广发证券资深金工分析师 张超 SAC: S0260514070002 zhangchao@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 国内可转债ETF发展现状 ： 目前国内已发行两只可转债ETF——博时中证可转债及可交换债券ETF（2025Q1规模376.6亿元）和海富通上证投资级可转债ETF（2025Q1规模57.8亿 元），合计规模占全市场可转债规模的6.1%。两只ETF管理费率低于主动可转债基金（分别为0.15%、0.25%）。2024年以来，机构投资者占比超97%，成 为规模迅速增长的主力，反映出机构通过配置ETF分散非系统性风险的需求。业绩方面，2024年收益：博时5.87%、海富通8.32%，优于主动可转债基金均 值，叠加低费率优势，被动化配置需求大幅上升。 美国可转债市场 ： 相比之下，美国可转债市场历史更为悠久，始于19世纪中叶，历经制度完善、产品创新与危机考验后，已成为全球最大、最成熟的可转债市场。截至2024 年末，美国可转债存量规模达2540亿美元，发行主体覆盖中小企业至大型公司，行业重点集中于TM ...

Core Viewpoint - The article discusses the performance and characteristics of the ChiNext New Energy Index, highlighting its focus on companies involved in the new energy and new energy vehicle sectors, and its historical outperformance compared to similar indices [1][4][27]. Group 1: Index Characteristics - The ChiNext New Energy Index includes 50 companies listed on the ChiNext board, focusing on the new energy and new energy vehicle industries, reflecting the overall performance of the new energy sector [1][4]. - As of May 9, 2025, the index's top five industries, including battery, photovoltaic equipment, automation equipment, wind power equipment, and metal new materials, account for 92% of the index's weight [12][21]. - The index has achieved an annualized return of 11.78% since its inception, outperforming other new energy indices [27][31]. Group 2: Valuation and Performance - The index has a high margin of safety in valuation, with a TTM price-to-earnings ratio of 24.45 and a price-to-book ratio of 3.31 as of May 9, 2025, indicating it is at the 44.7% and 34.9% historical percentiles, respectively [24]. - The index's historical performance shows a higher Sharpe ratio compared to other new energy indices, indicating better risk-adjusted returns [27][31]. Group 3: Lithium Battery Sector Insights - Leading companies in the lithium battery sector demonstrate stable profitability, with the battery segment accounting for 78.30% of the industry's profit in Q1 2023, and expected to rise to 90.10% in Q1 2024 [37]. - The financial indicators used to assess the industry cycle include weighted return on equity (ROE), quick ratio, and fixed asset turnover, suggesting that the battery sector may soon see a profitability turning point [38][39]. Group 4: Power Equipment Demand - The power equipment sector is experiencing high demand, with most companies reporting revenue growth and a robust order backlog, particularly in overseas markets [41][43]. - The industry is expected to benefit from ongoing investments in grid expansion and equipment upgrades, with significant growth in contract liabilities indicating a positive outlook for future revenue [44]. Group 5: Fund Product Overview - The Guotai ChiNext New Energy ETF closely tracks the ChiNext New Energy Index and employs a full replication strategy to minimize tracking error, with subscriptions opening on May 12 [47].

Core Viewpoint - The article discusses the performance and characteristics of the ChiNext New Energy Index, highlighting its focus on companies involved in the new energy and new energy vehicle sectors, and its historical outperformance compared to similar indices [1][4][27]. Group 1: Index Characteristics - The ChiNext New Energy Index selects 50 companies listed on the ChiNext board that are involved in the new energy or new energy vehicle industries, reflecting the overall performance of the new energy theme [1][4]. - As of May 9, 2025, the index's top five industries, including battery, photovoltaic equipment, automation equipment, wind power equipment, and metal new materials, account for 92% of the index's weight [12][21]. - The index has achieved an annualized return of 11.78% since its inception, outperforming other new energy indices [27][31]. Group 2: Valuation and Performance - The index has a high margin of safety in valuation, with a TTM price-to-earnings ratio of 24.45 and a price-to-book ratio of 3.31 as of May 9, 2025, indicating it is at the 44.7% and 34.9% historical percentiles, respectively [24]. - The index's historical performance shows a higher Sharpe ratio compared to other new energy indices, indicating better risk-adjusted returns [27][31]. Group 3: Lithium Battery Sector Insights - Leading companies in the lithium battery sector demonstrate stable profitability, with the battery segment accounting for 78.30% of the industry's profit in Q1 2023, and expected to rise to 90.10% in Q1 2024 [37]. - The financial indicators used to assess the industry cycle include weighted return on equity (ROE), quick ratio, and fixed asset turnover, suggesting that the battery sector may soon see a profitability turning point [38][39]. Group 4: Power Equipment Demand - The power equipment sector is experiencing high demand, with most companies reporting revenue growth and a robust order backlog, particularly in overseas markets [41][43]. - The industry is expected to benefit from ongoing investments in grid expansion and equipment upgrades, with significant growth in contract liabilities indicating a positive outlook for future revenue [44]. Group 5: Fund Product Overview - The Guotai ChiNext New Energy ETF closely tracks the ChiNext New Energy Index and employs a full replication strategy to minimize tracking error, with subscriptions opening on May 12 [47].

广发证券联席 首席金工分析师 陈原文 SAC: S0260517080003 chenyuanwen@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发证券资深金工分析师 张钰东 SAC: S0260522070006 zhangyudong@gf.com.cn 广发金工安宁宁陈原文团队 摘要 研究背景： 近年来，指数化投资理念愈发受到投资者认可。根据指数编制规则，上证50、沪深300和中证500等宽基指数于每年的6月和12月定期调仓，成 分股名单会部分调入调出。跟踪相应指数的指数型基金，同样会参照指数编制规则，被动调整持仓成分股。当前的被动型基金规模屡创新高，若指数成分 股存在较大变动，则可能带来潜在的投资机会。 指数类产品规模统计： 规模继续增长。根据Wind，截至4月30日，1969只被动指数型基金（ETF和场外被动指数型基金）规模合计3.4万亿元，346只增强 指数型基金规模合计2211亿元，合计规模高于偏股混合型基金（2.07万亿元）。根据跟踪指数的不同，统计各类指数的产品跟踪情况，跟踪指数产品规模 靠前的指数分 ...

Core Viewpoint - The article provides predictions for the periodic adjustments of major broad-based core indices in China, including the Shanghai 50 Index, CSI 300 Index, CSI 500 Index, CSI 1000 Index, ChiNext Index, Shenzhen 100 Index, Sci-Tech 50 Index, and Sci-Tech 100 Index, scheduled for June 2025. These adjustments may create event-driven trading opportunities for investors [1][4][5]. Group 1: Shanghai 50 Index Adjustment Predictions - According to the adjustment rules, five stocks including China National Offshore Oil Corporation and SAIC Motor Corporation will be added to the Shanghai 50 Index, while five stocks such as Haitian Flavoring and Food will be removed in June 2025 [1][6]. Group 2: CSI 300 Index Adjustment Predictions - Six stocks including China National Aviation Holdings and Shanghai Electric will be added to the CSI 300 Index, while six stocks such as 37 Interactive Entertainment and Hualan Biological Engineering will be removed in June 2025 [1][7]. Group 3: CSI 500 Index Adjustment Predictions - Fifty stocks including Tianshan Shares and Shenhuo Co. will be added to the CSI 500 Index, while fifty stocks such as Jianghuai Automobile and China Power Investment will be removed in June 2025 [2][9]. Group 4: CSI 1000 Index Adjustment Predictions - One hundred stocks including Kema Technology and Wireless Media will be added to the CSI 1000 Index, while one hundred stocks such as Jinbo Shares and Plai Ke will be removed in June 2025 [2][13]. Group 5: ChiNext Index Adjustment Predictions - Ten stocks including Ruijie Networks and Guibao Pet will be added to the ChiNext Index, while ten stocks such as Kaili Medical and Anke Bio will be removed in June 2025 [2][18]. Group 6: Shenzhen 100 Index Adjustment Predictions - Four stocks including AVIC Chengfei and Guangqi Technology will be added to the Shenzhen 100 Index, while four stocks such as TCL Zhonghuan and Kanglong Chemical will be removed in June 2025 [2][19]. Group 7: Sci-Tech 50 Index Adjustment Predictions - Four stocks including BeiGene and Huahong Semiconductor will be added to the Sci-Tech 50 Index, while four stocks such as BGI Genomics and Tianyue Advanced will be removed in June 2025 [2][21]. Group 8: Sci-Tech 100 Index Adjustment Predictions - Ten stocks including Qihoo 360 will be added to the Sci-Tech 100 Index, while ten stocks such as Airo Energy and YN Technology will be removed in June 2025 [3][23].

广发证券联席 首席金工分析师 陈原文 SAC: S0260517080003 chenyuanwen@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发证券资深金工分析师 张钰东 SAC: S0260522070006 zhangyudong@gf.com.cn 广发金工安宁宁陈原文团队 摘要 指数定期调整预测。 中证指数有限公司及深圳证券信息有限公司在每年的6月及12月会根据指数编制方案对主要的宽 基核心指数成分股进行定期的调整。 上证50指数调整预测。 根据上证50指数编制规则，2025年6月将会有中国海油、上汽集团等5只个股被调入上证50指 数，而海天味业等5只个股被调出上证50指数。 深证100指数调整预测。 根据深证100指数编制规则，2025年6月将会有中航成飞、光启技术等4只个股被调入深证100 指数，而TCL中环、康龙化成等4只个股被调出深证100指数。 科创50指数调整预测。 根据科创50指数编制规则，2025年6月将会有百济神州、华虹公司等4只个股被调入科创50指 数，而华大智造、天岳先进等4只个股被调 ...

Market Performance - The recent 5 trading days saw the Sci-Tech 50 Index increase by 0.24%, the ChiNext Index rise by 4.13%, large-cap value stocks up by 1.55%, large-cap growth stocks up by 2.05%, the SSE 50 Index up by 1.46%, and the small-cap represented by the CSI 2000 up by 3.77% [1] - The defense and military industry, as well as the communication sector, performed well, while steel and retail 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, which has historically reached extreme levels at two standard deviations above the mean during significant market bottoms, such as in 2012, 2018, and 2020 [1] - As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it was 4.08%, with a recent reading of 4.11% on January 19, 2024, marking the fifth occurrence since 2016 of exceeding 4% [1] Valuation Levels - As of May 9, 2025, the CSI All Index's PETTM is at the 50th percentile, with the SSE 50 and CSI 300 at 61% and 47% respectively, while the ChiNext Index is close to 11% [2] - 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 pattern of bear markets every three years followed by bull markets, with previous declines ranging from 40% to 45% [2] - The current adjustment cycle began in Q1 2021, suggesting a potential for upward movement from the bottom [2] Fund Flow and Trading Activity - In the last 5 trading days, ETF funds saw an outflow of 17.9 billion yuan, while margin trading increased by approximately 4.4 billion yuan [2] - The average daily trading volume across both markets was 1.2918 trillion yuan [2] AI and Machine Learning Insights - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes, with a current focus on banking [2][7] Market Sentiment - The proportion of stocks above the 200-day moving average is being tracked to gauge market sentiment [9] Equity and Bond Risk Preference - Ongoing monitoring of risk preferences between equity and bond assets is being conducted [11]

Core Viewpoint - The article presents a comprehensive analysis of macroeconomic and technical perspectives on major asset classes, indicating a bearish outlook for equities and industrial products, while being bullish on bonds and gold [1][3][21]. Group 1: Macroeconomic Perspective - The macroeconomic indicators suggest a negative outlook for equity assets, a positive outlook for bond assets, and a negative outlook for industrial products, while gold assets are viewed positively [3][5][21]. - Specific macro indicators such as PMI, CPI, and social financing stock growth rates are analyzed to determine their impact on asset performance [6][21]. Group 2: Technical Perspective - The technical analysis indicates a downward trend for equities, bonds, and industrial products, while gold shows an upward trend [10][11][21]. - The article employs various methods to assess asset trends, including historical price averages and specific trend indicators [7][11]. Group 3: Asset Valuation and Fund Flow - The equity risk premium (ERP) for the CSI 800 index is reported at 86.07%, indicating a low valuation level for equity assets [14][15]. - As of April 30, 2025, the net inflow for equity assets is recorded at 557 billion, suggesting a state of capital inflow [17][18]. Group 4: Performance Tracking of Asset Allocation Combinations - Historical performance data shows that the fixed ratio combined with macro and technical indicators yielded a return of 0.05% in April 2025, with an annualized return of 11.87% since March 2006 [2][26]. - Other combinations, such as volatility control and risk parity, also demonstrated positive returns, with annualized returns of 9.33% and 9.64% respectively [26][27].

Core Viewpoint - The article emphasizes the increasing importance of factor development and iteration in multi-factor models due to the declining returns from traditional factors and the challenges posed by factor crowding [1][3][62]. Factor Construction - The "Index Enhanced ETF Factor" is constructed using daily subscription and redemption data from index-enhanced ETFs, comparing the actual allocation weights of fund managers to the benchmark index weights to derive relative allocation (also known as "underweight") ratios [1][8]. - This process allows for the creation of signals based on fund managers' actual stock preferences, enhancing active management strategies [1][8]. Empirical Analysis - The constructed "Index Enhanced ETF Factor" shows a significant monotonic increase in returns across various indices (CSI 300, CSI 500, CSI 1000, and CSI 2000) during weekly backtesting, with notable excess returns for the top groups compared to the bottom groups [2][22]. - The factor's Information Coefficient (IC) performance is robust, with IC win rates of 62.42% for CSI 300, 64.33% for CSI 500, 72.32% for CSI 1000, and 60.00% for CSI 2000, indicating strong predictive power [2][40][43]. High-Frequency vs. Low-Frequency Data - High-frequency data offers advantages in factor development due to its larger volume and the ability to create diverse features through advanced techniques like machine learning, despite the challenges of noise and complexity [4][5][6]. - Low-frequency data, while more traditional, has limited incremental information, making it harder to extract significant alpha, thus necessitating innovative approaches to factor construction [6][62]. Strategy Explanation - The strategy involves tracking fund managers' preferences through the ETF's daily disclosure of holdings, allowing for the identification of stocks with higher expected returns based on their relative underweight status [8][62]. - The performance of index-enhanced ETFs has shown consistent outperformance against their benchmarks, validating the strategy's rationale [9][62]. Backtesting Results - The backtesting results indicate that the "Index Enhanced ETF Factor" has demonstrated significant cumulative returns across the four major indices, with a clear upward trend in group returns from low (G1) to high (G5) [22][62]. - The factor's IC values have shown a steady increase over time, particularly in the CSI 500 and CSI 1000 indices, highlighting its effectiveness in capturing excess returns [62][63]. Conclusion - The "Index Enhanced ETF Factor" effectively tracks fund managers' actual stock preferences, showing significant empirical validity in its ability to generate excess returns across various indices [62][63]. - The strategy is particularly well-suited for capturing structural opportunities in a rapidly changing market environment, outperforming traditional passive strategies [63].

Group 1 - The overall holding value of northbound funds reached 2.24 trillion RMB as of March 31, 2025, an increase of approximately 25.7 billion RMB compared to the end of Q4 2024, accounting for about 5.5% of the free float market value of A-shares [1][8][11] - Long-term allocation funds from foreign banks held 1.71 trillion RMB, increasing by about 10.8 billion RMB, representing 4.2% of the free float market value, while short-term trading funds from foreign brokerages held 0.38 trillion RMB, increasing by approximately 11.2 billion RMB, accounting for 0.93% [1][8][11] Group 2 - Northbound funds showed a significant increase in allocation to momentum, liquidity, and growth styles in Q1, reversing the previous quarter's reduction in these areas [2][17][22] - The overall style preferences of northbound funds included overweight positions in market capitalization, momentum, volatility, profitability, growth, and leverage, while underweight positions were noted in beta, BP, and liquidity [2][20][25] Group 3 - The highest holding value proportion of northbound funds was in the consumer sector at 6.9%, followed by financials at 6.0%, with a slight increase in the cyclical sector [3][28][32] - Northbound funds were overweight in consumer and financial sectors compared to the overall A-share market, while they were underweight in stability, technology, and cyclical sectors [3][38][42] Group 4 - The top five industries for northbound funds in terms of holding proportion changes were automotive, retail, consumer services, machinery, and electronics, while the bottom five included utilities, financials, telecommunications, real estate, and construction [3][42][45] - Northbound funds were overweight in industries such as power equipment and new energy, food and beverage, home appliances, banking, and automotive, while underweight in computer, basic chemicals, machinery, defense, and electronics [3][51][52] Group 5 - In terms of index allocation, northbound funds showed a decrease in holding proportions for the Shanghai 50 (-0.5%), CSI 300 (-0.3%), and CSI 500 (-0.2%), while there was a slight increase for the CSI 1000 (+0.1%) [4][58][62] - Northbound funds were overweight in the Shanghai 50 and CSI 300 compared to the overall A-share market, while underweight in the CSI 500 and CSI 1000 [4][67]