Core Viewpoint - The rapid development of ETFs in the A-share market has led to a significant increase in their scale and number, surpassing actively managed funds, indicating a growing preference for passive investment strategies among investors [4][5]. Group 1: ETF Growth and Market Dynamics - As of June 15, 2025, the total scale of stock ETFs (including off-market linked funds) reached 3.81 trillion yuan, with the number of ETFs totaling 2,031, exceeding the scale of actively managed funds at 2.84 trillion yuan [4][5]. - The A-share market exhibits significant industry and style differentiation, suggesting that merely holding a single ETF for the long term may not yield optimal investment experiences [4][6]. - The investment objective of ETFs is to closely track the net value performance of specific indices, making the choice of index crucial for investors seeking substantial returns [6][10]. Group 2: ETF Rotation Strategy Development - A common method for constructing ETF rotation strategies involves aggregating effective stock factors at the index level, allowing for index rotation effects [2][11]. - The use of the AGRU model based on daily K-line volume and price data has resulted in the identification of high-performing stock selection factors in the A-share market [12][16]. - Monthly rebalancing of the strategy yielded an average IC of 7.80%, with an annualized excess return of 4.92% and a maximum drawdown of -14.02% [31][39]. Group 3: Performance of Fixed Number ETF Rotation Strategies - Limiting the number of held ETFs to 5, 10, or 15 resulted in varying annualized excess returns: 12.34% for 5 ETFs, 8.75% for 10 ETFs, and 8.13% for 15 ETFs, with corresponding maximum drawdowns of -12.17%, -8.83%, and -8.66% respectively [59][65]. - The strategy consistently achieved positive excess returns annually, with a notable 8.74% excess return year-to-date [63][65]. Group 4: Factor Testing and Adjustments - The factor's performance was enhanced through the adjustment of the loss function, leading to improved multi-directional return performance [17][19]. - The AGRU factor demonstrated strong stock selection effects across various stock pools, with annualized excess returns of 21.97% for the CSI 300 pool and 11.46% for the CSI 500 pool [64][65]. Group 5: MMR Algorithm and Risk Diversification - The MMR (Maximum Marginal Relevance) algorithm was employed to reduce the correlation among selected investment targets, enhancing the stability of the strategy's performance [45][50]. - The strategy's annualized excess return improved from 7.94% to 8.43% after implementing the MMR adjustments, with a corresponding increase in the information ratio [50][52].

择时策略： 本文以DDQN作为核心模型，采用10分钟频的量价数据作为模型输入，择时策略的目标是让 模型学会在各个时间节点给出买入/卖出/继续持有/继续空仓等信号，并使得期末收益最大化。在回测环 节，强化学习模型每10分钟输出择时信号，并遵循t+1规则进行交易。若当天出现多个买入/卖出信号，则 仅选择每天出现的第一个买入/卖出信号进行交易，且当日买入的无法在当日卖出。 实证分析： 本文策略是对单一标的进行择时，其中包括流动性较好的某沪深300ETF、中证500ETF、中证 1000ETF以及某个股。在样本外2023/01/01~2025/05/31期间，按照t+1交易规则，本文策略在上述4个择时 标的中分别产生了72、30、73、188次择时信号（一买一卖算一次），平均胜率分别为52.8%、53.3%、 54.8%、51.6%，期末累计收益分别跑赢基准标的10.9%、35.5%、64.9%、37.8%。 广发证券首席金工分析师 安宁宁 anningning@gf.com.cn 广发证券联席首席金工分析师 陈原文 chenyuanwen@gf.com.cn 联系人：广发证券金工研究员 林涛 gflintao@gf ...

广发证券资深金工分析师 周飞鹏 SAC: S0260521120003 zhoufeipeng@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发金工安宁宁陈原文团队 摘要 主要结论： 在前期报告《基于龙头扩散效应的行业轮动框架-狭义与广义应用探讨》中我们提出了 一种数据拓展性良好的因子改进框架，并以景气度、资金流因子为例做了改进讨论。本报告在此基 础上进一步围绕多头组合构建进行测算并尝试寻找较理想的组合构建方式。 优选行业组合月度调 仓，投资范围为中信一级行业，2013年以来年化收益26.0%，相对全行业等权组合年化超额收益 19.1%，IR1.84，相对最大回撤13.8%。2022年以来组合超额增长稳定，年化超额收益11.7%。 板块行情的底层驱动机制： 受活跃资金挖掘概念主题手法的启发，我们思考板块行情的启动和发展过 程或许在微观层面上源自板块内个股上涨的蔓延与扩散，从最初的行情龙头到概念相关的更多个股，正 是行情覆盖范围的逐渐延伸催生了一轮行业上行趋势。我们将此过程称为"龙头扩散效应"。 扩散效应的逻辑梳理： 扩散 ...

Core Viewpoint - The article emphasizes the significance of the China Securities Hong Kong Stock Connect Technology Index, which focuses on large-cap technology companies with high R&D investment and revenue growth within the Hong Kong Stock Connect framework [1][3]. Group 1: Index Characteristics - The index selects 50 large-cap technology companies to reflect the overall performance of technology leaders in the Hong Kong Stock Connect [3]. - The index has a balanced industry distribution, with major allocations in internet, automotive, and innovative pharmaceuticals, avoiding overcrowded sectors like electronics and media [11]. - The index includes industry leaders such as BYD in automotive and BeiGene in innovative pharmaceuticals, reducing exposure to second-tier internet companies [12]. Group 2: Market Performance - The index has shown high elasticity in market performance, outperforming similar indices during various market cycles since 2014 [22]. - Since the end of 2014, the annualized return of the index has exceeded that of other similar indices, such as the National Securities Hong Kong Stock Connect Technology Index and the Hang Seng Technology Index [22]. - The index's performance has been driven by significant contributions from the automotive and biopharmaceutical sectors, with distinct phases of market activity observed [22]. Group 3: Fund Introduction - The Southern China Securities Hong Kong Stock Connect Technology ETF (code: 159269) is set to track the index and will be issued starting June 18 [37]. - The fund aims to closely replicate the index's performance, minimizing tracking deviation and error [37].

Market Performance - The Sci-Tech 50 Index decreased by 1.89% over the last five trading days, while the ChiNext Index increased by 0.22%. The large-cap value index rose by 0.10%, and the large-cap growth index fell by 0.16%. The Shanghai 50 Index declined by 0.46%, and the small-cap index represented by the CSI 2000 dropped by 0.74%. The non-ferrous metals and oil & petrochemical sectors performed well, whereas household appliances and food & beverage sectors lagged behind [1]. Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data to be at two standard deviations above the mean, with notable peaks in 2012, 2018, and 2020. As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it rose to 4.08%. The latest reading on January 19, 2024, was 4.11%, marking the fifth instance since 2016 exceeding 4%. As of June 13, 2025, the indicator was at 3.83%, with the two standard deviation boundary at 4.75% [1]. Valuation Levels - As of June 13, 2025, the CSI All Index's P/E TTM percentile was at 54%. The Shanghai 50 and CSI 300 indices were at 62% and 52%, respectively. The ChiNext Index was close to 13%, while the CSI 500 and CSI 1000 indices were at 30% and 22%. The ChiNext Index's valuation is relatively low compared to historical averages [2]. Long-term Market Trends - The technical analysis of the Deep 100 Index indicates a bear market every three years, followed by a bull market. Historical declines ranged from 40% to 45%, with the current adjustment starting in Q1 2021 showing sufficient time and space for a potential upward cycle [2]. Fund Flow and Trading Activity - Over the last five trading days, ETF funds saw an outflow of 17 billion yuan, while margin financing increased by approximately 9.4 billion yuan. The average daily trading volume across both markets was 1.3392 trillion yuan [2]. Neural Network Analysis - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes. The latest recommended themes include non-ferrous metals and banking sectors [9].

Core Viewpoint - The report aims to construct a multi-factor weighted ETF rotation strategy and optimize the stock factor mapping framework to test for marginal improvement in performance [1][5]. Group 1: Background and Market Overview - The concept of index-based investment has gained recognition among investors, with ETFs becoming a significant tool for asset allocation due to their transparency, low fees, and ease of trading. As of April 2025, the number of ETFs listed on domestic exchanges reached 1,141, with a total market value of 4.04 trillion yuan, an increase from 3.73 trillion yuan at the end of 2024 [4]. - Previous reports have constructed product rotation strategies based on various factors such as Level 2 data, redemption data, and neural networks, while also mapping individual stock factors to indices [4]. Group 2: Single Factor Stock Selection and ETF Rotation Comparison - The report compares the performance of single-factor stock selection and ETF rotation, highlighting the differences in factor characteristics. The factors include low-frequency price-volume, fundamentals, ETF redemption fund flows, Level 2 data, and neural network-related factors [6][7]. - Backtesting results indicate that the long position in stocks achieved significant excess returns compared to broad indices, while the RANK_IC and annualized returns of the ETF rotation factors showed marginal declines [11][15]. Group 3: ETF Rotation Backtesting Framework Adjustments - The report attempts to retain only the top-weighted components by setting an upper limit on weight thresholds and applying equal-weight "non-linear mapping." The results show that adjusting to equal-weight mapping covering all components led to a marginal decline in the performance of the single-factor long position [2]. - The ETF selection process identifies ETFs with similar holdings but different names, filtering out products with lower factor values when the overlap of constituent stocks exceeds a threshold. When an 80% overlap threshold is set, the performance of the single-factor long position improves [2]. Group 4: Multi-Factor Weighted ETF Rotation Empirical Testing - The empirical testing of the multi-factor weighted ETF rotation strategy, starting from January 2021, shows that the RANK_IC and ICIR of the portfolio improved marginally with monthly rebalancing. The equal-weighted top 5 long position achieved an annualized return of 18.6%, while the IC-weighted and ICIR-weighted portfolio achieved approximately 20% annualized returns, indicating more stable performance compared to the equal-weighted portfolio [2][15]. Group 5: ETF Rotation Backtesting Empirical Results - The backtesting results for ETF rotation indicate that the RANK_IC and annualized returns of the factors showed a noticeable marginal decline, with the ICIR significantly lower than that of the stock selection backtesting results [15].

Core Viewpoint - The article presents a comprehensive analysis of major asset classes from both macroeconomic and technical perspectives, indicating a generally favorable outlook for equities, bonds, industrial products, and gold, while highlighting specific trends and valuation metrics for each asset class [1][3][19]. Macroeconomic Perspective - The macroeconomic indicators suggest a positive outlook for equities, bonds, industrial products, and gold, with specific indicators showing varying degrees of influence on asset performance [5][19]. - The analysis employs T-tests to assess the impact of macroeconomic trends on asset returns, indicating significant differences in average returns under different macro conditions [3][4]. Technical Perspective - The technical analysis indicates that as of May 31, 2025, bond and gold prices are trending upwards, while equity and industrial product prices are trending downwards [9][10]. - Different methods are used to calculate trend indicators for various asset classes, with historical performance data guiding the selection of the most effective methods [7]. Valuation Metrics - The current equity risk premium (ERP) is at 82.51%, indicating that equity valuations are relatively low compared to historical averages [12][13]. - The analysis of funding flow metrics shows that the equity market is experiencing a net outflow of 1.3 billion yuan, suggesting a cautious sentiment among investors [15][16]. Asset Allocation Performance Tracking - Historical performance data indicates that the fixed ratio combined with macro and technical indicators yielded a return of 0.09% in May 2025, with an annualized return of 11.82% since April 2006 [2][20]. - The performance of various asset allocation strategies is tracked, showing that combinations of macro and technical indicators can enhance returns while managing risk [24][25].

Market Performance - The recent five trading days saw the Sci-Tech 50 Index decline by 0.36%, the ChiNext Index by 1.40%, and the large-cap value index by 0.16%, while the large-cap growth index fell by 2.71%. The Shanghai 50 Index decreased by 1.22%, whereas the small-cap index represented by the CSI 2000 rose by 0.94%. Sectors such as environmental protection and biomedicine performed well, while automotive and electrical equipment lagged behind [1]. Risk Premium Analysis - The risk premium, defined as the inverse of the static PE of the CSI All Index (EP) minus the yield of ten-year government bonds, indicates that the implied returns of equity and bond assets are at historically significant levels. For instance, on April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it was 4.08%. As of January 19, 2024, the indicator stood at 4.11%, marking the fifth occurrence since 2016 of exceeding 4%. As of May 30, 2025, the indicator was at 3.90%, with the two-standard deviation boundary set at 4.75% [1]. Valuation Levels - As of May 30, 2025, the CSI All Index's PETTM was at the 50th percentile, with the Shanghai 50 and CSI 300 at 61% and 48%, respectively. The ChiNext Index was close to 11%, while the CSI 500 and CSI 1000 were at 30% and 32%. The ChiNext Index's valuation style is relatively low compared to historical averages [2]. Long-term Market Trends - The technical analysis of the Deep 100 Index indicates a cyclical pattern of bear markets every three years, followed by bull markets. Historical declines ranged from 40% to 45%, with the current adjustment starting in the first quarter of 2021 showing sufficient time and space for a potential upward cycle [2]. Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 8.5 billion yuan, with margin trading increasing by approximately 720 million yuan. The average daily trading volume across both markets was 10.687 billion yuan [4]. AI and Machine Learning Applications - The use of convolutional neural networks (CNN) for modeling price and volume data has been explored, with features mapped to industry themes. The latest focus is on sectors such as banking [3][10].

Market Performance - The Sci-Tech 50 Index decreased by 1.47% over the last five trading days, while the ChiNext Index fell by 0.88%. In contrast, the large-cap value stocks rose by 0.48%, and the large-cap growth stocks declined by 0.40% [1] - The medical and biological sectors performed well, while the computer and machinery equipment sectors lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data to be at two standard deviations above the mean, with recent peaks at 4.17% on April 26, 2022, and 4.11% on January 19, 2024. As of May 23, 2025, the indicator stands at 3.84%, with the two standard deviation boundary at 4.76% [1] Valuation Levels - As of May 23, 2025, the CSI All Share Index's TTM PE is at the 51st percentile, with the SSE 50 and CSI 300 at 62% and 49%, respectively. The ChiNext Index is close to 11%, indicating a relatively low valuation level compared to historical averages [2] Long-term Market Trends - The Shenzhen 100 Index has experienced bear markets approximately every three years, followed by bull markets. The current adjustment, which began in Q1 2021, has shown sufficient time and space for a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds saw an outflow of 24 billion yuan, while margin financing decreased by approximately 20 million yuan. The average daily trading volume across both markets was 1.1376 trillion yuan [3] AI and Machine Learning Applications - A convolutional neural network (CNN) has been utilized to model price and volume data, mapping learned features to industry themes. The latest focus is on sectors such as banking and dividends [2][10]

Market Performance - The Sci-Tech 50 Index decreased by 1.10% over the last five trading days, while the ChiNext Index increased by 1.38%. The large-cap value and growth indices rose by 1.45% and 1.60%, respectively. The Shanghai 50 Index rose by 1.22%, and the small-cap index represented by the CSI 2000 increased by 0.16%. The beauty and personal care sector, along with non-bank financials, performed well, while the computer and defense industries lagged behind [1]. Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data at two standard deviations above the mean, with notable instances in 2012, 2018, and 2020. As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it was 4.08%. The latest reading on January 19, 2024, was 4.11%, marking the fifth instance since 2016 exceeding 4%. As of May 16, 2025, the indicator was at 3.86%, with the two standard deviation boundary at 4.76% [1]. Valuation Levels - As of May 16, 2025, the CSI All Index's PETTM percentile is at 52%. The Shanghai 50 and CSI 300 indices are at 62% and 50%, respectively, while the ChiNext Index is close to 11%. The CSI 500 and CSI 1000 indices are at 31% and 33%, indicating that the ChiNext Index's valuation is relatively low compared to historical levels [2]. Long-term Market Trends - The technical analysis of the Deep 100 Index shows a pattern of bear markets occurring every three years, followed by bull markets. The last bear market began in Q1 2021, with previous declines ranging from 40% to 45%. The current adjustment appears to have sufficient time and space, suggesting a potential upward cycle from the bottom [2]. Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 34.1 billion yuan, while margin financing decreased by approximately 600 million yuan. The average daily trading volume across the two markets was 1.2317 trillion yuan [3]. AI and Machine Learning Applications - The report discusses the use of convolutional neural networks (CNN) to model price and volume data, aiming to predict future prices. The learned features are mapped to industry themes, with a current focus on banking [6].