Core Viewpoint - The public fund market is experiencing a significant decrease in new fund subscriptions, with a 45.45% week-on-week decline in the number of funds available for subscription from October 20 to October 26, 2023, and an average subscription period extended to 27.8 days [1]. Fund Type Analysis - A total of 30 public funds are open for subscription this week, with 23 of them being equity funds, accounting for 76.67% of the total [2][3]. - Among the equity funds, passive index funds are particularly popular, with 11 out of 15 stock-type funds being passive index funds, representing 73.33% of the stock-type total [3]. - The bond fund issuance remains stable, with 3 new bond funds opening for subscription this week, maintaining the same level as the previous week [3]. - QDII (Qualified Domestic Institutional Investor) funds have seen a slight recovery, with 1 new QDII fund opening for subscription this week, compared to none the previous week [3]. Institutional Participation - This week, 27 public fund institutions have new funds available for subscription, with 24 institutions offering only 1 new fund each, while 3 institutions have 2 new funds [3]. - Notably, Huatai-PineBridge Fund and Harvest Fund each launched 2 new funds, with a mix of mixed and stock-type funds [3]. Market Sentiment and Strategy - The overall issuance of public products has cooled down, attributed to two main factors: market sentiment pressure due to high previous gains in the technology growth sector and a stable issuance strategy from public funds [3]. - Investors are showing concerns about the sustainability of earnings, leading to a contraction in risk appetite, which has resulted in fewer new products being launched and longer fundraising periods [3].

Report Summary 1. Investment Rating There is no information about the industry investment rating in the report. 2. Core View Last week (10/13 - 10/17), bond - type ETFs had a net capital outflow of 13.36 billion yuan. Convertible bond ETFs had a large drawdown, while the net values of credit bond and interest - rate bond ETFs showed marginal recovery. There was no new issuance of bond ETFs. The market values of interest - rate, credit, and convertible bond ETFs all decreased compared to the previous week. The average trading price of credit bond ETFs was lower than the fund's unit net value, indicating low allocation sentiment. The weekly turnover rates of all three types of products increased significantly [2][13][17]. 3. Section Summaries 3.1 Issuance Progress Tracking - No new bond ETFs were issued last week [17]. 3.2 Stock Product Tracking - As of October 17, 2025, the circulating market values of interest - rate bond ETFs, credit bond ETFs, and convertible bond ETFs were 134.1 billion yuan, 368.2 billion yuan, and 66 billion yuan respectively, with credit bond ETFs accounting for 64.8% of the total. Haifutong China Short - term Financing ETF and Bosera Convertible Bond ETF had the top two circulating market values [19]. - Compared to the previous week, the circulating market values of interest - rate bond ETFs, credit bond ETFs, and convertible bond ETFs decreased by 3.76 billion yuan, 3.41 billion yuan, and 2.16 billion yuan respectively. Products with a market value reduction of over 1.5 billion yuan last week included Haifutong China Short - term Financing ETF, Bosera Convertible Bond ETF, etc. [21]. - Among credit bond ETFs, the circulating market values of benchmark - making credit bond ETFs and science - innovation bond ETFs were 122 billion yuan and 246.5 billion yuan respectively, decreasing by 450 million yuan and 5.18 billion yuan compared to the previous week [24]. 3.3 ETF Performance Tracking - Recently, the market has been oscillating within a range. In the past two weeks, the cumulative unit net values of interest - rate bond ETFs and credit bond ETFs closed at 1.18 and 1.02 respectively [27]. - As of October 17, based on February 7 as the base date, the average cumulative return of benchmark - making credit bond ETFs rose to 0.42%. Based on July 17 as the base date, the cumulative return of science - innovation bond ETFs marginally recovered to - 0.30% but still remained in the negative range [32]. 3.4 Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit bond ETFs, interest - rate bond ETFs, and convertible bond ETFs were - 0.15%, + 0.001%, and + 0.04% respectively. The average trading price of credit bond ETFs was lower than the fund's unit net value, indicating low allocation sentiment. Specifically, the average weekly premium/discount rates of benchmark - making credit bond ETFs and science - innovation bond ETFs were - 0.21% and - 0.15% respectively [37]. 3.5 Turnover Rate Tracking - Last week, the turnover rate of interest - rate bond ETFs > credit bond ETFs > convertible bond ETFs. The weekly turnover rates of all three types of products increased significantly, rising to 187%, 143%, and 109% respectively. Specifically, interest - rate bond ETFs such as Haifutong Shanghai 5 - year Local Government Bond ETF and Huaxia Shanghai Benchmark - making Treasury Bond ETF had relatively high turnover rates [42].

Quantitative Models and Construction Methods - **Model Name**: Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy **Model Construction Idea**: The model explores the impact of China's macroeconomic factors on the overall performance and trends of Hong Kong-listed consumer companies, using dynamic macro event factors to construct a timing strategy framework [2][3][20] **Model Construction Process**: 1. **Macro Data Selection**: Select 20+ macroeconomic indicators across four dimensions: economy, inflation, currency, and credit, such as PMI, PPI, M1, etc [21][23] 2. **Data Preprocessing**: - Align data frequency to monthly frequency by either taking the last trading day of the month or calculating the monthly average for daily data - Fill missing values using the median of the first-order difference of the past 12 months added to the previous value $ X_{t}=X_{t-1}+Median_{diff12} $ [27] - Apply filtering using one-sided HP filter to avoid future data leakage $ \hat{t}_{t|t,\lambda}=\sum\nolimits_{s=1}^{t}\omega_{t|t,s,\lambda}\cdot y_{s}=W_{t|t,\lambda}(L)\cdot y_{t} $ [28] - Derive factors using transformations such as year-on-year, month-on-month, and moving averages [29] 3. **Macro Event Factor Construction**: - Determine event breakthrough direction by calculating the correlation between data and next-period asset returns - Identify leading or lagging relationships by deriving lagged event factors (0-4 periods) and selecting the most suitable lag period - Generate event factors using three types: data breaking through moving average, data breaking through median, and data moving in the same direction, with different parameters (e.g., moving average length: 2-12, rolling window: 2-12, same direction period: 1-5) [30][32] 4. **Event Factor Evaluation and Screening**: - Use two metrics: win rate of returns and volatility-adjusted returns during opening positions - Initial screening criteria: t-test significance at 95% confidence level, win rate >55%, occurrence frequency > rolling window period/6 [31][32] 5. **Combining Event Factors**: Select the highest win rate event factor as the base factor, then combine it with the second-highest win rate factor with a correlation <0.85. If the combined factor improves the win rate, it is selected; otherwise, the base factor is used [33] 6. **Dynamic Exclusion**: If no event factor passes the screening, the macro indicator is marked as empty for the period and excluded from scoring [33] 7. **Optimal Rolling Window Determination**: Test rolling windows of 48, 60, 72, 84, and 96 months to find the most suitable parameter for each macro indicator based on volatility-adjusted returns during opening positions [33] 8. **Final Macro Indicators**: Five macro factors were selected based on their performance in the sample period: - PMI: Raw Material Prices (96-month rolling window) - US-China 10Y Bond Spread (72-month rolling window) - Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY (48-month rolling window) - M1: YoY (48-month rolling window) - New Social Financing: Rolling 12M Sum: YoY (96-month rolling window) [34][35] 9. **Timing Strategy Construction**: - If >2/3 of factors signal bullishness, the category factor signal is marked as 1 - If <1/3 of factors signal bullishness, the category factor signal is marked as 0 - If the proportion of bullish signals falls between these ranges, the category factor is marked with the specific proportion - The score of each category factor is used as the timing position signal for the period [3][35] **Model Evaluation**: The strategy effectively captures systematic opportunities and avoids systematic risks, demonstrating superior performance compared to the benchmark in terms of annualized returns, maximum drawdown, Sharpe ratio, and return-drawdown ratio [2][3][20] --- Model Backtesting Results - **Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy** - **Annualized Return**: 10.44% - **Annualized Volatility**: 18.47% - **Maximum Drawdown**: -29.72% - **Sharpe Ratio**: 0.59 - **Return-Drawdown Ratio**: 0.35 [2][11][22] --- Quantitative Factors and Construction Methods - **Factor Name**: PMI: Raw Material Prices **Factor Construction Idea**: Use raw data to capture macroeconomic trends affecting asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] - **Factor Name**: US-China 10Y Bond Spread **Factor Construction Idea**: Reflect the impact of interest rate differentials on asset returns [35] **Factor Construction Process**: Utilize raw data with a 72-month rolling window [35] - **Factor Name**: Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY **Factor Construction Idea**: Measure credit expansion and its influence on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: M1: YoY **Factor Construction Idea**: Capture monetary supply changes and their impact on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: New Social Financing: Rolling 12M Sum: YoY **Factor Construction Idea**: Reflect credit growth and its effect on asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] **Factor Evaluation**: The selected factors demonstrated strong performance in the sample period, with high win rates and volatility-adjusted returns during opening positions [34][35] --- Factor Backtesting Results - **PMI: Raw Material Prices** - **Rolling Window**: 96 months [35] - **US-China 10Y Bond Spread** - **Rolling Window**: 72 months [35] - **Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY** - **Rolling Window**: 48 months [35] - **M1: YoY** - **Rolling Window**: 48 months [35] - **New Social Financing: Rolling 12M Sum: YoY** - **Rolling Window**: 96 months [35]

Core Viewpoint - The launch and promotion of new floating fee rate products in the public fund industry represent a significant innovation in fee structures, embodying the core value of "investor-centric" and driving high-quality development in the industry [1][2]. Group 1: New Floating Fee Rate Products - The new floating fee rate products initiated by 26 institutions, including Harvest Fund, mark an important step towards a performance-based management fee model, optimizing fund operation [2]. - These products feature a tiered management fee structure of 1.2% (benchmark), 1.5% (upward adjustment), and 0.6% (downward adjustment), linking management fees directly to investors' actual returns [2]. - The mechanism ensures that fund managers can only earn higher fees if investors achieve returns exceeding market benchmarks, thereby tightly aligning the interests of fund managers and investors [2][3]. Group 2: Impact on the Fund Industry - The introduction of floating fee rate products reshapes the public fund industry ecosystem, breaking the traditional fixed fee model and dynamically matching management costs with investment returns [3]. - This model encourages fund companies to focus on building robust research and investment systems, talent development, and risk control, enhancing their competitive edge [3]. - The shift from a "scale-driven" to a "trust-driven" and "capability-driven" development paradigm is a critical step towards creating sustainable and widely recognized "new value" in the industry [3][4]. Group 3: Future Outlook - The company plans to further leverage its platform-based, team-oriented, and integrated multi-strategy research and investment capabilities to systematically reshape product design, investment management, and client service logic [4]. - The maturation and popularization of these products are expected to foster a more resilient, responsible, and valuable public fund industry ecosystem [4].

Report Investment Rating - The report gives a "★☆☆" rating to CITIC's five-style stability, indicating a slightly bullish view with limited operability in the market [5]. Core Viewpoints - In the week ending October 17, 2025, Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index had weekly returns of -3.39%, 0.21%, and -1.14% respectively. In the public fund market, equity long strategies retreated, pure bonds outperformed, neutral strategy products showed mixed performance, and among commodities, precious metal ETFs rose while non-ferrous metal ETFs declined, and energy chemical and soybean meal ETFs continued to weaken [5]. - Among CITIC's five styles, the financial style rose last week while others fell. The style rotation chart shows that the growth and consumption styles weakened marginally in terms of relative strength, and the financial style increased significantly in terms of indicator momentum. In the public fund pool, cyclical style funds had better excess performance in the past week, and other style funds underperformed the index on average. The product's deviation from cyclical and consumption styles increased marginally, and the overall market congestion indicator increased marginally this week, with the cyclical style currently in a historically high congestion range [5]. - In the neutral strategy, the stock index basis showed a marginal recovery trend last week. The IM contract rebounded from below the -2 standard deviation of the three - month average to within one standard deviation, and the premium rates of the corresponding spot index ETFs of IH and IF were in the top 20% quantile range of the past three months [5]. - Among Barra factors, the residual momentum factor had better performance in the past week with a weekly excess return of 2.49%, while the momentum and capital flow factors had excess drawdowns. The win - rates of the profitability and leverage factors improved. The cross - section rotation speed of factors increased significantly this week and is currently in a relatively high quantile range in the past year [5]. - According to the latest scoring results of the style timing model, the consumption and financial styles recovered marginally this week, the cyclical style declined, and the current signal favors the stable style. The return of the style timing strategy last week was 0.52%, with an excess return of 1.45% compared to the benchmark equal - weighted allocation [5]. Summary by Directory Fund Market Review - In the public fund market, equity long strategies had a drawdown in the past week, pure bonds had better returns, neutral strategy products showed mixed performance, precious metal ETFs in commodities had large increases, non - ferrous metal ETFs had a return correction, and energy chemical and soybean meal ETFs' net values continued to weaken [5]. - Among CITIC's five styles, the financial style rose last week while others fell. Cyclical style funds had better excess performance in the public fund pool, and other style funds underperformed the index on average. The product's deviation from cyclical and consumption styles increased marginally, and the overall market congestion indicator increased marginally this week, with the cyclical style in a historically high congestion range [5]. - In the neutral strategy, the stock index basis recovered marginally last week, and the premium rates of the corresponding spot index ETFs of IH and IF were in the top 20% quantile range of the past three months [5]. - Among Barra factors, the residual momentum factor had a weekly excess return of 2.49%, the momentum and capital flow factors had excess drawdowns, and the win - rates of the profitability and leverage factors improved. The factor cross - section rotation speed increased significantly and is in a relatively high quantile range in the past year [5]. - According to the style timing model, the consumption and financial styles recovered marginally this week, the cyclical style declined, and the style timing strategy had a return of 0.52% last week, with an excess return of 1.45% compared to the benchmark [5]. Recent Market Returns - The weekly, monthly, quarterly, and semi - annual returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond (net), and Nanhua Commodity are presented in the report, along with data on the establishment scale of public funds in the past year, the maximum drawdown of major public fund strategy indices in the past three months, and the weekly returns of major public fund strategy indices [7]. CITIC Style Index - The net value trends of CITIC's financial, cyclical, consumption, growth, and stable style indices are shown, as well as the relative rotation chart of these style indices, which reflects the relative strength and momentum of different styles in different time periods [8][9]. - The excess return performance of CITIC style - based fund style indices in different time periods (weekly, monthly, quarterly, semi - annual, annual) is presented, along with the congestion levels of different styles (excluding the stable style due to data limitations) [10][11]. Barra Factors - The preference levels of Barra single - factors (ranging from 0 - 1) are shown, indicating the degree of preference for different factors. The excess return performance of Barra single - factor style strategies in different time periods (weekly, monthly) is also presented, as well as the excess net value trends of Barra single - factor styles since this year [13][14][17].

Group 1: Market Performance - On October 20, multiple Nikkei ETFs led the market with significant gains, particularly the Nikkei ETF (513520) which rose by 6.57% [3][4] - The A-share market experienced a rebound, with the Shanghai Composite Index increasing by 0.63%, the Shenzhen Component Index by 0.98%, and the ChiNext Index by 1.98% [3] - Several AI-focused ETFs on the ChiNext also saw gains exceeding 3% [3] Group 2: ETF Trading Activity - The Short-term Bond ETF (511360) recorded the highest trading volume in the market on October 20, with a transaction amount of 378.90 billion yuan [7][8] - Three technology innovation bond ETFs had active trading, each exceeding 100 billion yuan in transaction volume [7] Group 3: Gold ETFs and Market Trends - Gold ETFs continued to attract significant capital inflows in October, with notable net inflows recorded for several gold ETFs [9][10] - The gold stock ETF (517400) led the market in declines, dropping by 4.71% on October 20, amidst a broader downturn in gold-related stocks [5][6] - Analysts indicated that the recent pullback in precious metals was driven by both fundamental and technical factors, including overbought conditions and a decrease in market risk aversion [5][6] Group 4: Future Market Outlook - The U.S. is set to release the September CPI data, which is crucial for Federal Reserve monetary policy decisions, with expectations of continued volatility in precious metals [6] - Investment firms suggest that the equity market still presents structural investment opportunities, with a focus on technology innovation and cyclical assets [11]

整体来看，本周共30只公募基金开启首发。除20日发售的26只基金外，还有10月22日及23日开启发行的 4只基金，包括国投瑞银上证综合指数增强型基金、银华创业板综合ETF联接基金、中信保诚消费机遇 混合型发起式、华泰柏瑞盈泰稳健3个月持有期混合FOF。 指数基金募集火热 新基金火热发行，反映出权益市场向好、市场情绪回暖趋势。实际上，自2024年9月以来，新基金的发 行数量呈明显上升趋势。数据显示，2024年9月，共66只新基金发行，其中，股票型基金35只、混合型 基金9只。2025年9月，新基金发行数量达到151只，其中股票型基金77只、混合型基金25只，同比均大 幅增长。同时，股票型基金、混合型基金的平均发行份额整体也呈现上升趋势。 从总募集规模来看，9月结束募集的指数基金发行规模创下今年以来新高。据上海证券基金评价研究中 心统计，9月公募基金募集规模为1521.03亿元，环比增加26.99%，同比大增90.62%。其中，募集规模前 三的基金类型分别为：指数基金1079.04亿元，混合基金228.87亿元，债券基金127.39亿元。此外，近一 年指数增强基金的发行规模持续增长，月发行规模已从2024年10月 ...

Core Insights - The personal pension fund market has shown significant recovery in 2023, with an average return of 15.46% for existing funds, a notable increase from 3.12% at the end of Q2 [3][5] - A majority of funds established in late 2022 have turned positive, with 96% of the 132 products showing positive cumulative returns [4][5] - Despite the positive performance, many funds still struggle with low asset sizes, with over half having less than 10 million yuan, leading to several funds being forced to liquidate [6][7] Performance Recovery - The personal pension fund market has transitioned from a phase of losses to one of gains, with nearly all existing funds showing positive returns as of mid-October 2023 [3][4] - The best-performing fund, Tianhong Zhongzheng Kechuang Chuangye 50 ETF, has seen a return of 46.37% year-to-date [3] - Long-term performance has improved significantly, with the average return of funds established in late 2022 rising from -0.48% to 11.58% [4] Market Dynamics - The total scale of personal pension funds reached 12.405 billion yuan by the end of Q2 2023, reflecting a 35.65% increase from the previous year [5] - The number of personal pension funds has expanded to over 300, with a diverse range of products now available to investors [6][9] - Major fund management companies like E Fund and Huaxia Fund dominate the market, with significant inflows contributing to their growth [7] Challenges Ahead - Despite the positive trends, the issue of low fund sizes persists, with many funds at risk of liquidation due to not meeting the minimum asset requirements [7][8] - Investor awareness and understanding of personal pension products remain low, impacting participation rates [9][10] - The industry faces challenges in marketing and promoting these products effectively, as many investors are still hesitant due to past performance volatility [9][11] Recommendations for Improvement - Industry experts suggest enhancing investor education and simplifying the onboarding process to increase participation in personal pension funds [10][11] - There is a call for fund companies to focus on long-term investment strategies and to engage directly with potential investors to build trust [10][11] - Developing targeted products that cater to different professions and risk appetites could help in attracting a broader investor base [11]

Group 1 - The core viewpoint of the articles highlights the increased distribution of "red envelopes" or cash dividends by public funds, particularly by Jiashi Fund, to enhance investor experience and satisfaction [1][3] - Jiashi Fund announced a total of 11 products for profit distribution, with 10 of them having three or more dividends per year, showcasing the diversity, sustainability, and stability of their products [1] - The Jiashi Ultra Short Bond Fund, the first short bond fund in China, is among the products set for distribution, having achieved a total of 195 dividend distributions since its inception [1] Group 2 - The A500 ETF Jiashi (159351) has announced a quarterly dividend of 0.0750 yuan per 10 fund shares, marking its fourth cumulative dividend since inception [2] - The cash flow ETF Jiashi (159221) will distribute its first dividend of 0.1750 yuan per 10 fund shares, following its quarterly dividend mechanism [2] - Jiashi Theme Selection Mixed Fund (070010) leads in total dividend amount among Jiashi Fund products, with nearly 30 distributions and over 11.1 billion yuan in total dividends since its establishment [2] Group 3 - The trend of increasing stability, sustainability, and predictability of dividends from listed companies is being mirrored in public funds, driven by regulatory improvements in dividend mechanisms [3] - Public funds are actively enhancing their "investor return" orientation, transitioning towards high-quality development, with a continuous increase in total dividend amounts and frequencies benefiting investors [3]

Core Viewpoint - The article discusses the increasing differentiation among personal pension fund products as they achieve both performance and scale, highlighting the need for investors to make informed choices as the year-end investment window approaches [5]. Group 1: Expansion of Personal Pension Funds - The personal pension fund catalog expanded again in the third quarter, reaching a total of 302 products by the end of September, with 8 new additions compared to the end of the second quarter [3][4]. - The new entrants predominantly feature index-enhanced funds, with five out of the eight new products focusing on tracking the CSI 500 and CSI 300 indices [6][7]. Group 2: Performance of Personal Pension Funds - Personal pension funds have shown impressive performance this year, with an average net value growth exceeding 15% and the highest return reaching 46% [4][10]. - As of October 17, only one out of 302 personal pension funds reported negative returns this year, while the average unit net value increase was 15.13% [10]. Group 3: Market Dynamics and Investor Behavior - The growth in personal pension fund scale is accompanied by a notable differentiation among products, with only one fund exceeding 1 billion yuan in scale, while most remain below 200 million yuan [12]. - Investors are increasingly favoring funds with lower risk levels and shorter holding periods, reflecting a preference for more flexible or conservative allocations [12]. Group 4: Investment Strategies and Future Outlook - As the fourth quarter approaches, it is considered a critical period for personal pension account funding and product allocation, prompting investors to reassess their portfolios [14]. - Analysts suggest a balanced allocation between stocks and bonds, focusing on sectors aligned with national long-term development strategies, such as technology innovation and high-end manufacturing [14][15]. - The personal pension market is expected to continue evolving, with potential inclusion of more diverse asset classes like public REITs in the future [15][16].