◎记者 赵明超 指数化投资趋势显著，国内ETF总规模超5万亿元，其中超百只ETF规模突破100亿元。在业内人士看 来，ETF已成为资本市场重要稳定器，生态圈持续完善，为投资者提供丰富的资产配置选择，随着ETF 产品谱系的持续拓宽，未来发展空间十分广阔。 ETF发展迈上新台阶 ETF发展驶入新阶段。据Choice测算，截至8月25日，全市场ETF数量达1273只，规模合计为5.07万亿 元，较去年底增长超1.3万亿元。这是ETF总规模首次突破5万亿元大关。 从ETF的发展历程来看，2004年，首只ETF宣告成立，标志着我国ETF市场正式启航。历经16年发展， 2020年10月，ETF规模首次突破1万亿元。此后ETF发展显著提速，跨过万亿元整数关口时间不断缩 短。 具体来看：2023年8月，ETF规模突破2万亿元；2024年9月，突破3万亿元；今年4月，突破4万亿元；截 至今年8月25日，突破5万亿元。 值得一提的是，百亿级ETF阵营持续扩容。截至去年底，共有66只ETF规模超百亿元，而到了今年8月 25日，这一数字为101只，更有7只产品规模在千亿元以上。从千亿级ETF来看，均为宽基ETF。其中， 华泰柏瑞沪深 ...

范雨露 制图 公募固收+"搭桥" 居民"存款搬家"有新路径 ◎记者 陈玥 "存款搬家"新路径 沪上一家大型基金公司市场部人士介绍："今年以来，全行业都在推'固收+'产品，因为渠道需求增长非 常显著。" 天风证券研究表明，基金回本后，如果出现5%以上回撤、且回撤后一个月内净值创新高，后续两个季 度投资者将出现明显超额净申购行为。后续基金批量回本后，如果"恰好"出现回调行情，然后继续新 高，那么更多增量资金值得期待。 资产配置"旧爱与新宠" A股市场走势活跃，在银行渠道的推动下，不少资金正在通过公募"固收+"产品流入市场，间接配置权 益资产。 随着权益资产吸引力进一步增强，更多资金正在路上。 兴证研究统计数据显示，今年以来，理财产品规模增长低于去年同期，尤其是权益市场上涨后，理财规 模下降进一步提速，或表明居民存款正从理财产品向权益资产进行"再配置"。此外，7月居民存款减少 1.1万亿元，兴证研究认为，结合A股市场近期表现，居民存款流向权益资产的概率较高。 通过公募基金入市的资金也在明显增加，行业主题ETF成为主要增量。同源统计数据显示，截至8月15 日，6月以来行业主题ETF净流入238亿元，成为近期ETF的主 ...

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels to provide actionable insights[3] - **Model Construction Process**: The model calculates the crowding levels of various industries based on specific metrics (not detailed in the report) and ranks them accordingly. For example, the report highlights that the building materials, military, and non-ferrous industries had high crowding levels, while coal, petrochemical, and transportation had low crowding levels on the previous trading day[3] - **Model Evaluation**: The model provides a useful tool for identifying industry trends and potential investment opportunities by analyzing crowding dynamics[3] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products by calculating their premium rate Z-scores, identifying potential arbitrage opportunities while also warning of potential pullback risks[4] - **Model Construction Process**: The model employs a rolling calculation of the Z-score of the premium rate for various ETF products. The Z-score is calculated as: $ Z = \frac{(X - \mu)}{\sigma} $ where $ X $ is the current premium rate, $ \mu $ is the mean premium rate over a rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window. This helps identify ETFs with significant deviations from their historical norms[4] - **Model Evaluation**: The model is effective in identifying ETFs with potential arbitrage opportunities and provides a risk management tool for investors[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - **Top Crowded Industries**: Building materials, military, and non-ferrous industries had the highest crowding levels on the previous trading day[3] - **Least Crowded Industries**: Coal, petrochemical, and transportation industries had the lowest crowding levels on the previous trading day[3] 2. Premium Rate Z-Score Model - **Application Example**: The model flagged specific ETFs for potential arbitrage opportunities, such as the Battery Leaders ETF (159767.SZ), which tracks the New Energy Battery Index and has a fund size of 1.13 billion yuan[14] --- Quantitative Factors and Construction Methods No specific quantitative factors were detailed in the report beyond the models described above --- Factor Backtesting Results No specific backtesting results for individual factors were detailed in the report beyond the models described above

Group 1: A-Share Market Activity - The A-share market has seen increased trading activity, with the Shanghai Composite Index surpassing 3600 points, indicating a positive market cycle driven by profit effects [3][4] - Since July, there has been a significant inflow of funds into industry-themed ETFs, with active equity fund issuance showing a notable recovery [3][4] - Institutional positions have been continuously increasing, becoming a crucial support for the market rally [3][7] Group 2: ETF Inflows - Industry-themed ETFs have attracted substantial capital, particularly in three main areas: dividend-themed ETFs, AI sector ETFs, and ETFs related to policies aimed at reducing competition [4][5][6] - Notable inflows include 39.45 billion RMB into the Southern S&P China A-Share Large Cap Dividend Low Volatility ETF and 30.54 billion RMB into the Huatai-PB CSI Dividend Low Volatility ETF since July [4] - The surge in industry-themed ETF sizes often correlates with improved market activity and the formation of consensus on market themes [4][5] Group 3: Active Equity Funds - Active equity funds have experienced a resurgence, with an average annual return exceeding 14% and several products doubling in net value [6][7] - Seven active equity funds launched since July have raised over 1 billion RMB each, with the largest being the Dacheng Insight Advantage Mixed Fund at 2.461 billion RMB [6] - The second batch of floating management fee funds is also being launched, potentially driving further growth in the active equity fund market [6] Group 4: Institutional Investment Sentiment - Institutions have been increasing their positions, with average stock fund positions reaching approximately 90.55% as of August 8, reflecting a bullish outlook [7][8] - Major public funds, such as Southern Fund, have committed over 230 million RMB to their own equity funds, indicating confidence in the long-term stability of the capital market [8] - Analysts believe that the A-share market is currently in a favorable position for long-term investment, with expectations of continued inflows of capital [8][9] Group 5: Hong Kong Market Dynamics - There is a significant shift in global asset allocation, with a notable influx of capital into the Hong Kong market, which has become a favored destination for global investors [10][11] - As of August 11, net inflows from southbound funds into Hong Kong exceeded 800 billion RMB, surpassing the total for the entire year of 2024 [11] - The Hong Kong market is seen as a bridge for investing in China, with many private equity firms increasing their allocations to Hong Kong stocks while reducing exposure to U.S. equities [10][13] Group 6: Investment Opportunities in Hong Kong - The technology sector remains a key focus for institutional investors, with major stocks like Tencent and Alibaba seeing high trading volumes [17][18] - High-dividend stocks and new consumption sectors are also attracting attention, with significant net purchases in financials and consumer discretionary sectors [18] - The emergence of new core assets in China, driven by economic transformation, is expected to attract more global capital into the Hong Kong market [18]

Group 1 - The overall scale of ETFs is growing, but domestic stock ETFs are experiencing significant redemptions, with the total fund share falling to 1.96 trillion shares by the end of July [1][4] - As of the end of July, the total scale of stock ETFs reached 3.1 trillion yuan, an increase of over 70.3 billion yuan compared to the previous month [1][2] - The trend of increased net redemptions in stock ETFs has been notable, with July marking the second-highest net redemption of the year, following February [4][5] Group 2 - The broad-based ETFs, which account for the largest share, are key to the redemption situation of stock ETFs, with a total fund share of 1 trillion shares as of the end of July, a decrease of 79.04 billion shares from June [5][6] - The A500 index-linked broad-based ETFs have seen the most significant redemptions, with 21 ETFs linked to the index experiencing net redemptions of over 1 billion shares in July [5][7] - The scale of A500-linked ETFs dropped from over 210 billion yuan in June to 178 billion yuan by the end of July, a decrease of 38.1 billion yuan [5][6] Group 3 - Despite the redemptions in broad-based ETFs, industry-themed ETFs continued to see net subscriptions in July, particularly in dividend themes and undervalued sectors [1][8] - The banking sector ETF was the most subscribed stock ETF in July, with an increase of 9.99 billion shares, bringing its scale to 14.577 billion yuan [8][11] - Other industry-themed ETFs, such as those related to financial technology and liquor, also saw significant net subscriptions, indicating a diverse interest from investors [9][10][12] Group 4 - Looking ahead, institutional analysts remain optimistic about continued inflows into ETFs, with expectations that industry ETFs will remain active as tools for investors to participate in structural market trends [3][12] - The anticipated market volatility in August, coupled with the current earnings disclosure period, is expected to favor technology sectors and small-cap styles [12]

Core Viewpoint - The stock ETF market experienced a net outflow of approximately 3.22 billion yuan on July 21, despite the overall market reaching new highs for the year [1][2]. Group 1: Market Overview - On July 21, the total scale of the stock ETF market reached 3.74 trillion yuan, with a total increase of 3.123 billion shares [2]. - The industry-themed ETFs saw the highest net inflow, totaling 3.152 billion yuan [2]. - The top five sectors for inflows included Hong Kong financials (1.2 billion yuan), infrastructure and construction (1.16 billion yuan), Hong Kong internet (990 million yuan), Sci-Tech 50 (610 million yuan), and building materials (530 million yuan) [2]. Group 2: Fund Performance - The Hong Kong Internet ETF led with a net inflow of 969 million yuan, followed by the Hong Kong Securities ETF with 624 million yuan, and the Infrastructure 50 ETF with 598 million yuan [3]. - Major fund companies like E Fund and Huaxia Fund reported significant inflows in their ETFs, with E Fund's total scale reaching 671.44 billion yuan, increasing by 5.23 billion yuan on the same day [4]. - Huaxia Fund's Sci-Tech 50 ETF and Robot ETF saw net inflows of 439 million yuan and 163 million yuan, respectively [4]. Group 3: Outflows in Broad-based ETFs - Broad-based ETFs experienced the largest net outflows, totaling 2.396 billion yuan, with the CSI A500 seeing the highest outflow of 1.581 billion yuan [5]. - The Southern Fund's CSI 1000 ETF had a net outflow exceeding 400 million yuan, ranking first among stock ETFs [5]. - The recent market trend indicates that some investors are opting to take profits as the index rises, leading to outflows from A500-related ETFs [5].

- The industry crowding monitoring model was constructed to monitor the daily crowding levels of Shenwan primary industry indices. It identified utilities and building materials as having high crowding levels, while automotive, food & beverage, and home appliances showed low crowding levels. The model also tracked significant daily changes in crowding levels for industries like agriculture, coal, and environmental protection[4] - The Z-score premium rate model was developed to screen ETF products for potential arbitrage opportunities. This model uses rolling calculations to identify signals and warns of potential risks of price corrections for the identified ETFs[5] - Daily net inflows for broad-based ETFs amounted to 3.77 billion yuan, with top inflows observed in CSI 1000 ETF (+7.78 billion yuan), SSE 50 ETF (+6.96 billion yuan), and CSI 300 ETF (+5.38 billion yuan). Conversely, top outflows were recorded for ChiNext ETF (-6.73 billion yuan), CSI A500 ETF (-4.06 billion yuan), and STAR 50 ETF (-3.51 billion yuan)[6] - Industry-themed ETFs saw a daily net inflow of 1.82 billion yuan, with top inflows in Military ETF (+4.01 billion yuan), Securities ETF (+2.63 billion yuan), and Defense ETF (+2.31 billion yuan). Top outflows were noted for Robotics ETF (-1.39 billion yuan), Semiconductor ETF (-1.05 billion yuan), and AI ETF (-0.99 billion yuan)[6] - Style-strategy ETFs recorded a daily net inflow of 2.29 billion yuan, with top inflows in Low Volatility Dividend ETF (+1.62 billion yuan), Low Volatility Dividend 50 ETF (+0.53 billion yuan), and Dividend State-Owned Enterprise ETF (+0.28 billion yuan). Top outflows included CSI Dividend ETF (-0.19 billion yuan), Low Volatility Dividend ETF (-0.18 billion yuan), and Low Volatility Dividend 100 ETF (-0.15 billion yuan)[6] - Cross-border ETFs experienced a daily net outflow of 0.51 billion yuan, with top inflows in Hong Kong Non-Bank ETF (+3.84 billion yuan), Hang Seng Low Volatility Dividend ETF (+0.63 billion yuan), and S&P 500 ETF (+0.42 billion yuan). Top outflows were observed for Hang Seng Tech ETF (-1.19 billion yuan), Hong Kong Dividend ETF (-0.82 billion yuan), and Nasdaq 100 ETF (-0.69 billion yuan)[6]

Core Insights - The number of ETFs exceeding 10 billion yuan has reached a record high of 83, an increase of 17 from the end of last year, driven by various types of ETFs including bond, Hong Kong stock, and gold ETFs [1][3][4] Group 1: ETF Market Growth - The domestic ETF market size has reached 4.32 trillion yuan, with a year-to-date increase of 593.07 billion yuan, indicating strong growth momentum [3][9] - The growth of ETF scale is primarily driven by supportive policies and increased investor risk appetite, with significant inflows into certain ETFs like gold ETFs [3][4] - The average size of ETFs has increased due to rapid growth in market scale, particularly in broad-based ETFs, which have a high concentration [4][10] Group 2: Head Effect and Market Concentration - The "Matthew Effect" is becoming more pronounced, with leading fund companies significantly expanding their ETF scales; for instance, China Asset Management's ETF scale increased by 95.4 billion yuan this year [6][7] - Twelve fund companies have ETF scales exceeding 100 billion yuan, collectively accounting for 83.55% of the market, highlighting a trend towards market concentration [7][10] - Despite the growth in ETF numbers and scales, the number of fund managers issuing ETFs has not significantly increased, indicating a competitive environment among existing players [7][10] Group 3: Future Trends and Challenges - The growth trend of passive investment is expected to continue, with potential for further concentration in broad-based ETFs and significant innovation in product offerings [10] - The demand for niche technology-focused ETFs is anticipated to grow, driven by advancements in fields like artificial intelligence and biotechnology [10] - The future growth of ETFs may be influenced by market conditions and the performance of actively managed funds, which could divert some capital away from ETFs [10]

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels and significant changes in crowding over time[4]. - **Model Construction Process**: The model calculates crowding levels for each industry index daily, based on metrics such as main fund inflows and outflows. It identifies industries with the highest and lowest crowding levels and tracks significant changes in crowding over recent trading days[4]. - **Model Evaluation**: The model provides actionable insights into industry crowding dynamics, helping to identify potential investment opportunities or risks[4]. 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of premium rates over a rolling window[5]. - **Model Construction Process**: The model involves the following steps: 1. Calculate the premium rate of an ETF as the percentage difference between its market price and net asset value (NAV). 2. Compute the Z-score of the premium rate over a rolling window to standardize the deviation. 3. Identify ETFs with extreme Z-scores as potential arbitrage opportunities[5]. - **Model Evaluation**: The model effectively highlights ETFs with significant deviations from their NAV, which may indicate arbitrage opportunities or risks of price corrections[5]. --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results were provided for this model[4]. 2. Premium Rate Z-Score Model - No specific numerical backtesting results were provided for this model[5]. --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the report. --- Factor Backtesting Results No specific quantitative factor backtesting results were provided in the report.

Quantitative Models and Construction 1. Model Name: Industry Crowdedness Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowdedness levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowdedness levels to provide insights for potential investment opportunities or risks [4] - **Model Construction Process**: 1. The model calculates the crowdedness levels of each industry index based on specific metrics (not explicitly detailed in the report) 2. Daily updates are performed to track changes in crowdedness levels across industries 3. Industries with significant changes in crowdedness levels are highlighted for further analysis [4] - **Model Evaluation**: The model effectively identifies industries with extreme crowdedness levels, providing actionable insights for investors [4] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model identifies potential arbitrage opportunities in ETF products by calculating the Z-score of their premium rates over a rolling window [5] - **Model Construction Process**: 1. The premium rate of each ETF is calculated as the difference between its market price and net asset value (NAV) 2. A rolling window is applied to compute the Z-score of the premium rate for each ETF 3. ETFs with Z-scores exceeding a certain threshold are flagged as potential arbitrage opportunities [5] - **Model Evaluation**: The model provides a systematic approach to detect arbitrage opportunities while also warning of potential price corrections [5] --- Backtesting Results of Models 1. Industry Crowdedness Monitoring Model - **Top Crowded Industries**: Basic Chemicals, Textile & Apparel, Light Manufacturing [4] - **Least Crowded Industries**: Real Estate, Electronics, Social Services, Steel, Non-Banking Financials [4] - **Significant Daily Changes**: Petroleum & Petrochemicals experienced notable daily crowdedness changes [4] 2. Premium Rate Z-Score Model - **Highlighted ETFs for Arbitrage**: Specific ETFs flagged for potential arbitrage opportunities are not detailed in the report [5]