and Low Correlation with Stocks Make It Suitable for Stock - Bond Rotation Strategies - The ETF is suitable for stable - allocation investors and stock - bond portfolio rotation strategies due to its low credit risk, stable coupon income, anti - drawdown ability, low correlation with the Shanghai Composite Index, and good secondary - market liquidity [5][55].

Group 1 - The core viewpoint is that state-owned enterprises (SOEs) in the cyclical resource sector are in a valuation trough, which is expected to be positively impacted by three major policy benefits: (1) anti-involution; (2) debt resolution; (3) infrastructure investment [11][13][15] - The Hong Kong dividend assets have a higher dividend yield compared to A-shares, with a long-term higher dividend premium [12][19] - The investment value of the National New Hong Kong Stock Connect SOE dividend strategy includes: (1) high dividend and low valuation, emphasizing absolute return attributes; (2) focusing on leading SOEs in petrochemicals, communications, transportation, and coal; (3) significant long-term return advantages; (4) long-term performance superior to the overall Hong Kong market, characterized by high dividends and high free cash flow [12][24][41] Group 2 - The industry distribution focuses on high-dividend SOEs in cyclical sectors, with significant weights in oil and petrochemicals (29%), communications (23%), transportation (14%), and coal (11%) [28][30] - The long-term performance of the National New Hong Kong Stock Connect SOE dividend index shows a cumulative increase of 118% since early 2017, closely approaching the 129% increase of the Hang Seng High Dividend Yield Index [5][36] - The constituent stocks of the National New Hong Kong Stock Connect SOE dividend index have outperformed the overall Hong Kong market, with an average net profit growth rate of 12% since 2015, significantly higher than the overall Hong Kong average of 4.7% [6][41] Group 3 - The Invesco Great Wall CSI National New Hong Kong Stock Connect SOE Dividend ETF (520990) is designed to closely track the performance of the CSI National New Hong Kong Stock Connect SOE Dividend Index, providing investors with a tool to invest in the Hong Kong SOE dividend sector [50][51] - The fund was established on June 26, 2024, and aims to minimize tracking deviation and error to achieve returns similar to the underlying index [50][51]

日期期间申购（亿份）期间赎回（亿份）期末总份额（亿份）期末净资产（亿元）净资产变动率2025- 06-300.000.010.020.02-24.80%2025-03-310.000.000.020.02-2.54%2024-12-310.000.020.020.02-47.96%2024- 09-300.000.020.040.04-27.77% 数据显示，该基金近3个月收益率0.32%，近一年收益率1.39%，成立以来收益率为63.56%。其股票持仓 前十分别为：中材国际，前十持仓占比合计0.32%。 天眼查商业履历信息显示，景顺长城基金管理有限公司成立于2003年6月，位于深圳市，是一家以从事 资本市场服务为主的企业。注册资本13000万人民币，法定代表人为李进。 来源：金融界 截至2025年6月30日，景顺长城基金旗下景顺货币A（260102）期末净资产566.05亿元，比上期减少 1.81%，该基金经理为陈威霖。 简历显示，陈威霖女士:硕士研究生,曾担任格兰达技术(深圳)有限公司研发部行政专员,平安利顺货币经 纪公司债券市场部债券经纪人。2013年6月加入景顺长城基金管理有限公司,历任交易管理部交易 ...

Group 1 - The core viewpoint is that Hong Kong's innovative drug concept stocks are becoming increasingly active, with significant gains in the net value of related ETFs [2] - As of July 18, the top ten passive index funds by weekly growth include various innovative drug ETFs, all showing over 13% increase [3] - The top-performing fund, Invesco Great Wall CSI Hong Kong Stock Connect Innovative Drug ETF Link A Fund, achieved a weekly growth of 13.98% [3] Group 2 - The Invesco Great Wall CSI Hong Kong Stock Connect Innovative Drug ETF Link Fund was established on March 14, 2025, with a net asset value of approximately 493 million yuan as of the end of Q2 [4] - The fund has underperformed its benchmark by 4.76 percentage points since inception but outperformed it by 3.69 percentage points in Q2 [4] - The only healthcare ETF among the top funds is the Fortune Heng Seng Hong Kong Stock Connect Healthcare ETF, which has underperformed its benchmark by 0.76 percentage points since inception and by 7.43 percentage points over the past year [5]

Quantitative Models and Construction Methods 1. Model Name: "Core + Satellite" Dual-Layer Framework - **Model Construction Idea**: The model aims to balance "stability" and "growth" by combining low-correlation asset classes to smooth volatility and achieve returns[4][53] - **Model Construction Process**: - **Core Layer**: Primarily consists of interest rate bonds, supplemented by credit bonds[53] - **Satellite Layer**: Includes high-volatility assets such as equities, overseas assets, and commodities. These assets are allocated across regions and strategies to reduce portfolio drawdowns and achieve stable returns[53] - **Model Evaluation**: The dual-layer structure effectively reduces portfolio volatility while maintaining stable returns, aligning with the fund's conservative investment objectives[4][53] 2. Risk Control System - **Model Construction Idea**: The primary goal is to limit maximum drawdowns to ensure principal safety while pursuing stable annualized returns[54] - **Model Construction Process**: - Strict risk control measures are implemented to ensure a positive return experience for investors at any entry point[54] - Risk budgets are adjusted dynamically to reduce exposure to asset classes with anticipated risks, avoiding "buying the dip" strategies[54] - **Model Evaluation**: The risk control system ensures a smooth investment experience, reducing irrational redemptions and maintaining portfolio management continuity[54] 3. Return Realization Path - **Model Construction Idea**: The model emphasizes a diversified approach to achieve returns through bonds, equities, and commodities[57] - **Model Construction Process**: - **Bond Investments**: Focus on government bonds, supplemented by credit bonds[57] - **Equity Investments**: Core allocation to passive funds, with at least 50% of total equity exposure. The remaining allocation is split between individual stocks and active funds, with intra-day stop-loss and stop-gain mechanisms[57] - **Commodity Strategy**: Utilized as a supplementary return source[57] - **Model Evaluation**: The diversified approach ensures stable returns while mitigating risks through dynamic adjustments and sector/region rotation[57] --- Model Backtesting Results 1. "Core + Satellite" Dual-Layer Framework - **Annualized Return**: 5.16% (A-class), 5.84% (Y-class)[24][28][32] - **Annualized Volatility**: 1.68% (A-class), 1.90% (Y-class)[24][28][32] - **Sharpe Ratio (IR)**: 2.61 (A-class), 2.23 (Y-class)[24][28][32] - **Maximum Drawdown**: -0.88% (both A-class and Y-class)[24][28][32] - **Calmar Ratio**: 5.66 (A-class), 6.33 (Y-class)[24][28][32] 2. Peer Comparison - **Annualized Return (Median)**: 4.76% (peer group)[49] - **Annualized Volatility (Median)**: 4.22% (peer group)[49] - **Sharpe Ratio (Median)**: 0.82 (peer group)[49] - **Maximum Drawdown (Median)**: -3.17% (peer group)[49] - **Calmar Ratio (Median)**: 1.53 (peer group)[49] 3. Performance Rankings - **Sharpe Ratio Ranking**: Top 1.20% among peers[49] - **Maximum Drawdown Ranking**: Top 2.40% among peers[49] - **Calmar Ratio Ranking**: Top 2.20% among peers[49] 4. Additional Metrics - **3-Month Positive Return Probability**: 100% for both A-class and Y-class[33] - **Longest Non-New High Period**: 42 days (A-class), 39 days (Y-class)[40][43] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Asset Allocation - **Factor Construction Idea**: Diversify across asset classes to reduce correlation and smooth portfolio volatility[53] - **Factor Construction Process**: - Allocate to low-correlation assets such as bonds, equities, and commodities[53] - Adjust allocations dynamically based on market conditions and risk assessments[54] - **Factor Evaluation**: The multi-asset allocation strategy effectively balances risk and return, ensuring stable performance across market cycles[53][54] --- Factor Backtesting Results 1. Multi-Asset Allocation Factor - **Annualized Return**: 5.16% (A-class), 5.84% (Y-class)[24][28][32] - **Annualized Volatility**: 1.68% (A-class), 1.90% (Y-class)[24][28][32] - **Sharpe Ratio (IR)**: 2.61 (A-class), 2.23 (Y-class)[24][28][32] - **Maximum Drawdown**: -0.88% (both A-class and Y-class)[24][28][32] - **Calmar Ratio**: 5.66 (A-class), 6.33 (Y-class)[24][28][32]

Core Viewpoint - The AI Fund, Invesco Great Wall Economic Preferred One-Year Holding Mixed A (017639), reported a profit of 8.96 million yuan for Q2 2025, with a net value growth rate of 6.71% for the period [2] Fund Performance - As of July 18, the fund's unit net value was 1.178 yuan, with a one-year compounded net value growth rate of 30.62%, the highest among its peers [2][3] - The fund's performance over the last three months showed a compounded net value growth rate of 14.55%, ranking 182 out of 607 comparable funds, and 10.81% over the last six months, ranking 302 out of 607 [3] Fund Management Insights - The fund manager indicated that external uncertainties are rising, and internal economic momentum requires continued fiscal and monetary policy support to boost domestic demand [2] - The report highlighted that after the export effect diminishes, external demand may weaken, impacting production and employment in export-related sectors, alongside pressures from declining housing prices affecting consumer spending [2] Fund Metrics - The fund's Sharpe ratio since inception is 0.5491, indicating a moderate risk-adjusted return [7] - The maximum drawdown since inception is 33.47%, with the largest quarterly drawdown occurring in Q1 2024 at 22.99% [9] Fund Holdings - As of Q2 2025, the fund's total assets amounted to 142 million yuan, with a historical average stock position of 86.5%, slightly above the peer average of 85.36% [12][13] - The top ten holdings include Guorui Technology, Sitaiwei, Zijin Mining, Nine Company, Jingzhida, Zhongtian Technology, Chongqing Rural Commercial Bank, Fujing Technology, Chip Source Micro, and Yun Aluminum [16]

Core Viewpoint - The AI Fund, Invesco Great Wall State-Owned Enterprise Value Mixed A (018294), reported a profit of 606,500 yuan for Q2 2025, with a weighted average profit per fund share of 0.0018 yuan. The fund's net value growth rate was 1.68%, and its total scale reached 295 million yuan by the end of Q2 2025 [3][16]. Fund Performance - As of July 18, the fund's unit net value was 1.295 yuan. The fund manager, Zou Lihua, oversees 10 funds, all of which have positive returns over the past year. The highest one-year return among these funds was 9.59% for Invesco Great Wall Cycle Select Mixed A, while the lowest was 0.86% for Invesco Great Wall Energy Infrastructure Mixed A [3]. - The fund's performance over different time frames includes a three-month net value growth rate of 7.45%, a six-month growth rate of 6.25%, and a one-year growth rate of 3.06%, ranking 51/82, 49/82, and 59/77 among comparable funds, respectively [4]. Risk and Return Metrics - The fund has a Sharpe ratio of 1.0531 since inception, indicating a favorable risk-adjusted return [9]. - The maximum drawdown since inception is 12.56%, with the largest quarterly drawdown occurring in Q3 2024 at 11.67% [12]. Investment Strategy - The average stock position of the fund since inception is 68.23%, compared to the industry average of 84.87%. The fund reached its highest stock position of 86.46% at the end of H1 2025 and its lowest of 59.42% at the end of H1 2024 [15]. - The fund has a high concentration of holdings, with the top ten stocks including Zijin Mining, China Mobile, Shenhuo Co., Tencent Holdings, China National Offshore Oil, Chuan Yi Co., Sinopharm, Zhuhai Mining, Yun Aluminum, and CRRC Corporation [19]. Market Outlook - The fund management anticipates that despite potential short-term economic pressures, the relatively loose policy environment may prevent the market from overly pricing in short-term weaknesses. The medium-term outlook suggests a stabilization of the domestic economy, with the negative impact of real estate on the economy potentially nearing its end, leading to a mild recovery in the fundamentals over the next six months [3].